Sugarcane is a vital commercial crop in India, playing a crucial role in the agricultural economy and supporting the sugar industry. However, its productivity is significantly threatened by various diseases caused by fungi, bacteria, viruses, and pests. Major diseases such as red rot, leaf scald, smut, and rust can spread rapidly if not identified at an early stage, resulting in substantial yield reduction and economic losses for farmers. Conventional methods of disease detection, which rely on manual
inspection, are time-consuming, labor- intensive, and inefficient for large-scale farming environments
This study presents an automated system for detecting sugarcane diseases using machine learning and image processing techniques.
The proposed system employs remote sensing data acquired through drones or high- resolution imaging devices to enable continuous monitoring of agricultural fields.
The methodology is divided into two primary stages. In the first stage, a dataset comprising images of healthy and diseased sugarcane leaves is collected and preprocessed. Preprocessing steps include image resizing, noise reduction, grayscale transformation, and normalization to improve image quality and consistency. Feature extraction is carried out using edge detection methods, particularly the Canny Edge Detection algorithm, along with histogram-based analysis to capture variations in color and texture associated with disease symptoms.
In the second stage, a machine learning model is trained to classify the leaf images into healthy or diseased categories. The model evaluates features such as leaf patterns,

discoloration, lesion characteristics, and irregular textures to detect infections at an early stage. Early identification of diseases, especially at the outer leaf layer, enables timely intervention and helps prevent large- scale crop damage.
The proposed approach offers a cost- effective, efficient, and scalable solution for modern agriculture. It facilitates real-time crop monitoring, reduces reliance on manual inspection, minimizes excessive pesticide usage, and enhances overall crop productivity. By integrating remote sensing with machine learning, this system contributes to sustainable agriculture and promotes precision farming practices.

Taxation is a critical element of the economic fabric of a country, but knowledge of tax laws among young people, especially students, is scant. The research explores the awareness of taxation and tax laws among students in India, particularly in Delhi. The study explores the disconnect between conceptual knowledge about taxes and awareness of tax laws and procedural aspects. The research adopts an empirical methodology, drawing on primary data gathered through a questionnaire from 50 student respondents from varied academic backgrounds through convenience sampling.
The results show that although most students have a fundamental understanding of the concepts of taxation, including awareness of basic taxes and their importance, their knowledge of specific tax laws such as the Income Tax Act, Goods and Services Tax (GST) law and taxation procedures such as filing Income Tax Returns (ITR) is relatively weak. Knowledge of practical aspects such as tax compliance, deductions and statutory provisions is also found to be moderate to low. The study also suggests a high level of agreement among respondents about the importance of including tax education in school and college programs, and increasing awareness through institutional and government programs.
The research draws the conclusion that while taxation is gaining prominence in our lives, the current educational and awareness programs are not adequate. It highlights the need for tax education and training and awareness programs to develop a holistic understanding and legal knowledge of taxation among students.

This paper presents LearnVeda, an innovative e-library platform designed to revolutionize digital learning and knowledge management in educational institutions. The platform integrates modern web technologies to provide seamless access to digital resources, personalized learning experiences, and efficient library management systems. LearnVeda addresses the challenges of traditional library systems by offering features such as intelligent search algorithms, user recommendation systems, digital resource management, and analytics-driven insights. The system architecture employs a scalable three-tier design pattern, ensuring robust performance and security. Implementation results demonstrate significant improvements in resource accessibility, user engagement, and administrative efficiency. This research contributes to the advancement of digital education infrastructure and provides a framework for future e-library implementations.

Background and Purpose: Standardized skill assessment tools are fundamental to talent identification and evidence-based coaching in field hockey. Despite the growing participation of school-level female hockey players across Tamil Nadu, no validated, age-stratified skill assessment test exists for this population. This study aimed to construct and standardize a Hockey Skill Assessment Test (HSAT) encompassing five fundamental hockey skills for school-level female players in three age groups: Under-14, Under-17, and Under-19.
Methods: A purposive sample of 1,500 school-level female hockey players from Tamil Nadu (500 per age group) was recruited. Five skills dribbling, push pass, flick, shooting, and dodging were integrated into a continuous time measured circuit. Validity was established via the subjective rating method; reliability and objectivity were determined through test-retest procedures. Percentile norms and T-score qualitative categories were developed using descriptive statistics.
Results: Validity coefficients were 0.85 (Under-14), 0.87 (Under-17), and 0.88 (Under-19); reliability coefficients were 0.90, 0.92, and 0.94; and objectivity coefficients were 0.89, 0.91, and 0.93 (all p

The construction sector consumes vast natural resources and generates significant demolition waste, while cement production contributes heavily to CO2 emissions. This study explores sustainable concrete by partially replacing coarse aggregate with recycled concrete aggregate (RCA) and cement with Ground Granulated Blast Furnace Slag (GGBFS). Experimental investigation assesses workability, compressive strength, and durability. By conducting the SEM (Scanning Electron Microscopy) tests provide high-resolution images and chemical composition data to characterize a material’s surface morphology, topography, and elemental makeup, which is crucial for quality control, failure analysis, and R&D across materials science, nanotechnology, and other fields.

With the rapid advancement of computer vision and deep learning, automated traffic monitoring has gained significant attention for improving road safety and rule enforcement. This paper presents VisionGate X, an intelligent system that integrates object detection, helmet recognition, license plate reading, and natural-language caption generation into a unified framework. The system employs the YOLOv8 model for real-time vehicle and rider detection, heuristic-based helmet identification, and EasyOCR for automatic number plate recognition (ANPR). Furthermore, a dynamic caption generation module produces descriptive text summarizing detected events, such as “A red motorcycle with two riders without helmets, vehicle MH12AB1234.” All outputs, including detection logs and violations, are stored in a SQLite database and visualized through a Streamlit-based web interface. The results demonstrate that VisionGate X efficiently detects motorcycles, identifies helmet compliance, and recognizes license plates with high accuracy in real-time environments. The system’s modular design, lightweight deployment, and strong visual analytics capabilities make it a promising solution for smart surveillance and traffic safety automation.

The accelerated growth of e-commerce has intensified the need for dependable logistical verification systems capable of addressing the increasing financial impact of return fraud. Although conventional interactive video solutions have proven valuable in product marketing and sales contexts, they are not designed to support the operational speed and scale demanded by modern logistics networks. Their dependence on manual configuration and offline video processing limits their practicality in high-volume environments.

To address these shortcomings, this study introduces the Real-Time Automated Packaging Verification System (RT-APVS). The proposed framework employs barcode-triggered mechanisms to embed relevant metadata directly into live video streams at the moment of packaging. By eliminating the need for post-production editing or manual record compilation, RT-APVS streamlines the generation of verifiable digital evidence and strengthens end-to-end traceability across the supply chain. Experimental evaluation indicates that the system reduces dispute resolution time by approximately 90 percent when compared with traditional manual logging approaches.

This research sparked paper offers a conceptual model on how our recent advances in neuromarketing techniques, informed by reviewing the most state-of-the-art theoretical, methodological, and applied literature from marketing, consumer neuroscience, and digital media studies, can shape consumer decision-making in the era of digital advertising. As advertising environments move from over-information, short attention spans, algorithmic targeting and affect-laden visual formats, traditional self-report methods fall short of providing a comprehensive explanation of how consumers allocate attention to, process, and respond to advertising stimuli. In this context, neuromarketing can provide an insightful framework for understanding seemingly subconscious and pre-conscious consumer responses using techniques such as electroencephalography (EEG), eye-tracking, functional magnetic resonance imaging (fMRI), skin conductance, facial coding, and related biometric metrics in the study of attention, emotion, memory, preference formation, and purchase intention in advertising settings. Neuromarketing literature has grown substantially post mid-2010s and is being increasingly applied to digital contexts such as social media advertisement, video advertisement, e-commerce interface, and marketing systems enhanced with AI among others. The primary aim of this paper is to create a conceptual framework regarding the mechanisms in which neuromarketing methods influence the consumer's decision-making process in digital advertising. This aspect of the paper allows the authors to argue that the effectiveness of digital advertisements relies not just on conscious evaluation, but on the unconscious nature of neural and emotional processes such as attention capture, affective engagement, cognitive load, recall, trust and behavioral intention. It additionally suggests that neuromarketing tools allow us to elaborate on why some digital ads do better than others in terms of how they impact viewers in relation to visual salience, message framing, personalization, platform design, and advertising repetition. The paper is conceptual in nature, it is desk-based and it synthesizes relevant literature into a framework which connects neuromarketing inputs, psychological processes and consumer decision outcomes. The results suggest that neuromarketing can extend and bridge digital advertising theory and practice by illuminating our understanding of nonconscious drivers of consumer choice, but they also evoke ethical, privacy, manipulation, and commercial utilization issues related to biometric and neurophysiological data. This study advances marketing management literature, by (1) a structured conceptual model for future empirical assessment, (2) defining neuromarketing opportunities and limits in current digital advertising planning.

It explores the opportunities and risks that the tokenisation of money and securities pose to the future financial system. Tokenisation is the expression of claims on money or financial assets on programmable digital ledgers to allow for automatic execution, transfer, settlement, and record-keeping. Recent policy and research work has highlighted factors that limit market efficiency, beyond just post-trade processes, and provides promising benefits that tokenisation offers to alleviate these. This includes through more efficient settlement, transparency, programmability, and broader access to markets. The literature also notes many serious risks such as market segmentation into incompatible platforms, cyber and operational risk, legal uncertainty over ownership and finality, liquidity risk, governance weaknesses, and potential financial stability risk, if tokenised systems scale without robust standards and interoperable settlement arrangements. Its overall goal is to identify whether tokenisation can achieve tangible enhancements in the efficiency and resilience of financial markets without compromising monetary trust, market integrity, and systemic stability. This work is qualitative and analytical in approach by way of reviewing institutional reports, policy documents, and emergent market evidence from the lens of international institutions: Bank for International Settlements, International Monetary Fund, Financial Stability Board, and European Central Bank. The research compares tokenised money and tokenised securities over core dimensions setting out the case for why it is useful to distinguish between these two categories: settlement efficiency, transparency, interoperability, legal design, liquidity management, financial stability and regulatory readiness. The research anticipates that there will be substantive tokenization opportunities if it is based on reputable settlement assets, robust governance, cross-jurisdictional legal clarity and interoperable infrastructure. Yet where platforms are still walled off, where settlement assets are poorly anchored, or where regulatory frameworks fall behind innovation, it will at best only be partly experienced. This paper makes an original contribution to the current finance literature by conducting a systematic study of tokenization as a technological innovation and a systemic policy challenge for: 1) central banks; 2) the regulators; 3) financial market infrastructures; 4) commercial banks; and 5) institutional investors.

The rapid integration of artificial intelligence (AI) into higher education systems presents profound opportunities and challenges from a public economics perspective. This study aims to examine the implications of AI-driven transformation for efficiency and equity across public universities and private universities in North Sulawesi, Indonesia. Employing an interpretive qualitative research design, data were collected through in-depth semi-structured interviews with 32 key informants, including university administrators, faculty members, students, and policymakers across six higher education institutions. Data were analyzed using thematic analysis and constant comparative methods informed by public economics frameworks, including market failure theory, public goods theory, and distributive justice principles. The findings reveal a persistent efficiency–equity tension: AI adoption enhances operational productivity and instructional quality in resource-rich PTN institutions, while simultaneously exacerbating the digital divide for PTS institutions serving economically disadvantaged student populations. Three key mechanisms are identified: (1) asymmetric resource allocation favoring state-funded institutions, (2) AI-induced credential stratification that reshapes labor market signaling, and (3) governance gaps in regional AI policy frameworks. This study contributes a context-specific analytical model the AI-Education Public Economics (AI-EPE) Framework which integrates efficiency and equity dimensions in higher education transformation. The findings offer important theoretical insights and practical implications for education governance, public funding design, and equitable AI policy development in developing-country contexts.

A significant trend in the pharmaceutical industry today is digital transformation. Digital technologies are reshaping both external and internal aspects of pharmaceutical business operations. In the wake of the COVID-19 pandemic, pharmaceutical companies have been compelled to shift their focus toward digital marketing and more personalized customer engagement strategies. The primary objective of omnichannel marketing is to deliver a seamless, integrated, and customized experience to customers, irrespective of the platform or device utilized.
This study aims to contribute to the ongoing discourse on understanding omnichannel marketing within the pharmaceutical sector in a coherent manner. The key objectives of this research are:
1. To examine the factors driving the adoption of omnichannel marketing in the pharmaceutical industry.
2. To explore the challenges and barriers associated with the implementation of omnichannel marketing in the pharmaceutical industry.
In the initial phase of the study, the key drivers and obstacles were identified through semi- structured telephonic interviews with pharmaceutical professionals. In the subsequent phase, qualitative analysis was carried out, and practical implications along with future research propositions were discussed.

Operations Research (OR) plays a critical role in optimizing complex engineering systems by applying mathematical modeling, analytical techniques, and computational tools. This paper presents an extended study on the applications of OR in engineering, integrating classical techniques with modern advancements such as artificial intelligence and data analytics. It explores optimization models, decision-making frameworks, and real-world engineering applications including manufacturing, transportation, healthcare, and supply chain systems. A case study on hospital scheduling using integer programming is also discussed. The results highlight that OR significantly improves efficiency, reduces cost, and enhances decision quality. Future research directions include hybrid models combining OR with machine learning.

Bioterrorism refers to the deliberate use of biological agents to cause disease, disability, or death in humans, animals, or plants, with the intent to instill fear, disrupt societies, or achieve political objectives. These agents include a wide range of microorganisms such as bacteria, viruses, and fungi, as well as biologically derived toxins. Among the various modes of dissemination, aerosol release is the most common and effective method, as it enables widespread exposure through inhalation. Additionally, contamination of food and water supplies can result in large-scale outbreaks. The use of mechanical devices or advanced delivery systems further enhances covert dissemination, making bioterrorism a major public health and global security concern (1).

Bekasam is a traditional fermented fish product that relies on carbohydrates as an energy source for microorganisms during fermentation. Palm sugar is a conventional carbohydrate additive used in bekasam production, capable of generating a distinctive aroma; however, its relatively high market price necessitates the exploration of alternative carbohydrate sources. Sucrose represents a viable substitute, serving as an energy source for lactic acid bacteria (LAB) during fermentation. The present study aimed to determine the physicochemical characteristics and hedonic properties of tilapia bekasam prepared with different sucrose concentrations, and to identify the optimal formulation. The manufacturing procedure commenced with salting of cleaned and transversely cut tilapia fillets at a salt concentration of 10% for 15 minutes, followed by the addition of 40% cooked rice and sucrose at varying concentrations into glass jars. Fermentation was conducted at room temperature for 7 days. A completely randomized design (CRD) was employed, comprising treatments of 5% palm sugar (control), 1% sucrose, 3% sucrose, and 5% sucrose. The results indicated that increasing sucrose concentration significantly affected water activity, moisture content, total plate count (TPC), pH, and hedonic scores. Higher sucrose concentrations elevated TPC values and hedonic scores for appearance and taste, while reducing water activity, moisture content, pH, and total volatile base nitrogen (TVBN). The optimal treatment was identified as bekasam with 5% sucrose, exhibiting the following values: appearance 6.06 ± 0.90, aroma 6.63 ± 0.73, taste 6.31 ± 0.68, texture 6.66 ± 0.80, water activity 0.80 ± 0.001, moisture content 60.02 ± 0.29%, TPC 9.44 ± 0.03 cfu/g, pH 5.85 ± 0.03, and TVBN 19.33 ± 0.68 mgN/100g.

Vulvovaginitis is a common gynecological condition characterized by inflammation of the vulva and vagina, often presenting with itching, burning, discharge, soreness, and discomfort.Vulvovaginitis is a frequent reason for women to see a health care provider and has been linked to adverse Reproductive and psychosocial consequences. Clinical approaches and professional guidelines should be updated and improve the diagnosis and management of acute and recurrent vulvovaginitis.While commonly prescribed homeopathic remedies are frequently effective, certain chronic, recurrent, or atypical cases require deeper individualization and the use of lesser-known medicines from the Materia Medica. This article focuses on the role of rarely indicated homeopathic remedies in the management of vulvovaginitis, emphasizing their characteristic symptoms, clinical indications, and therapeutic value. By expanding beyond commonly used remedies, homeopathic physicians can enhance precision in prescription and achieve better outcomes in resistant cases. The article highlights the importance of constitutional assessment, symptom totality, and careful remedy selection in accordance with classical homeopathic principles.

In emergency situations, immediate access to medical support is crucial for saving lives. The Lifeline – Emergency AI Doctor Assistant is an intelligent healthcare system that uses Artificial Intelligence (AI) to provide preliminary diagnosis, first-aid recommendations, and hospital guidance. The system integrates AI-based symptom triage, voice and image processing, medical ID storage, and real-time hospital routing. It assists users in emergencies by analyzing symptoms and suggesting optimal actions even before professional medical help arrives. This paper presents the architecture, methodology, and evaluation of the Lifeline system, highlighting its ability to provide fast, accessible, and reliable emergency assistance using AI and mobile technology.

The rapid evolution of Artificial Intelligence (AI) and smart mobility technologies has revolutionized the way drivers access navigation and fuel services. Traditional systems provide static information and fail to consider dynamic parameters such as real-time fuel availability, pricing, and traffic conditions. The AI-Driven Fuel Locator and Detector proposes an intelligent system that integrates machine learning and GPS-based location services to recommend optimal fuel or charging stations. This survey paper explores the existing research, methodologies, architectures, and applications of AI-based fuel detection systems, comparing them to traditional methods. The objective is to highlight the efficiency, scalability, and adaptability of AI-enabled smart transport systems in modern vehicular networks.

The rapid expansion of digital recruitment platforms has significantly increased the number of resumes received for each job opening, making manual screening inefficient and inconsistent. traditional applicant tracking systems (ats) rely heavily on keyword-based filtering, which often fails to capture the contextual relevance of skills, experience, and overall resume quality. this paper presents an ai-powered resume analyzer and applicant tracking system designed to automate resume evaluation using artificial intelligence and natural language processing techniques within a fully client-side environment.

Real estate and interior design industries increas- ingly demand smarter, faster, and more immersive design solu- tions. Traditional 2D and static 3D design methods often fail to communicate spatial depth and functional flow effectively. This paper presents a Smart VR-Integrated 3D Visualization System that combines Virtual Reality (VR) with Machine Learn- ing (ML) to enhance user experience and decision-making in spatial design. The system enables users to generate 3D layouts, visualize them in immersive VR environments, and receive ML- based layout recommendations that consider spatial harmony, ergonomics, and aesthetic balance. The proposed system reduces design time, improves visualization accuracy, and enhances user satisfaction compared to traditional tools. Experimental results show smooth rendering at 55–60 FPS and layout recommendation accuracy exceeding 90%, demonstrating the effectiveness of VR– ML integration for real estate and interior applications.

This study investigates the impact of hybrid learning models on library services within the Nigerian tertiary education system. Employing a mixed-methods approach, data was collected from 300 respondents comprising students, library staff, and academic staff across 10 institutions in South-West Nigeria through surveys and interviews. The findings reveal a significant "adaptation gap," where a rapid user shift towards digital resource consumption (e.g., online exam repositories, mean =4.4; e-books, mean =3.8) is met with institutional constraints. Key challenges identified include inadequate funding for digital resources (mean =4.7), unreliable power and internet connectivity (mean=4.6), and insufficient staff training (mean =4.0). Consequently, student perceptions of service adequacy were neutral to negative (overall mean=2.9), with particular dissatisfaction regarding the sufficiency of online resources and the adaptation of physical spaces. The study concludes that libraries are struggling to transition from traditional, physical-centric models to dynamic, service-oriented hubs for hybrid learning. It recommends strategic interventions, including dedicated funding for e-resources, robust infrastructure investment, continuous staff development, and a proactive marketing of existing digital services to bridge the identified gap and enhance library relevance in the evolving educational landscape.

The evolution of modern medicine has transformed death from an inevitable biological event into a prolonged, technologically mediated process. In India, this transformation has created a profound constitutional dilemma: whether the right to life under Article 21 mandates the indefinite preservation of biological existence, even when such preservation no longer serves any meaningful human purpose. This paper examines the constitutional validity of prolonging life through medical intervention in circumstances where treatment has lost therapeutic value and instead perpetuates suffering, dependency, and loss of dignity.
Drawing upon doctrinal legal analysis, ethical reasoning, and judicial developments, the study interrogates the traditional sanctity of life doctrine and its continued relevance in contemporary end-of-life care. It argues that the Constitution does not compel the mechanical extension of life through invasive technologies when such extension conflicts with the values of dignity, autonomy, and proportionality. Through an engagement with landmark decisions such as Gian Kaur v. State of Punjab, Aruna Ramachandra Shanbaug v. Union of India, and Common Cause v. Union of India, the paper demonstrates a gradual judicial shift from an absolutist protection of life to a more nuanced recognition of dignified dying.
Ultimately, the study concludes that prolonging life is not inherently constitutional. Its legitimacy depends on whether it retains a therapeutic purpose, respects the will and dignity of the individual, and operates within a framework of lawful and compassionate
decision-making. The Constitution, properly understood, protects not mere existence, but meaningful human life.

India ranks among the countries with the highest global burden of diabetes. According to a Lancet study, the total number of diabetes cases in India is 212 million (Zhou et al., 2024). This accounts for 26% of global diabetes cases, the greatest proportion provided by any country (Lancet, 2024). Persons with diabetes face various physical and mental health concerns. Ill health reduces productivity of the affected individual and drain of financial resources. Individual’s wellbeing and overall life is impacted. This study seeks to explore the health-seeking behaviour of individuals living with diabetes mellitus. A descriptive study was conducted on 120 respondents, which included men and women within the age group of 25-55 years, living in two urban areas of West Bengal, Kolkata and Purba Bardhaman. It was revealed that most of the respondents were suffering from type 2 diabetes; more women than men faced multiple physical and psychological comorbidities, diabetes distress and social stigma. The prevalence of physical and psychological comorbidities and diabetes distress had adverse effect on interpersonal relationships of women respondents. Respondents adhered to the advice of their doctors but doing regular exercises or yoga was a challenge for them. Seeking support for psychological comorbidities was limited among a few. Diabetes is a lifestyle disease, awareness and routine alteration can help prevent the occurrence of the disease, reduce comorbidities, and improve the wellbeing of patients. The researchers suggest interventions for prevention of diabetes and care and treatment of diabetes patients to ensure positive health for all.

This study examined the effectiveness of the Jigsaw teaching strategy on students’ interest and academic performance in Social Studies in comparison with the conventional lecture method. The findings revealed that students taught using the Jigsaw strategy demonstrated significantly higher levels of interest and better academic performance than those taught using the lecture method. The results further established that the teaching strategy accounted for a substantial proportion of the variance in students’ learning outcomes, thereby confirming the superiority of learner-centred and cooperative instructional approaches over traditional teacher-centred methods. Based on these findings, several recommendations were made. Social Studies teachers are encouraged to adopt the Jigsaw teaching strategy as a regular classroom practice to enhance students’ engagement and achievement. Educational authorities and school administrators should organise continuous professional development programmes, such as workshops and seminars, to equip teachers with the skills required for effective implementation of cooperative learning strategies. Curriculum planners, including the Nigerian Educational Research and Development Council, are advised to integrate and emphasise learner-centred approaches like Jigsaw in the Social Studies curriculum. In addition, schools should provide adequate instructional materials to support interactive and group-based learning.Furthermore, teachers should foster a collaborative classroom environment that promotes peer interaction and active participation, while reducing overdependence on the lecture method. There is also a need for continuous monitoring and evaluation of teaching practices to ensure effective implementation of innovative strategies. Finally, further research is recommended across different subjects, educational levels, and locations to validate and generalise the effectiveness of the Jigsaw teaching strategy.In conclusion, the adoption of the Jigsaw teaching strategy has significant implications for improving the quality of teaching and learning in Social Studies and enhancing students’ overall academic outcomes.

Background: The Coimbatore–Erode–Salem corridor, one of South India's most economically dynamic urban agglomerations, has undergone rapid spatial expansion over the past two decades, converting large tracts of its highly productive Vertisol and alluvial agricultural land to residential, industrial, and infrastructure uses. This transformation threatens regional food security, soil ecosystem services, and the livelihoods of smallholder farm communities. Objective: This systematic review synthesises peer-reviewed evidence published between 2017 and 2026 on the spatial extent, biophysical consequences, and socio-economic impacts of urban expansion-driven agricultural land loss in this corridor, with particular emphasis on geospatial analytical methods, soil quality trajectories, and policy responses. Methods: Following PRISMA 2020 guidelines, 114 peer-reviewed studies were identified from Scopus, Web of Science, ScienceDirect, and Google Scholar after screening 3,214 initial records. Studies were appraised using the GRADE framework and categorised by analytical type. Results: Evidence consistently documents a corridor-wide agricultural land loss of approximately 118,000 ha between 2000 and 2025, representing a 28.3% reduction in cropland. High-value Vertisol tracts and peri-urban paddy lands bear the greatest conversion pressure. Documented consequences include significant soil organic carbon depletion (−21 to −41%), groundwater depth increases of 8–13 m, urban heat island intensification (up to +5.1°C by 2025), and cumulative displacement of over 134,000 farm households. Conclusions: Existing evidence overwhelmingly establishes the urgency of spatially differentiated land-use governance reform, inclusive of agricultural land classification, greenbelt zoning, and digital monitoring platforms, to arrest the irreversible conversion of the corridor's most productive agricultural soils.

This research paper presents the design and implementation of a real-time voice interaction system that enables natural conversational communication between users and an artificial intelligence model. The system is developed using OpenAI's Realtime API combined with WebRTC technology to enable low-latency audio communication.
The proposed architecture integrates a React TypeScript frontend with a FastAPI Python backend, creating a scalable and efficient framework for real-time voice-based AI interactions. The application captures user speech through the browser microphone, transmits audio streams to the AI model for processing, and returns AI-generated responses in real time.
The system emphasizes low latency, seamless communication, and an intuitive user interface, allowing users to interact with the AI through natural speech. By leveraging modern web technologies and peer-to-peer communication protocols, the system significantly reduces response delays and improves conversational efficiency. This research demonstrates the potential of real-time AI voice systems in modern human-computer interaction.

Despite decades of research, the development of an effective HIV vaccine remains a significant challenge. Recent findings from three large vaccine efficacy trials have identified
antibodies against the V1V2 domain of the HIV envelope glycoprotein as a potential correlate of reduced infection risk, offering a promising avenue for improving vaccine efficacy. Vaccine-elicited anti-V1V2 antibodies do not mediate potent virus-neutralizing activities, but they mediate Fc-dependent effector functions.
The use of Antiretroviral drugs in treating HIV/ AIDS patients has enormously increased their life spans with serious disadvantages. The virus infection still remains a public health problem worldwide with no cure and vaccine for the viral agent until now. The use of nanoparticles (NPs) for the treatment and prevention of HIV/AIDS is an emerging technology of the 21st century. NPs are solid and colloid particles with 10 nm to <1000 nm size range; although, less than 200 nm is the recommended size for nanomedical usage. There are NPs with therapeutic capabilities such as liposomes, micelles, dendrimers and nanocapsules. The particle enters the body mainly via oral intake, direct injection and inhalation. It has been proven to have potentials of advancing the prevention and treatment of the viral agent. Certain NPs have been shown to have selftherapeutic activity for the virus in vitro. Strategies that are novel are emerging which can be used to improve nanotechnology, such as genetic treatment and immunotherapy. In this review, nanoparticles, the types and its characteristics in drug delivery were discussed. The light was furthermore shed on its implications in the prevention and treatment of HIV/AIDS.

Early – Onset Alzheimer’s Disease (EOAD) is a rare but serios neurodegenerative disorder that affects individuals typically below 65 years of age unlike late – onset Alzheimer’s EOAD
often progresses more rapidly and has a stronger genetic component it is characterized by progressive cognitive decline , memory impairment, behavioral changes and loss of functional independence the pathophysiology involves accumulation of amyloid-beta plaques tau protein tangles neuroinflammation and neuronal loss Early diagnosis is challenging due to atypical presentation and misdiagnosis as psychiatric disorders- modifying therapies such as monoclonal antibodies have improved early detection and management this review discusses the causes clinical manifestations pathophysiology diagnosis and current management strategies of EOAD along with recent therapeutic advancements from 2020-2025.

Insulin resistance, as a common pathological process of many metabolic diseases, including diabetes and obesity, has attracted much attention due to its relevant influencing factors. To date, studies have mainly focused on the shared mechanisms between mitochondrial stress and insulin resistance, and they are now being pursued as a very attractive therapeutic target due to their extensive involvement in many human clinical settings. In view of the complex pathogenesis of diabetes, natural drugs have become new players in diabetes prevention and treatment because of their wide targets and few side effects. In particular, plant phenolics have received attention because of their close relationship with oxidative stress. In this review, we briefly review the mechanisms by which mitochondrial stress leads to insulin resistance. Moreover, we list some cytokines and genes that have recently been found to play roles in mitochondrial stress and insulin resistance. Furthermore, we describe several natural drugs that are currently widely used and give a brief overview of their therapeutic mechanisms. Finally, we suggest possible ideas for future research related to the unique role that natural drugs play in the treatment of insulin resistance through the above targets.
Nonsteroidal anti-inflammatory drugs (NSAIDs) are a group of heterogeneous drugs largely known for their anti-inflammatory, antipyretic, and analgesic effects, which are met by means of the inhibition of the cyclooxygenase (COX) enzymes. Even when their use in patients with diabetes mellitus is limited due to relevant adverse events, some pharmacological and metabolic effects of NSAIDs have been further studied to be potentially beneficial in the prevention and/or treatment of diabetic subjects. Effects on endogenous glucose production, peripheral insulin resistance, pancreatic islet, and systemic inflammation and the insulin clearance have been reported. In this article, we overview the scientific literature of the last 5 years regarding the potential effects of NSAID treatment on diabetes prevention/treatment.

Bakery operations operate within a highly time-sensitive environment influenced by perishable inventory, fluc-tuating customer demand, and the simultaneous handling of walk-in orders, scheduled pickups, customized production, and delivery processes. Despite the growing adoption of digital solutions, many small and medium-scale bakeries continue to rely on fragmented systems for order management, kitchen coordination, customer interaction, and administrative control. This lack of integration often results in delayed synchronization, limited visibility of order states, workflow inefficiencies, and reduced traceability of operational activities.
This paper proposes an event-driven bakery operations platform that integrates order processing, kitchen execution, delivery coordination, loyalty management, and policy-driven administrative control within a unified architecture. The system is developed using the MERN stack and employs Socket.io to enable real-time communication and dynamic workflow updates. The proposed design separates user interaction, transaction processing, and event handling into distinct layers to enhance scalability, maintainability, and system responsiveness.
Furthermore, the platform incorporates role-based access control, audit-enabled governance, and a structured lifecycle model to enforce valid order transitions and improve operational clarity. Performance evaluation through simulated workflows demonstrates improved synchronization, reduced latency, enhanced visibility, and reliable event propagation. The results indicate that an event-driven approach provides an effective and scalable solution for modern bakery operations by replacing disconnected systems with a cohesive real-time coordination framework.

In today’s fast-changing business world, companies are no longer focused only on making profits. They are also expected to take responsibility for protecting the environment, supporting social well-being, and ensuring long-term economic growth. This broader approach to business is known as sustainability. It focuses on balancing financial success with environmental protection and social responsibility so that future generations are not negatively affected.
Over the past few years, the concept of sustainability has become very important due to rising global challenges such as climate change, pollution, resource depletion, and social inequality. As a result, businesses are under increasing pressure from governments, customers, and society to adopt more responsible and ethical practices.
At the same time, digital marketing has emerged as one of the most powerful and widely used tools for business growth and communication. Digital marketing includes various online platforms and techniques such as social media (Facebook, Instagram, LinkedIn), company websites, mobile applications, email campaigns, search engine optimization (SEO), and online advertising. These tools allow businesses to connect with a large audience quickly, efficiently, and at a lower cost compared to traditional marketing methods.
In the modern era, businesses are increasingly integrating digital marketing strategies with their sustainable business goals. This combination helps companies not only promote their products and services but also communicate their environmental and social efforts to the public. Through digital platforms, organizations can share information about eco-friendly products, corporate social responsibility (CSR) activities, and sustainable initiatives in a transparent and engaging way.

This paper analyzes the environmental advantages of clean energy development in Delta State, Nigeria, to see the household energy consumption trends, their awareness of renewable energy technologies as well as the possibility of using clean energy technologies. The study was a mixed method which was a combination of quantitative and qualitative data gathered using 380 households in six chosen Local Government Areas within the state. Information on energy use, awareness of renewable technologies, and perceptions of environmental benefits of adopting clean energy were collected using structured questionnaires, interviews and focus group discussions. Descriptive statistics and correlation analysis were used to analyze the quantitative data. The results indicate that most households use the national electricity grid and generators that use fuel as their main sources of power because the power supply is not reliable, and the use of renewable energy sources like the use of solar systems is also low. The research also discovered that the knowledge about clean energy technologies is moderately high, especially with regards to better cookstoves and solar home systems, which exhibit potential to be adopted promisingly since they are perceived to have a positive environmental and economic impact. But, high initial investment cost and insufficient financial aid are still significant obstacles to mass adoption. It is concluded that increased availability of affordable clean energy technologies, and enhanced awareness of the population can be a substantial boost to environmental sustainability, carbon emission reduction, and sustainable energy development in Delta State.

This study assessed the role of digital finance in addressing poverty and financial exclusion among vulnerable groups in Abuja, Nigeria. Using a mixed-method approach, data were collected from 385 households through structured questionnaires and semi-structured interviews. Descriptive statistics, logistic regression, and odds ratio analyses were employed to examine the determinants of digital finance adoption and the barriers affecting its utilization. The findings reveal that household digital literacy, access to internet and mobile networks, education, and trust in digital finance institutions significantly increase the likelihood of adopting digital financial services. Conversely, perceived high costs and regulatory concerns were found to hinder adoption, particularly among vulnerable households. The study also demonstrates that digital finance adoption positively influences access to financial services and employment opportunities, contributing to poverty reduction and economic empowerment. Despite these benefits, disparities in infrastructure, digital literacy, and socio-economic vulnerability limit the full potential of digital financial inclusion. The study concludes that digital finance is a critical tool for fostering inclusive economic development in urban Nigerian contexts, provided that complementary interventions, such as digital literacy programs, affordable service offerings, and trust-building initiatives, are implemented. These findings have important implications for policymakers, financial institutions, and development stakeholders seeking to enhance financial inclusion and poverty alleviation through digital innovations.

The insurance sector in India has been undergoing a significant transformation due to the rapid adoption of Artificial Intelligence (AI) and Insurtech innovations. Traditionally, insurance distribution was largely dependent on physical channels such as insurance agents, brokers, branch offices, and face-to-face customer interactions. However, changing customer expectations, increasing internet penetration, smartphone usage, and the need for faster and more efficient services have encouraged insurance companies to adopt digital and technology-driven distribution methods. In this context, AI and Insurtech have emerged as major forces reshaping the way insurance products are marketed, sold, serviced, and renewed.
The study adopts a descriptive research design based on primary data collected from policyholders, insurance agents, and insurance employees to understand how AI-driven technologies are influencing the insurance distribution process. The findings reveal that AI and Insurtech have increased convenience, speed, and accessibility in policy purchase and servicing.
However, challenges such as low digital literacy, trust issues, data privacy concerns, and fear of job displacement among agents continue to affect adoption. The study concludes that while AI and Insurtech are redefining insurance distribution, a balanced model combining technology with human advisory support is essential for long-term growth and customer trust.

The analysis of solar phenomena such as sunspots and solar flares is critical for understanding space weather and its impact on Earth-based and satellite systems. Traditional methods for detecting these features have largely relied on manual observation and classical image processing techniques, which often struggle with noise, illumination variability, and complex solar structures. This study presents a deep learning-based approach for the segmentation and tracking of solar features, specifically sunspots and solar flares, using advanced computer vision techniques. A Mask R-CNN framework, supported by a ResNet backbone, is employed to perform instance segmentation on solar images obtained from telescope and space-based observations. The dataset undergoes comprehensive preprocessing, including normalization, noise reduction, contrast enhancement, and resizing, to improve feature visibility and model performance. The model is trained using annotated datasets with clearly labeled solar features, leveraging cross-entropy loss for classification and mask loss for precise segmentation.

After-school home tutoring, situated within the broader concept of shadow education, has become a significant global phenomenon, yet remains underexplored in conflict-affected and developing contexts. This study examines its impact on students’ academic performance through teachers’ perspectives at Fatih Sultan Mohamed School in Borama, Somaliland. Using a mixed-methods design, data were collected from 23 teachers via structured questionnaires and analyzed using descriptive statistics and thematic analysis. Findings indicate that home tutoring is widely perceived as enhancing academic performance, subject understanding, student confidence, and classroom participation. Tutored students were reported to achieve higher grades, improved examination outcomes, and stronger problem-solving skills. However, its effectiveness is influenced by factors such as tutor quality, session frequency, parental involvement, student motivation, and resource availability. The study also highlights key challenges, including financial inequality, student dependency, pedagogical misalignment, and learner fatigue. It concludes that while home tutoring can improve academic outcomes, it may also reinforce educational disparities. The study recommends greater regulation, alignment with formal schooling, and strategies to promote equitable access.

Allama Mohammad Iqbal, the renowned poet-philosopher of Islam, presents a profound educational philosophy rooted in Quran and Sunnah, critiquing modern systems for promoting wrong ideals, fragmenting knowledge, and ignoring the soul in favor of the mind. In his key work, The Reconstruction of Religious Thought in Islam, he urges Muslims to rethink faith through modern science without breaking from tradition, Islamize knowledge by blending reason, intuition, and Faqr for holistic selfhood (Khudi), and oppose borrowed Western education&#39;s expansionism, liberal individualism, and secularism that produce superficial individuals lacking moral and national ideals. Iqbal views education as inseparable from culture, essential for preserving traditions and fostering communal perfection as outlined in Rumuz-i-Bekhudi, while praising selective adoption of scientific methods if subservient to Deen, transforming knowledge from poison to a blessing for justice and self-awareness.

The objective of this research was to prepare and characterize neem bark extract from Azadirachta indica. Bark was collected in mid-August, and the methanolic extract was analyzed for physical, chemical, antimicrobial properties, λmax, purity (UV spectroscopy), and structural features using FTIR.

Climate change has emerged as one of the most complex and urgent global challenges, with far-reaching implications for environmental sustainability, economic development, and human rights. India, as a rapidly developing nation with diverse ecological systems and socio-economic disparities, faces the dual challenge of sustaining economic growth while ensuring environmental protection. This article undertakes a comprehensive analysis of climate change governance in India by examining constitutional provisions, policy frameworks, legislative developments, and recent judicial trends. Particular emphasis is placed on the evolving role of the National Green Tribunal (NGT) and the Kerala High Court in shaping climate jurisprudence. The study adopts a doctrinal methodology and critically evaluates the effectiveness of existing legal mechanisms. It argues that despite progressive judicial interventions, India’s climate governance remains fragmented due to the absence of a comprehensive legislative framework. The article concludes with recommendations for strengthening climate governance through legal reform, institutional capacity-building, and enhanced public participation.

The proposed system is a lightweight, sensor-integrated wearable glove designed to translate hand gestures into audible speech in real time. It incorporates flex sensors and inertial measurement units (IMUs) embedded across the fingers and hand to capture fine-grained motion with high precision. Emphasizing portability, low-latency performance, and ergonomic comfort, the glove is optimized for everyday use. Its embedded-machine-learning architecture (TinyML) enables all signal processing and classification to run directly on-device, avoiding the limitations of camera-based systems such as lighting dependency, privacy concerns, and high processing overhead. The glove supports fast calibration and a customizable vocabulary, enabling users with different signing styles to adapt the system to their needs. By combining efficient gesture recognition with on-board speech synthesis, it serves as a practical and accessible assistive communication tool suitable for daily use.

The dual-glove configuration employs a total of ten flex sensors five on each glove and two MPU- 6050 IMUs to capture finger bend angles, wrist movement, and dynamic hand orientation. Sensor data is sampled at 50 Hz and transmitted wirelessly using the ESP-NOW protocol for low-power, low- latency communication. A hybrid 1D CNN–LSTM deep learning model operates on 100-sample sliding windows comprising 16 features (10 flex + 6 IMU axes), achieving gesture classification accuracy between 90–95% for Indian Sign Language (ISL). After training, the model is quantized to INT8 format and deployed on the ESP32-S3 microcontroller using TensorFlow Lite Micro, reducing memory usage to approximately 0.15 MB. Recognized gestures are converted into audible English speech through a Bluetooth speaker via a text-to-speech engine. The system currently supports over 100 ISL words, with scalability to 500+ planned in future iterations, and maintains an end-to-end latency below 100 ms while operating fully offline. With a prototype system aims to deliver affordable, real-world assistance across educational, healthcare, workplace, and public-interaction environments. In addition to technical efficiency, the system is designed with user-centric considerations such as ease of operation, minimal hardware complexity, and long-term reliability. The modular structure allows future upgrades in vocabulary, gesture sets, and model accuracy without redesigning the entire hardware. This combination of scalability, affordability, and robustness makes the glove a promising foundation for next-generation assistive communication devices.

BACKGROUND: Despite mounting evidence that shift work poses health concerns, it is necessary for our round-the-clock culture. Working hours are just as important as the psychosocial office environment. One of the main causes of employment impairment is mental health conditions. OBJECTIVES: To evaluate the socioeconomic standing of transportation personnel. to find out how common self-reported OSA and restless nights are among transportation professionals. to investigate mental health and sleep issues. investigating the effects of erratic work schedules on the mental health of transportation personnel. METHODS AND MATERIALS: Primary data was gathered using quantitative and descriptive methods from 139 transport workers in the Chengalpattu district through stratified sampling. Using structured questionnaires with eleven different designs, 50 items on a five-point Likert scale were used to gather demographic data from transport workers. Both factor and descriptive analysis were applied. CONCLUSION: Both work-related and general mental health were linked to working time differences. Work intensity was associated with burnout symptoms, suggesting that psychosocial recovery from work exposures is the mechanism. Depressed feelings were connected to schedule control and predictability, suggesting work-life imbalance and a lack of recovery.

The study investigated the effect of jigsaw and peer tutoring strategies on students’ retention in social studies in Cross River State, Nigeria. The study employed a quasi-experimental non-randomized pre-test, post-test control group design. Three research questions and hypothesis were formulated. The sample consisted of 308 Junior Secondary School students in two intact classes in two secondary schools in Cross River State, Nigeria. The instrument used for data collection were Social Studies Academic Performance Test (SOSAPT) Social Studies Retention Test (SSRT). The instrument were face validated by three experts. Kuder-Richardson (K-R20) formula was used to test the internal consistency reliability of SSAPT and the result was 0.98 and Cronbach Alpha was used to ascertain the reliability coefficient of SSRT which yielded a reliability coefficient of 0.89. The data were analysed using descriptive statistics and Analysis of Covariance (ANCOVA). The results showed that was a significant difference between the mean retention scores of students taught using Jigsaw teaching strategy and those taught using peer tutoring strategies in Social Studies F (1, 201) = 12.300; p = 0.001 0.05. The study recommended that social Studies teachers should use jigsaw and peer tutoring to enhance students’ retention in the subject and principals and Supervisors of Social Studies teaching and learning encourage the use of Jigsaw and peer tutoring strategies to enhance male and female students’ retention in the subject.

Managing and analyzing student academic data has become increasingly important for institutions striving to improve educational outcomes. This paper presents a Student Performance Enhancement System — a full-stack web application that digitizes the collection, processing, storage, and analysis of student academic records. The system integrates React for a dynamic frontend, FastAPI for a high-performance Python backend, and PostgreSQL as a structured relational database. Student information and subject marks are entered through an intuitive interface, and the backend automatically calculates totals and averages while storing records securely. A machine learning component employing Random Forest Regression evaluates and predicts student performance trends with up to 95% accuracy and a low error rate of 5%. The system eliminates manual record-keeping errors, ensures centralized data access, and supports data-driven decision-making for educators. Testing across functional, validation, and database scenarios confirmed robust performance and reliability. The system is well-suited for academic institutions seeking a scalable, secure, and efficient solution for student performance management.

The global demand for traditionally-rooted medical preparations, including Ayurvedic formulations, has witnessed a sustained upward trend. Abhrak bhasma, an Ayurvedic herbomineral preparation derived through iterative incineration of mica with decoctions of several medicinal plants, has long been employed in the management of conditions such as respiratory disorders, hemorrhagic diseases, urinary ailments, diabetes mellitus, anemia, dermatological complaints, and splenic dysfunctions.12 In addition to these therapeutic applications, it is traditionally recognized for its rejuvenating and anti-infertility properties. However, scientific validation of such preparations in terms of their mechanism of action at the physiological and molecular level remains largely inadequate.3 The present investigation was designed to elucidate the probable chemical transformations that abhrak bhasma undergoes within the gastrointestinal (GI) tract. Spectrophotometric analysis conducted under simulated acidic and alkaline GI conditions demonstrated notable alterations in the absorbance profile, predominantly attributable to iron-containing compounds within the preparation. Infrared spectroscopy confirmed the dominance of metallic constituents with minimal organic or moisture moisture content. Assessment of antimicrobial properties revealed an absence of intrinsic antibacterial activity; however, partial inhibition of yeast cell proliferation was documented. Further mechanistic investigations are warranted to comprehensively characterize the bioactive constituents released during GI transit.

Bioplastic films based on carboxymethyl cellulose (CMC) and gelatin have gained considerable attention as sustainable alternatives to synthetic packaging materials, particularly for food applications. This study aimed to characterize composite films composed of 1.5% CMC and 1.5% gelatin, plasticized with 20% glycerol (relative to total polymer), and incorporated with cinnamon essential oil (CEO) at concentrations of 0% (A), 0.1% (B), 0.3% (C), and 0.5% (D), fabricated using the casting method. Physical, mechanical, and barrier properties were evaluated, including thickness, tensile strength, elongation at break, water vapor transmission rate (WVTR), water solubility, and antimicrobial activity. Results showed that CEO concentration significantly influenced elongation at break and WVTR. Elongation increased from 43.80% (A) to 61.59% (D), while tensile strength decreased from 11.00 MPa (A) to 6.24 MPa (D). WVTR decreased substantially from 47.13 g/m²/24h (A) to 19.63 g/m²/24h (D), indicating improved moisture barrier properties with higher CEO. Film thickness remained consistent across all treatments (~0.15–0.18 mm). Water solubility values ranged between 33.82% and 44.27%. Antimicrobial activity was not detected in any treatment under the test conditions applied. These findings suggest that the incorporation of cinnamon essential oil can effectively modulate the functional properties of CMC-gelatin composite films, with treatment D (0.5% CEO) showing the most promising barrier characteristics for potential food packaging applications.

Ensuring safety using modern systems that mitigate the risk of violence in the workplace, in schools, and online is critical in today's society, as the presence of violence is becoming more ubiquitous. The traditional method of monitoring and surveillance, including the manual recording of events, is inefficient and often riddled with monitoring errors. Therefore, this project presents the automation of violence detection using deep learning methodologies to process and evaluate video inputs for real-time violent activity recognition. The convolutional neural networks (CNNs) and Long Short-Term Memory Networks (LSTM) work collaboratively to capture the spatial and temporal features in video frames in order to effectively classify violent acts such as fighting and assaulting. The model architecture for this project is comprised of video input, frame capture, classification of violent and non-violent acts, and feature learning (spatial and temporal). This model augments, with tuneable parameters, generalization around the core violence detection classification. The model architecture evaluation uses publicly available benchmark datasets. Expected precision, accuracy, and recall thresholds guide the formation of the model, as well as the f1-score. The architecture in this project works to complement current surveillance systems and ensure there is a significant decrease in the human effort that is required to monitor live video feeds. The outcome of this work is to create a safer environment.

The present study has been associated with the evaluation of an Occupational Health and Safety Risk Assessment among Police Employees. This study identified Occupational Health and Safety for police employees. To analyze an Occupational Health and Safety Risk Assessment. To analyze the factorize of the evaluation of an Occupational Health and Safety Risk Assessment. Using quantitative study type with a descriptive research design that surveyed the primary data conducted by 345 police employees in Tiruchirappalli district at Tamil Nadu by using the Random Sampling Method. The methods applied in this study include descriptive analysis and factor analysis, and data was obtained from police employees using structured questionnaires with 8 constructions, including demographic variables with 50 items set on a 5-point Likert scale. A study described the process of creating an OSH Risk Assessment Self-Evaluation. An evaluation of an Occupational Health and Safety Risk Assessment among police officers showed significant progress toward establishing a safe and secure workplace. The commitment made to establishing a safety culture, together with the adoption of thorough rules and procedures, has not only protected our employees' well-being but also increased operational efficiency and effectiveness. Moving forward, continued monitoring, frequent evaluations, and ongoing training programs will be critical to maintaining these accomplishments and furthering our commitment to prioritize the health, safety, and welfare of all police officers.

Clean water is something most of us take for granted, yet millions of people around the world face serious risks because of contaminated water sources. Growing industries, urban expansion, and poor waste management have all taken a toll on water quality, making it harder to ensure that the water reaching homes and farms is truly safe. The traditional way of checking water quality, which involves collecting samples and sending them to a lab, takes too long and often catches problems only after people have already been exposed.
This paper presents an AI-Powered Water Quality Analyzer that takes a smarter, faster approach. Instead of waiting for lab results, the system uses Machine Learning to analyze water quality indicators like pH, turbidity, Total Dissolved Solids (TDS), temperature, and dissolved oxygen — and gives an instant verdict on whether the water is Safe, at Medium Risk, or Unsafe. We trained a Random Forest model on historical water data, and the system also watches for trends over time so it can warn users before conditions get dangerous, not after.
Built with Python, Scikit-learn, and Gradio, the system gives anyone — not just trained specialists — the ability to check water quality through a simple web interface. In testing, the model reached an accuracy of 94.33%, which shows it is reliable enough for real-world use.

The Delhi Transport Corporation (DTC), operating one of the largest urban bus networks in India, faces significant challenges in fleet management, route optimization, and schedule adherence. With a fleet of 6,100 buses serving approximately 2.9 million passengers daily across 5.01 lakh kilometers, the complexity of manual scheduling has led to operational inefficiencies including bus bunching, overcrowding, financial losses exceeding ₹14,000 crore, and suboptimal resource utilization . This research paper presents a comprehensive framework for an Automated Bus Scheduling and Route Management System (ABS-RMS) designed specifically for DTC. The proposed system integrates real-time data analytics, Geographic Information Systems (GIS), and advanced scheduling algorithms including Particle Swarm Optimization (PSO) for real-time bus assignment and controller-based scheduling . The system architecture incorporates linked and unlinked duty scheduling modules, geospatial route visualization, and predictive analytics for demand forecasting. Experimental analysis demonstrates potential improvements of 25-35% in schedule adherence, 20% reduction in operational costs, and significant enhancement in passenger satisfaction. The paper also addresses implementation challenges, data integration requirements, and provides a phased roadmap for deployment across DTC's 6,100-bus fleet.

This study focuses on analyzing the financial performance of Telangana Grameena Bank using the CAMEL model as a structured analytical framework. The banking sector plays a significant role in the economic development of a country by mobilizing savings, providing credit, and supporting various sectors such as agriculture, small-scale industries, and rural enterprises. In India, Regional Rural Banks (RRBs) were established to improve financial inclusion and provide banking services to underserved rural and semi-urban populations. Telangana Grameena Bank, being one of the prominent RRBs in the state, plays a vital role in supporting rural livelihoods and economic activities.
The study is based on secondary data collected from the published annual reports, financial statements, and other relevant sources of Telangana Grameena Bank for a period of five years from 2021 to 2025. A quantitative research approach is adopted to examine the financial performance of the bank. The CAMEL model is used as the primary tool for analysis, as it is widely accepted for evaluating the financial health and operational efficiency of banking institutions.
The CAMEL framework consists of five key components: Capital Adequacy, Asset Quality, Management Efficiency, Earnings Quality, and Liquidity. Each of these components represents an important aspect of a bank’s financial performance. Capital adequacy reflects the bank’s ability to absorb potential losses and maintain stability. Asset quality indicates the level of credit risk and the extent of non-performing assets. Management efficiency evaluates how effectively the bank utilizes its resources and manages operational activities. Earnings quality measures the bank’s ability to generate consistent income and sustain profitability. Liquidity assesses the bank’s capacity to meet its short-term financial obligations and maintain adequate cash flow.
In this study, multiple financial ratios under each CAMEL component are analyzed to provide a comprehensive understanding of the bank’s performance. The analysis includes indicators related to capital strength, credit risk, operational efficiency, profitability, and liquidity management. The study also considers the growth trends of the bank in terms of deposits, advances, net profit, and total business over the selected period.
The purpose of this research is to provide a systematic evaluation of the financial position of Telangana Grameena Bank using a structured and widely recognized model. By examining various financial indicators, the study aims to present a clear understanding of the bank’s operational and financial structure. The research also contributes to academic understanding by applying the CAMEL model to a Regional Rural Bank and highlighting its relevance in analyzing banking performance in the context of rural financial institutions.

Successful product launch plans are essential for long-term commercial success in the highly regulated and competitive pharmaceutical sector. The main strategic frameworks, factors, and difficulties affecting the introduction of pharmaceutical products are examined in this review. It emphasizes the significance of integrated strategy planning, stakeholder participation, product differentiation, and market access. The results imply that launch performance and competitive advantage are greatly enhanced by a well-organized framework that combines tactical and strategic components.

As a novel strategy to enhance oral hygiene and patient compliance, the current study focuses on the creation and assessment of fast-dissolving oral toothpaste paper strips. Fast dissolving oral strips are thin, flexible films that dissolve quickly when they come into touch with saliva. This eliminates the need for water and makes them very practical for use while on the go.
Numerous physicochemical and performance metrics, including thickness, weight uniformity, folding endurance, surface pH, disintegration duration, drug content uniformity, and in vitro dissolution profile, were assessed for the produced strips. The results showed that the prepared strips were homogeneous, mechanically stable, and quickly disintegrated in a matter of seconds, guaranteeing prompt release of active substances in the oral cavity.
Compared to traditional toothpaste, the new fast-dissolving toothpaste strips provide a number of benefits, such as mobility, accurate dosage, increased patient compliance, and appropriateness for both pediatric and elderly populations. As a result, this innovative dosage form offers a viable substitute for more convenient and efficient oral care delivery systems.

In today’s digital era, Online platforms have completely changed how recruitment works today. The study details the design and implementation of web-based job portal system developed to enhance efficiency and effectiveness in the hiring process for recruiters and applicants. This system enables users to create profiles, upload resume, search for relevant jobs and apply seamlessly, while recruiters can post jobs and manage applications efficiently. This application is developed using MERN stack ensuring a scalable and responsive architecture. To increase recruitment efficiency, the system provides a resume scoring mechanism that analyses candidate resume based on job requirements and assign a relevance score. Additionally, a skill-based job recommendation system is implemented to suggest suitable job opportunities by matching user skills with job descriptions.
Overall, the system provides a smart, user-friendly, and efficient solution for modern hiring needs.

In order to handle the important issues of data security, privacy, and interoperability in healthcare systems, this research article suggests a blockchain-based architecture for handling Electronic Health Records (EHRs). Fragmentation, security breach risk, and limited patient control over personal health data define the present EHR architecture. This paper looks at how blockchain technology may provide patient-centric access control and offer a distributed, unchangeable, transparent answer for EHR administration. By means of thorough investigation of both primary and secondary data, this study shows that blockchain implementations may considerably lower data breaches by 67%, increase interoperability among healthcare providers by 78%, and raise patient satisfaction with data ownership by 84%. Smart contracts for automated access control, cryptographic methods for safe data storage, and consensus systems for verifying health record transactions comprise the suggested architecture. By building trust, increasing operational efficiency, and enabling patients with more control over their health information, the results imply that blockchain-based EHR systems may transform healthcare data management.

The paper draws on the challenges faced by girls and women with disabilities in the 21st-century Zimbabwean society, with special reference to areas in and around Harare. It seeks to answer the question: What are the challenges faced by girls and women with disabilities in the community, family and education? How does this affect their future lives? Using a purposively selected sample of girls and women with disabilities in and around Harare, this qualitative paper uses semi-structured interviews to gather the lived experiences of this group of people. The paper is thus phenomenological in nature. The paper posits that by dint of their gender, challenges faced by women and girls with disabilities become more complex and heightened. Knowing the challenges that girls and women with disabilities face is vital for a more comprehensive and inclusive response to these challenges. The researcher recommends that the government should come up with adequate budgets to assist girls and women with disabilities in supporting full inclusion and employment. Funding for the inclusion of girls and women with disabilities. Renovations and building of infrastructure to be accessible is another recommendation given by the researcher.

Environmental noise pollution has become a major concern due to rapid urbanization, industrial growth, and increased transportation activities. Accurate measurement of sound levels is essential for monitoring, controlling, and mitigating noise pollution.
This research paper focuses on the measurement of sound levels using a sound level meter and analyzes noise intensity in different environments. The study highlights the importance of sound measurement in assessing public health risks and maintaining regulatory standards.
A sound level meter is used to quantify sound pressure levels in decibels (dB), providing reliable data for analysis. The research involves collecting sound data from
multiple locations such as residential, industrial, and commercial areas. The measured values are compared with permissible limits defined by environmental standards. The study also evaluates variations in sound levels based on time, location, and activity.
The results indicate that noise levels in urban and industrial zones often exceed safe limits, leading to potential health issues such as stress, hearing loss, and sleep
disturbances. This paper emphasizes the need for effective noise monitoring systems and awareness regarding noise pollution. The findings can be useful for policymakers, urban planners, and researchers working in environmental engineering and public health sectors.

Stock market prediction has been a topic of immense interest among researchers, investors, and financial analysts due to its significant economic implications. The unpredictable and volatile nature of financial markets makes accurate forecasting extremely challenging. Traditional statistical models often fail to capture nonlinear dependencies and temporal patterns inherent in stock price movements. This research paper proposes the application of Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), for stock price prediction. The study utilizes historical time-series data and applies preprocessing techniques such as normalization and sequence generation. The LSTM model is trained and evaluated using performance metrics like Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The findings indicate that LSTM models can effectively capture long-term dependencies and provide improved predictive accuracy compared to conventional methods. The study also discusses limitations and future research directions, including hybrid architectures, sentiment integration, and real‑time deployment.

The digital transformation of education has generated vast amounts of lecture recordings, yet most remain unprocessed and underutilised due to the time‑intensive nature of manual summarisation. This research paper presents an intelligent classroom audio recorder that not only captures high‑quality lecture audio but also automatically generates concise, coherent, and pedagogically relevant summaries. The system integrates a real‑time audio processing pipeline (noise reduction, speaker diarisation) with a state‑of‑the‑art automatic speech recognition (ASR) engine (Whisper large‑v2) and a novel two‑stage summarisation model. The summarisation model combines extractive selection (using BERT‑based sentence embeddings and PageRank) with abstractive generation (fine‑tuned BART‑large) to produce summaries that preserve key technical terms and logical flow. The system is evaluated on a corpus of 50 hours of university lectures (STEM and humanities). Using ROUGE, BERTScore, and human evaluation (expert lecturers), the proposed model achieves an average ROUGE‑L F1 of 0.52, BERTScore F1 of 0.86, and a human‑rated “adequacy” score of 4.2/5. The system also demonstrates real‑time operation with a latency of less than 5 seconds per minute of lecture. The paper discusses challenges such as handling accented speech, domain‑specific vocabulary, and lengthy lectures, and outlines future directions for multimodal summarisation (audio + slides) and personalised summaries.

Professional networking platforms like LinkedIn have become essential for job seeking, recruiting, and personal branding. A well‑optimised profile with relevant keywords significantly increases visibility in recruiter searches. However, many users struggle to identify the most impactful keywords for their industry, role, and experience level. This research paper presents an AI‑powered keyword recommendation system that analyses a user’s LinkedIn profile (headline, summary, work experience, skills) and suggests domain‑specific, high‑value keywords to improve search ranking and profile completeness. The system employs a hybrid architecture: (1) a contextual embedding model (fine‑tuned BERT) to encode profile text and extract latent topics, (2) a graph‑based keyword extraction algorithm (TextRank with part‑of‑speech filtering) to identify candidate terms, and (3) a learning‑to‑rank module trained on a large dataset of recruiter search logs and successful profile views to predict the “impact score” of each keyword. Additionally, the system provides competitor benchmarking by comparing the user’s keyword set with those of top profiles in similar roles. The model is trained on 500,000 anonymised LinkedIn profiles (with permission) and 10 million recruiter search sessions. Evaluation using held‑out data shows that profiles that adopted the recommended keywords saw a 34% increase in profile views and a 27% increase in recruiter messages within 30 days. The system achieves a mean average precision (MAP) of 0.82 for keyword relevance.

The COVID‑19 pandemic has placed unprecedented strain on healthcare systems worldwide, highlighting the urgent need for rapid, accurate, and scalable diagnostic tools. Reverse transcription‑polymerase chain reaction (RT‑PCR) remains the gold standard, but it suffers from delays, shortages, and false negatives. Chest X‑ray (CXR) imaging offers a faster alternative, as COVID‑19 often manifests as distinctive pulmonary opacities. This research paper presents a deep learning framework for automated detection of COVID‑19 from chest X‑ray images, distinguishing it from viral pneumonia, bacterial pneumonia, and healthy controls. The proposed model employs a convolutional neural network (CNN) architecture based on a fine‑tuned DenseNet‑121, pretrained on ImageNet, with additional attention modules to focus on abnormal lung regions. The model is trained on a large publicly available dataset (COVID‑19 Radiography Database) comprising 21,165 images: 10,192 normal, 6,016 COVID‑19, 6,012 viral pneumonia, and 3,000 bacterial pneumonia. Data augmentation (rotation, zoom, horizontal flip) is applied to mitigate overfitting. The model achieves an overall accuracy of 97.8%, with COVID‑19 detection sensitivity of 96.5% and specificity of 98.2%. The area under the ROC curve (AUC) for COVID‑19 vs. others is 0.993. Class activation maps (Grad‑CAM) provide explainability by highlighting the regions of the lungs that contribute most to the decision. The paper compares the proposed DenseNet‑121 with ResNet‑50, VGG‑16, and a baseline CNN, demonstrating superior performance. Challenges such as dataset bias, generalisation to other populations, and integration with clinical workflows are discussed. Future directions include multi‑modal fusion (CXR + clinical data) and lightweight models for edge deployment.

The proliferation of digital misinformation, including fake news articles and AI‑generated deepfake videos, poses a severe threat to democratic processes, public health, and social trust. Traditional fact‑checking methods are slow and cannot scale to the volume of online content. This research paper presents a unified machine learning platform for the automated detection of both fake news and deepfakes. For fake news detection, we employ a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Bidirectional Long Short‑Term Memory (Bi‑LSTM) with attention mechanisms, trained on textual features (headlines, body text) and metadata. For deepfake detection, we use a CNN‑based architecture (EfficientNet‑B4) fine‑tuned on spatial and temporal inconsistencies in video frames, supplemented with frequency‑domain analysis using Discrete Cosine Transform (DCT). The platform integrates both modules into a web‑based interface that accepts URLs or uploaded media. Evaluation on benchmark datasets (LIAR, FakeNewsNet for text; FaceForensics++, Celeb‑DF for video) yields an accuracy of 96.4% for fake news detection and 98.2% for deepfake detection (AUC = 0.99). The system also provides explainability via LIME (Local Interpretable Model‑agnostic Explanations) for text and heatmaps for video. The paper discusses challenges such as adversarial attacks, generalisation across languages, and computational efficiency, and outlines future directions for real‑time detection and multi‑modal fusion.

Employee welfare facilities play a crucial role in enhancing employee productivity and organizational performance. Welfare measures such as medical benefits, safety provisions, transportation facilities, canteen services, rest rooms, and insurance coverage significantly influence employees’ motivation and job satisfaction. This study examines the impact of welfare facilities on employee productivity at Ideal Conmix. The research is based on primary data collected from 30 respondents using a structured questionnaire with Likert scale statements. Percentage analysis, correlation, regression, and ANOVA were applied to analyze the data. The results indicate a positive and significant relationship between welfare facilities and employee productivity. The study concludes that effective welfare measures improve employee morale, reduce absenteeism, and enhance overall efficiency.

Echoes of Power: Gossip, Whispers, and Rumour in Indian History examines the cultural, political, and philosophical role of informal speech in shaping authority, social order, and collective consciousness in India. Challenging the notion of gossip as trivial, the paper argues that whispered communication functioned as a crucial medium of negotiation, surveillance, and resistance in premodern societies. Drawing on interdisciplinary approaches from sociology, philosophy, and reception theory, it situates Indian oral practices within a broader global context in which unofficial speech could both legitimise and destabilise power. Through close readings of Vedic hymns, Upanishadic dialogues, the Mahābhārata, the Rāmāyaṇa, and Kautilya’s Arthaśāstra, the study demonstrates how whispers operated simultaneously as instruments of control and as vehicles of dissent. The Vedic concept of vāc (speech) intensifies this ambivalence, attributing sacred potency to words while acknowledging their disruptive potential. The discussion further explores gendered and spatial dimensions, showing how courtyards, temples, and monastic spaces shaped the acoustic circulation of secrets, and how women’s whisper networks enabled subtle forms of social regulation. By reinterpreting gossip as a dynamic cultural force, the paper recovers the muted voices of everyday life and reveals how murmured conversations quietly shaped the moral and political fabric of Indian history.

This study examines the effectiveness of dual-role marketing strategies in Indonesian Army hospitals in West Java, which are required to simultaneously fulfill national defense mandates and provide public healthcare services. Despite growing attention to public-sector marketing, integrative analyses that address the intersection of military and civilian service roles remain limited. The research adopts a qualitative literature review approach, drawing on international journal articles, scholarly books, and policy documents related to healthcare marketing and military institutions. Through this approach, the study synthesizes existing knowledge to identify key factors influencing the success of dual-role marketing. The findings reveal that the effectiveness of dual-role marketing is shaped by three primary factors. First, strengthening military institutional identity serves as a source of differentiation and enhances service credibility. Second, the application of public service–oriented marketing principles improves public trust, accessibility, and community engagement. Third, adaptive organizational governance is essential to align military operational demands with the expectations of civilian healthcare users. In conclusion, the success of marketing strategies in Indonesian Army hospitals depends on their ability to maintain a dynamic balance between defense functions and public service responsibilities. This study contributes to the conceptual advancement of public-sector marketing by integrating military and healthcare perspectives, while also offering practical insights for policymakers and hospital administrators in optimizing service delivery within dual-role institutional contexts.

The research adopts a descriptive and analytical design using a case study approach. Primary data was collected through structured questionnaires from 80 respondents across various functional areas, including production, IT, quality control, and supply chain, while secondary data was gathered from company reports and existing literature. Statistical tools such as percentage analysis, mean scores, and Likert scale analysis were employed to interpret the data.The findings reveal that SAP ERP significantly improves production planning accuracy, inventory visibility, resource utilization, and supply chain coordination, with over 70% of respondents expressing positive agreement. The system also contributes to cost reduction, minimizes manual errors, and enhances product quality consistency. However, challenges such as system complexity, lack of training, and technical issues persist, limiting the full utilization of ERP capabilities.The study concludes that while SAP ERP has a substantial positive impact on manufacturing efficiency, its effectiveness depends on proper training, user adoption, and continuous system optimization. The research provides practical recommendations for improving ERP utilization and contributes to the existing literature on ERP-driven manufacturing performance in the Indian context.

Pharmaceutical marketing serves a critical and essential function in fostering business growth within the intricate and highly regulated healthcare sector. Unlike the promotion of fast-moving consumer goods, pharmaceutical marketing primarily focuses on healthcare professionals (HCPs) and patients, emphasizing the communication of crucial scientific and clinical information to support informed therapeutic decisions. This research study explores how effective marketing strategies—integrating both conventional and contemporary digital approaches—enhance product visibility, shape prescribing patterns, strengthen brand credibility, and ultimately contribute to an organization’s market share and financial performance.
The study evaluates the fundamental elements of the pharmaceutical marketing mix (Product, Price, Place, Promotion, People, Process, Physical Evidence, Packaging, Partnership, and Policy) and their relevance in the modern environment. Key strategies examined include precision targeting, digital interaction via online platforms and mobile applications, patient-focused programs, and the significant role of sales representatives in professional education and relationship development.
The research utilizes a blend of primary data (surveys involving marketing professionals and HCPs) along with secondary sources to evaluate the impact and efficiency of these strategies, while addressing ethical considerations and strict regulatory frameworks.
The findings indicate that effective marketing initiatives, by reducing information asymmetry and facilitating faster patient access to innovative therapies, directly support the financial stability of pharmaceutical firms and encourage further investment in research and development (R&D). The study concludes that a strategically designed, ethical, and data-oriented marketing approach is crucial for pharmaceutical companies to succeed in a competitive landscape, strengthen their market position, and contribute to enhanced public health outcomes.

In today’s fast-paced lifestyle, deciding what to cook with available ingredients is a common challenge. The RecipeBowl system is an intelligent web-based solution that uses Natural Language Processing (NLP) to analyze ingredients entered by the user and recommend suitable recipes, home remedies, and wellness tips. The system enhances user experience by providing step-by-step cooking instructions and integrating YouTube video tutorials for better understanding. It helps reduce food waste, saves time, and promotes a healthy lifestyle by offering personalized and efficient cooking assistance.

Modern high-rise buildings are increasingly being designed with a focus on sustainability to address rapid urbanization, environmental challenges, and resource efficiency. This study evaluates the effectiveness of sustainable design strategies in a G+10 storey reinforced concrete (RCC) high-rise building by comparing its performance with a conventional building model. Key sustainable interventions such as optimal building orientation, passive design techniques, natural ventilation, daylight utilization, water conservation systems, and renewable energy integration are incorporated into the proposed model.
The performance of both building models is assessed using parameters including energy consumption, water usage, material efficiency, indoor environmental quality, and overall environmental impact. The results indicate that the sustainable building achieves a significant reduction in energy consumption (approximately 30–40%) and water usage (around 35–40%) compared to the conventional design. Additionally, improvements in indoor environmental quality and reduction in carbon emissions are observed due to the use of eco-friendly materials and efficient systems.
The comparative analysis demonstrates that integrating sustainable design strategies enhances building performance while reducing operational costs and environmental impact. The study highlights the importance of adopting an integrated approach to sustainability in modern high-rise construction, particularly in urban contexts, to achieve long-term environmental and economic benefits.

The application of herbal hair oils has garnered considerable attention due to the increasing awareness of the benefits of natural products in hair care. These oils are prepared using medicinal plants recognized for their therapeutic properties in stimulating hair growth, minimizing hair loss, and enhancing scalp health. This review examines both traditional and contemporary approaches to the development and assessment of herbal hair oil. Important herbal components, such as Amla, Bhringraj, Brahmi, Neem, and Hibiscus, are highlighted for their contributions in nourishing hair and managing common conditions like dandruff and hair thinning. The study further emphasis’s different extraction techniques, including maceration, infusion, and decoction, along with physicochemical and biological evaluation methods to ensure product level and effectiveness. By integrating traditional knowledge and scientific authentication, this review establishes a basis for the development of safe and effective herbal hair oil preparation.

Antibiotic misuse remains a major contributor to antimicrobial resistance, adverse clinical outcomes, and increased healthcare burden. This study was conducted to evaluate antibiotic prescribing patterns, assess adherence to Indian Council of Medical Research (ICMR) guidelines, and examine their association with clinical outcomes in hospitalized patients. A hospital-based observational study was carried out among 400 inpatients receiving antibiotic therapy. Data on demographic characteristics, comorbidities, infection profiles, antibiotic regimens, and clinical outcomes were collected from medical records. Prescriptions were evaluated for compliance with ICMR treatment guidelines, and antimicrobial stewardship practices were assessed. Respiratory infections were the most common indication for antibiotic use. Broad-spectrum β-lactam and β-lactamase inhibitor combinations were frequently prescribed, particularly in moderate to severe infections. Guideline adherence was observed in 83.75% of prescriptions. Clinical improvement was noted in 79.25% of patients, with 78% achieving recovery at discharge and a mortality rate of 4%. Antimicrobial stewardship review was documented in 50.5% of cases, indicating partial implementation. The findings suggest that adherence to national treatment guidelines is associated with favorable clinical outcomes and supports the concept of precision-oriented prescribing. However, gaps in stewardship practices highlight the need for continuous monitoring and optimization of antibiotic therapy. Strengthening antimicrobial stewardship and promoting evidence-based prescribing may further improve treatment effectiveness and help mitigate antimicrobial resistance.

This research takes an innovative approach to the narratives of Anna Sewell’s Black Beauty and Virginia Woolf’s Flush. It finds that their texts may not straightforwardly represent animal subjectivity; instead, they are systems that produce new disciplined forms of feeling, behaviour and relationality in animals and humans alike. This paper does not ask how animals are represented but rather how these narratives show how animals are tied to the idea of obedience and control. Black Beauty identifies suffering with docility within systems of labour and domestication. In contrast, Flush throws this disciplining logic off with its valorization of sensory excess and instability with an animal perception that refuses to conform.
Based on posthumanist theory, companion species philosophy and Animal Studies, the paper argues that these texts do not simply critique anthropocentrism, they also regulate interspecies relations. The novel Black Beauty creates an effective economy in which the animal is legible, patient, and morally instructive for ethical recognition. Although the Flush narrative is managed from the human perspective, the author occasionally exploits smell, desire and movement for loss of control.
Reception theory also shows how the texts situate the reader within this framework. The clear-cut morality of Black Beauty creates stability in reader response; Flush's uncertainty, by contrast, pushes readers to deal with non-assimilable forms of experience. In the end, the essay shows that animal narratives are more than sites of ethical reflection; they also govern the animal and human in literary and cultural systems.

Employee grievance handling mechanisms play a vital role in ensuring workplace harmony and organizational effectiveness. This study evaluates the effectiveness of the grievance handling mechanism at Sri Amman Coating and Colouring, Hosur. Primary data were collected from 100 respondents ndents using a structured questionnaire. Statistical tools such as percentage analysis, ANOVA, t-test, Z-test, and Chi-square test were used. The findings indicate that improvements are required in communication, transparency, and timely resolution of grievances. The study recommends structured grievance procedures and digital systems to enhance efficiency.

In the modern automotive industry, organizational success depends not only on technological advancement but also on effective human resource practices. This study examines the impact of organizational culture on employee commitment at Hanon Automotive Systems Private Limited. Organizational culture, defined by shared values, beliefs, and practices, significantly influences employee behavior, motivation, and engagement. Employee commitment, comprising affective, continuance, and normative dimensions, is essential for organizational stability and performance. Using theoretical frameworks such as Social Exchange Theory, Schein’s Model of Organizational Culture, and Meyer and Allen’s Three-Component Model, this study explores how cultural practices shape employee commitment. The findings highlight that supportive leadership, transparent communication, and ethical practices enhance employee loyalty and performance. The study also identifies gaps in cultural communication and career development that may affect long-term commitment.

In the digital era, organizations are experiencing rapid technological changes that require employees to continuously develop new skills. Upskilling and reskilling have become important human resource strategies that help employees adapt to digital transformation. This study examines the effectiveness of upskilling and reskilling initiatives implemented at Titan Company Limited, Jewellery Division, Hosur. The study aims to understand employee awareness, participation, satisfaction, and the impact of digital training programs on employee performance. A descriptive research design was adopted and primary data were collected from 150 employees using a structured questionnaire. Statistical tools such as percentage analysis, ANOVA, correlation, and regression were used for analysis. The findings reveal that employees are aware of training programs, actively participate in them, and believe that such programs improve digital skills, productivity, and job security. The study concludes that continuous workforce development is essential for organizational competitiveness in the digital era.

The rapid adoption of Artificial Intelligence (AI) in Human Resource Management (HRM) is transforming traditional workplace practices and redefining employee experiences. This study examines the impact of AI integration in HR functions—such as recruitment, performance management, employee engagement, and decision-making—on employee attitude and behaviour.AI-driven tools enhance efficiency, accuracy, and objectivity in HR processes, reducing human bias and administrative workload. However, the increasing reliance on automation also raises concerns among employees regarding job security, privacy, and reduced human interaction. These contrasting effects significantly influence employee attitudes, including job satisfaction, motivation, trust in management, and organizational commitment. The study further explores how AI adoption shapes behavioural outcomes such as adaptability, productivity, collaboration, and resistance to change. While some employees demonstrate positive behavioural shifts by embracing digital transformation and upskilling, others may exhibit stress, anxiety, or reluctance due to perceived threats from automation.

This descriptive paper analyzes behavioral reactions of investors and financial markets to central bank digital currency (CBDC) announcements in emerging economies. Moreover, as CBDCs become more commonly featured in monetary policy discussions their announcements are likely to affect market also expectations, investor behavior and short-run trading particularly in emerging financial markets that have high informational frictions, policy sensitivity and macroeconomic volatility. The paper applies an event-study methodology to a sample of official CBDC-related announcements by central banks in selected emerging economies over a set period. Market reactions are measured by abnormal stock market returns, currency movement, trading volume and volatility in event windows surrounding announcement dates. Panel regression techniques are used to test the robustness of the analysis, assessing institutional credibility, inflation dynamics, digital financial development and announcement type, as potential determinants of market response in terms of significance (magnitude) and direction. The results show that CBDC announcements have statistically significant yet heterogeneous effects on behaviors. Market reactions are relative to exploratory statements, and consequently more pronounced for pilot-stage/implementation-oriented announcements; countries with lower policy credibility/higher macroeconomic uncertainty provide stronger volatility/sentiment dynamics. This find creates a bridge in the literature between the impact of digital currency policy communication, behavioral finance in emerging markets (with an emphasis on China), and introduces some real effects this policies can generate within investors’ expectations suggesting that if you have been vocally defending against BCTs and ghost currencies leading to a crypto panic then everything should be directed to clear and credible central bank communication which is key in reducing uncertainty mitigating investor expectations ultimately stabilizing the financial market.

This project introduces an intelligent, web-based architectural management platform that solves the gap between design visualization and financial tracking. By integrating Babylon.js for real-time 3D model interaction and Machine Learning for cost forecasting, the system provides a "Digital Twin" of construction sites. The platform enhances the client-architect relationship through an automated AI Agent (Archie) that answers queries regarding project status and market trends, ensuring transparency and reducing budget overruns.

Healthcare organizations generate vast amounts of data daily, including patient records, treatment details, and administrative information. Managing and interpreting this data effectively is a major challenge. This study focuses on the role of healthcare data visualization in improving patient outcomes and operational efficiency at Kauvery Hospital. Data visualization tools such as charts, graphs, and dashboards help convert complex data into simple and meaningful insights, enabling faster and more accurate decision-making. The study adopts a descriptive research design using both primary and secondary data. A sample of 100 respondents, including doctors, nurses, and administrative staff, was selected using convenience sampling. Statistical tools such as percentage analysis, Chi-square test, and ANOVA were used for analysis. The findings reveal that data visualization significantly improves decision-making, reduces errors, enhances efficiency, and supports better patient care.

The purpose of this study was to examine the influence of strength training and traditional training on selected biomotor and power performance variables among college-level badminton players. Forty-five inter-collegiate badminton players from Erode District, aged between 18 and 25 years, were randomly selected as subjects for the study. The subjects were divided into three groups of fifteen players each. Group I underwent a structured strength training programme, Group II underwent traditional badminton training and Group III served as the control group, which did not participate in any specific training programme apart from their routine activities.
The experimental training programme was conducted for a period of twelve weeks with five sessions per week. The selected biomotor and power performance variables included speed, agility, muscular strength and leg explosive power. Standardized tests were administered to measure the respective variables before and after the training period. The collected data were analyzed using Analysis of Covariance (ANCOVA) to determine the significant differences among the groups.
The results of the study revealed that both strength training and traditional training produced significant improvements in the selected biomotor and power performance variables among college-level badminton players, whereas the control group showed no significant improvement. The strength training group demonstrated comparatively greater improvement in muscular strength and explosive power, while the traditional training group showed notable improvement in speed and agility due to regular badminton skill drills such as smash, rally, footwork, court movement and defensive returns.
It was concluded that systematic strength training, when combined with traditional badminton training methods, is highly effective in enhancing performance-related variables among badminton players.

Acne by definition is multifactorial chronic inflammatory disease of pilosebaceous units. Propionibacterium acnes and staphylococcus epidermidis are considered as the major skin bacteria that cause the formation of acne. Although acne does not pose serious threat to general health, it is one of the most socially distressing conditions especially for adolescents. Acne vulgaris is a common human skin disease characterized by areas of skin with seborrhoea (scaly red skin), comedowns (blackheads and whiteheads), papules (pinheads), nodules (large papules), pimples and possibly scarring. Demand of herbal formulations has been continuously growing in the world market. The present work deals with the preparation and evaluation of the anti-acne cream containing hydroalcoholic extract of pomegranate (Punica granatum, puniaceae). The plant has been reported in the literature having good anti-microbial, antioxidant and anti-inflammatory activity. Red beetroot is well known for its high proportion of battalions, with great potential as functional food ingredients due to their health-promoting properties.

Cinnamomum verum is a widely recognized spice plant valued for its diverse medicinal and pharmacological properties. Its former botanical name, Cinnamomum zeylanicum, originates from the old name of Sri Lanka, Ceylon. Since ancient times, this plant has been extensively used as a therapeutic spice. It is indigenous to Sri Lanka and the southern regions of India. Belonging to the Lauraceae family, Cinnamomum verum is commonly obtained from the dried inner bark after removal of the outer cork and parenchymatous layers.
This plant is a well-known medicinal herb with broad applications in traditional and modern systems of medicine. It has long been used as a flavoring agent in food and as an active ingredient in pharmaceutical formulations for treating various disorders. In contemporary usage, it is incorporated into products such as confectioneries, chewing gums, mouthwashes, and toothpastes.
Cinnamomum verum is rich in volatile oils, mainly cinnamaldehyde, cinnamic acid, and cinnamate, along with eugenol, which serves as a major bioactive compound responsible for several biological activities. The plant exhibits a wide spectrum of pharmacological effects, including antimicrobial, wound healing, antidiabetic, anti-HIV, anxiolytic, and anti-Parkinson’s activities.
Key phytoconstituents include eugenol, cinnamaldehyde, cinnamyl acetate, copaene, and camphor. Among these, cinnamaldehyde has been extensively studied for its therapeutic potential. This review aims to provide a comprehensive overview of the plant, covering its morphology, phytochemical composition, and pharmacological activities.

One of the most important factors influencing contemporary consumer behavior is perceived social responsibility (PSR). Customers are now motivated by a company's social behavior in addition to price and quality due to growing knowledge of environmental, ethical, and social issues. The notion of PSR, its theoretical underpinnings, its determinants, and its influence on consumer purchasing behavior are all examined in this review study. The paper demonstrates how PSR affects customer perceptions, purchase intentions, loyalty, and actual purchasing decisions by synthesizing data from previous research.

The present study was undertaken to formulate and evaluate an herbal facewash gel containing Aloe vera as the primary active ingredient. The formulation was developed using natural ingredients including Aloe vera gel, neem extract, tulsi extract, glycerin, and mild surfactants. The prepared facewash was subjected to various physicochemical parameters including pH determination, viscosity measurement, foaming ability assessment, and stability testing. The formulated product exhibited satisfactory physicochemical properties with a pH of 6.8, which is compatible with human skin. The facewash demonstrated excellent cleansing action along with moisturizing properties attributed to the presence of Aloe vera. Stability studies conducted over a period of three months at different temperature conditions revealed that the formulation remained stable without any significant changes in physical appearance, pH, or microbial growth. The herbal facewash was found to be effective in removing dirt, excess oil, and impurities from the skin surface while maintaining the skin's natural moisture barrier. The results of this study suggest that the formulated Aloe vera based herbal facewash can serve as a safe and effective alternative to synthetic chemical-based cleansers, offering additional skin benefits through its natural constituents.

This paper examines how sustainable finance education and financial literacy can affect the adoption of environmentally friendly financial behavior by people. It dwells on the role of educational interventions, access to financial information, and financial self-efficacy in enabling informed decision making as regards to green investments and sustainable financial practices. The main data was gathered with the help of a structured questionnaire given to 272 participants with diverse educational, professional and income levels.
The results suggest that financial education is critical to the increasing awareness of sustainable financial products and increasing the capacity of individuals to estimate the environmentally friendly investment opportunities. Credible sources of information especially online platforms and consultancy support reinforce financial literacy and promote accountable financial decision-making. Financial self-efficacy is a central factor that connects knowledge and behavior and allows people to effectively implement financial knowledge in sustainable practices. Education and profession are also demographic factors that affect the interest in sustainable finance.
The paper provides the significance of merging ideas of sustainability into financial literacy programs to enable responsible economic conduct and environmental and financial health in the long term.

Kenya's pursuit of Universal Health Coverage (UHC) faces critical financing challenges threatening sustainability and equity. This article examines Kenya's healthcare financing landscape following the 2024 transition from NHIF to the Social Health Insurance Fund (SHIF). Using household expenditure data from 18,450 households and health facility records (2019-2024), we assess financing adequacy, equity, and sustainability through descriptive analysis, benefit incidence analysis, and microsimulation modeling. The findings reveal that healthcare spending of 4.8% of GDP remains below the 15% Abuja target, with out-of-pocket expenditure at 27% of total health spending, exceeding the UHC threshold of 20%. Catastrophic health expenditure affects 11.3% of households, reaching 23.7% among the poorest quintile. SHIF generates approximately KES 98 billion annually, leaving a financing gap of KES 29-180 billion depending on benefit package comprehensiveness. The study proposes a multi-pronged financing strategy combining progressive payroll contributions, earmarked health taxes, efficiency improvements, and strategic purchasing reforms. Our recommendations emphasize evidence-based priority-setting, strengthened primary healthcare, and enhanced financial protection for vulnerable populations while acknowledging implementation challenges within Kenya's devolved governance structure.

This paper will address the principles of taxation, the merits and demerits of the progressive and regressive tax systems, and the benefits and drawbacks of moving away with Electronic Government Procurement Systems (EGPS) from the manual one in Kenya. The principles of Equity, Efficiency, and Simplicity form the foundation of the study since they are the base tenets of evaluating the effectiveness of a tax regime. The systematic review and comparative policy analysis methodology was adopted to rely on canonical material about public economics and the current policy assessments. The results demonstrate that there is a natural trade-off between equity and efficiency in progressive taxation which requires complicated design decisions in the quest of optimal taxation. More so, the analysis of tax regime highlights the redistribution effects of consumption-oriented, retrogressive taxes. As the Kenyan EGPS analysis indicates, it can be a more transparent, accountable, and efficient system of spending the state funds, which opens it to the possibility of becoming the one that meets the constitutional requirement to have a system that is cost-effective. Nevertheless, application is greatly hindered by such obstacles as resistance on the part of established interests, capacity inequalities, and legislative inertia, which the Public Choice Theory foresaw. The paper concludes that the best policy to employ in public finance is one that is holistic: a policy that balances both the best tax regime and sound digital mechanisms to curb rent-seeking behaviour and enhance value-for-money in state expenditure.

Acne through manner of method of definition is multifactorial persistent inflammatory disease of pilosebaceous units. Propionibacterium acnes and staphylococcus epidermidis are considered due to the fact the most pores and pores and skin bacteria that motive the formation of zits. Although zits does now not pose severe danger to popular health, it`s some distance one of the most socially distressing conditions especially for adolescents. Acne vulgaris is a now no longer unusualplace human pores and pores and skin disease characterized through manner of method of areas of pores and pores and skin with seborrhoea (scaly purple pores and pores and skin), comedones (blackheads and whiteheads), papules (pinheads), nodules (large papules), acne and likely scarring. Demand of herbal formulations has been continuously growing withinside the worldwide market. The present artwork gives with the coaching and evaluation of the anti-zits cream containing extract of Pomegranate. The plant has been suggested withinside the literature having perfect anti- microbial, anti-oxidant and anti-inflammatory activity. Red beetroot is widely recognized for its immoderate percent of betalains, with notable functionality as sensible food additives due to their health- promoting properties.

The jurisprudence of bail in India stands at a critical crossroads, caught between the historical baggage of colonial procedure and the progressive mandates of constitutional rights. This research paper undertakes a comprehensive analysis of the evolution, current deficiencies, and necessary reforms in Indian bail law. It argues that the existing framework, primarily governed by the Code of Criminal Procedure (CrPC), 1973, operates on a binary of ‘bailable’ and ‘non-bailable’ offences a classification that has proven inadequate to address the complexities of modern criminality, including economic offences, terrorism, and digital crimes. The paper traces the trajectory from the draconian undertones of the Code of 1898 to the post-Maneka Gandhi era, where Article 21’s expanded interpretation mandates that bail be the rule and jail the exception. Through a critical examination of landmark judgments from Gurbaksh Singh Sibbia to Vernon Gonsalves the study highlights judicial inconsistencies, the tyranny of monetary conditions, and the epidemic of undertrial incarceration. The paper proposes a structured reform agenda, including the insertion of a presumption of bail after a specified period of detention (akin to Section 436A of CrPC), the introduction of risk assessment tools, the abolition of the ‘discretionary’ culture in favor of ‘default’ bail, and the enactment of a separate Bail Act. Concluding with a comparative glance at the UK Bail Act 1976 and US pretrial reforms, the paper asserts that reforming bail is not merely a procedural necessity but a constitutional imperative to protect liberty, equality, and the presumption of innocence.

The AI-Based Text to Quiz Generator plays an important role in transforming textual learning materials into structured assessment content for educational environments. However, preparing quizzes manually from textbooks, lecture notes, and study materials is time-consuming and requires significant effort from educators. This project proposes an AI-Based Text to Quiz Generator that integrates Natural Language Processing (NLP) techniques to automatically generate quiz questions from textual input. The system processes text using preprocessing techniques such as tokenization, stopword removal, and sentence segmentation to prepare the data for analysis. Important keywords are extracted using methods such as TF-IDF and RAKE algorithms to identify significant concepts within the content. Based on these extracted keywords, the system generates Multiple Choice Questions (MCQs), fill-in-the-blank questions, and True/False questions along with correct answers. The proposed system improves efficiency in quiz preparation, reduces manual workload for educators, and enables automated generation of structured assessment content for digital learning platforms.

As of the 7th of July 2024, 775,754,322 confirmed cases of COVID-19, including 7,053,902 deaths worldwide, had been reported to the WHO (World Health Organization). Nevertheless, untill the 15th of July 2024, a total of 13,578,710,228 vaccine doses had been administered, with almost no country spared from COVID-19 attacks. The pathophysiology of this virus is complicated, and several symptoms require a deep understanding of the actual mechanisms. It is unclear why some patients develop severe symptoms while others do not, although literature suggests a role for vitamin D. Vitamin D plays a crucial role in the infection or in ameliorating the severity of symptoms. The mechanism of action of vitamin D and vitamin D deficiency (VDD) is well understood. VDD is associated with increased hospitalization of severely ill patients and increased levels of COVID-19-caused mortality. Recent studies suggest that vitamin D levels and genetic variations in the vitamin D receptor (VDR) gene significantly impact the severity and outcomes of COVID-19, especially in the infections caused by Delta and Omicron variants. Furthermore, VDD causes immune system dysregulation upon infection with SARS-CoV-2, indicating that vitamin D sufficiency is crucial in fighting against COVID-19 infection. The therapeutic effect of vitamin D raises interest in its potential role as a prophylactic and treatment adjunct. We evaluate the immunomodulatory effects of vitamin D and its ability to enhance the efficacy of new antiviral drugs like molnupiravir and paxlovid against SARS-CoV-2. This review discusses the role of vitamin D sufficiency and VDD in COVID-19 initiation and progression, emphasizing the molecular mechanisms by which vitamin D exerts its actions as a proactive step for the next pandemic. However, there is still no clear evidence of vitamin D's impact on prevention and treatment, leading to contradictory findings. Therefore, large-scale randomized trials are required to reach a definitive conclusion.

The study examined efficiency indicators namely resource utilization and financial efficiency and effective governance in public universities in Edo State, Nigeria. Effective. The study was guided by 2 research questions and 2 hypotheses. A correlational research design was employed for the study. The population consisted of 1,480 lecturers in public universities in Edo State. A sample of 278 lecturers, representing 18.78% of the total population, was selected using stratified random sampling. The instrument used for data collection was a structured questionnaire titled “Efficiency Indicators and Governance Rating Scale” (EIGRS), developed by the researcher. The face and content validity of the instrument were determined by an expert in Measurement & Evaluation. The reliability of the instrument was confirmed using the Cronbach Alpha method, yielding reliability coefficients of 0.83 and 0.87 for the two subscales: Resource Utilization and Financial Efficiency and Effective Governance, respectively. The questionnaire was administered by the researcher with the assistance of three trained research assistants. A total of 278 completed copies were used for data analysis, using Pearson coefficient of determination and inferential statistics such as Pearson Product Moment Correlation. The two hypotheses were tested at 0.05 level of significance. The findings revealed that a significant positive relationship between resource utilization, financial efficiency and effective governance of public universities in Edo State, Nigeria. Based on these findings, it was recommended amongst others that university Resource Management Departments and Administrative Heads should optimize the use of available resources by adopting modern management systems and conducting regular audits to ensure efficiency and minimize waste.

The transition to autonomous vehicles represents a paradigm shift in modern transportation, offering the potential to drastically reduce traffic fatalities that were caused by human error. However, the legal integration of self-driving technology presents profound challenges to traditional liability frameworks, which are fundamentally predicated on human agency, negligence, and control. This research paper provides an exhaustive and comparative analysis of the legal status of autonomous vehicles, focusing on the friction between legacy laws and advanced artificial intelligence. Utilizing the Society of Automotive Engineers (SAE) levels of automation, the study dissects the "responsibility gap" that emerges when the driving task shifts from a human subject to an automated software driven system. The paper evaluates the viability of transitioning from fault-based negligence to product liability, strict liability, and no-fault compensation systems. Furthermore, it conducts a comprehensive global review of emerging regulatory frameworks, detailing recent federal legislative efforts in the United States, the European Union's Artificial Intelligence Act and Product Liability Directive, the United Kingdom's Automated Vehicles Act 2024, Germany's pioneering ethical guidelines, and the infrastructural constraints within India's Motor Vehicles Act. Finally, the paper synthesizes recent jurisprudence—including high-profile litigation involving Tesla, Uber, and General Motors’ Cruise—to illustrate how courts are currently adjudicating the failures of both driver-assistance technologies and fully autonomous fleets. Ultimately, the research concludes that the safe and equitable deployment of autonomous vehicles requires abandoning fragmented legal frameworks in favor of harmonized, strict-liability systems that ensure rapid victim compensation and establish clear corporate accountability.

In recent years, researchers worldwide have recognized that the effective duration of any antimicrobial agent is limited due to the rising development of microbial resistance. As a result, numerous investigations have focused on identifying alternative antimicrobial sources, particularly from plant origins. The objectives of this study were to evaluate the antimicrobial potential of essential oils extracted from Australian-grown Ocimum tenuiflorum (Tulsi), to quantify the volatile constituents present in flower spikes, leaves, and essential oil, and to identify the compounds responsible for the observed activity.
The broth micro-dilution technique was employed to determine the minimum inhibitory concentration (MIC) of Tulsi essential oil against selected microbial strains. The essential oil, at concentrations of 4.5% and 2.25%, completely suppressed the growth of Staphylococcus aureus (including MRSA) and Escherichia coli, while exhibiting only partial inhibition against Pseudomonas aeruginosa. Out of 54 compounds detected in Tulsi leaves, flower spikes, and essential oil, three—camphor, eucalyptol, and eugenol—are suggested to be primarily responsible for the antimicrobial activity.
Since S. aureus (including MRSA), P. aeruginosa, and E. coli are significant pathogens responsible for skin and soft tissue infections, Tulsi essential oil may serve as a promising topical antimicrobial agent for managing such infections.

Financial literacy plays a crucial role in strengthening financial inclusion and empowering individuals to make informed economic decisions. In India, several key institutions such as the Reserve Bank of India, Securities and Exchange Board of India, Insurance Regulatory and Development Authority of India, Pension Fund Regulatory and Development Authority, National Bank for Agriculture and Rural Development, Small Industries Development Bank of India, National Payments Corporation of India, and Financial Stability and Development Council actively contribute to promoting financial awareness through various programs and initiatives. The National Centre for Financial Education, through the National Strategy for Financial Education, provides a structured framework to enhance financial knowledge, skills, and behavior among different sections of society. These initiatives focus on areas such as banking, insurance, pensions, investments, and digital payments, while also addressing issues like fraud prevention and consumer protection. Special emphasis is given to rural populations, women, students, and economically weaker sections to ensure inclusive growth. Overall, these coordinated efforts aim to build a financially aware society, improve economic stability, and support sustainable development in India.

This paper presents a robust machine learning framework designed for the automated classification of fetal health conditions from Cardiotocography (CTG) examination records, representing a meaningful advancement in the domain of prenatal diagnostic intelligence and clinical decision support systems. The accurate interpretation of CTG signals is inherently complex due to high inter-observer variability, nuanced fetal heart rate (FHR) pattern morphologies, and the multi-dimensional nature of uterine contraction data. To address these challenges, the proposed system employs a comprehensive feature engineering pipeline alongside an ensemble of supervised machine learning classifiers—principally Random Forest and Gradient Boosting—to distinguish between three clinically critical health states: Normal, Suspect, and Pathological. The raw classification output was further reinforced through threshold-based confidence filtering and class-imbalance correction strategies using SMOTE (Synthetic Minority Oversampling Technique), specifically adapted to the skewed distribution of pathological fetal outcomes in real-world obstetric datasets. Experimental evaluation demonstrated robust predictive performance with an overall multi-class classification accuracy of 93.60%, alongside high precision and recall values across all three target categories. The trained pipeline was deployed via a lightweight Flask-based REST API web interface, enabling real-time diagnostic inference and integration with hospital electronic health record (EHR) systems.

The rapid growth of the herbal and ayurvedic drug industry has necessitated increasing interest in dependable identification methods that guarantee authenticity, purity, and safety of the crude drug materials. Traditional approaches involving morphology, microscopy, or phytochemical profiling very often prove to be inadequate in cases of powdered, processed, or intentionally adulterated samples with visually similar materials. DNA barcoding has emerged as an exact, reproducible, and universally applicable method at a molecular level for species authentication. The method of DNA barcoding, through the analysis of short, standardized genetic loci such as ITS2, rbcL, and psbA-trnH, can correctly identify even closely related taxa and reveal intentional or unintentional substitution in commercial herbal raw materials. The review consolidates major advances in DNA barcoding applications for crude drug verification, summarizes successful case studies for detection of adulteration, evaluates marker performances in complex matrices, and also shows integrated workflows combining barcoding with chemo profiling for enhanced quality assurance. The status of current gaps in research, including limited availability of reference sequences, challenges posed by degraded DNA, and a lack of sufficient validation for polyherbal formulations, is also presented. Addressing these gaps is bound to establish DNA barcoding as a routine component of regulatory quality-control frameworks and ensure global confidence in herbal medicine supply chains.

This study presents a comprehensive bibliometric evaluation of research on Good Agricultural Practices (GAP), utilizing Scopus data to examine the temporal and thematic progression of the field from 1964 to 2024. GAP, developed to ensure food safety, environmental sustainability, and economic feasibility in agriculture, has gained global recognition. However, research in this domain remains fragmented across disciplines such as agronomy, environmental science, and food safety. The study of GAP is essential for addressing global challenges, including climate change and food security. It serves as a framework to enhance sustainable agricultural productivity, reduce environmental impacts, and strengthen the resilience of production systems against escalating global pressures.
By analyzing research trends, this study highlights the pivotal role of GAP in shaping a sustainable future for global agriculture. Bibliometric tools such as VOSviewer and Biblioshiny were employed to synthesize and visualize key contributions to GAP research. A total of 1,732 publications were analyzed to identify leading authors, major contributing countries, and significant thematic trends. The results reveal an increasing focus on sustainable crop management, food safety regulations, and the integration of advanced technologies such as precision agriculture into GAP.
The findings also indicate that most existing evidence on GAP’s potential to minimize food processing waste is concentrated in developed regions. In contrast, developing regions face significant barriers, including limited financial resources, inadequate knowledge dissemination, and insufficient infrastructure, which hinder effective implementation. Addressing these socio-economic constraints is critical to achieving equitable and sustainable agricultural development worldwide.
A significant research gap persists in studies focusing on developing regions, where GAP could have the greatest impact. Additionally, socio-economic challenges—particularly those faced by smallholder farmers in adopting GAP—remain underexplored. The study emphasizes the need for further investigation into the long-term effects of GAP, especially in relation to climate change and global food security. This analysis identifies research gaps and emerging trends, providing a foundation for future studies and policy interventions aimed at promoting sustainable agricultural practices globally.
However, certain limitations exist, including reliance on the Scopus database, which excludes relevant studies indexed in other sources. Furthermore, the bias toward English-language publications may obscure valuable research conducted in non-English-speaking regions, potentially overlooking important global patterns.

Traditional construction practices often face problems due to poor communication and coordination among project stakeholders. These issues frequently lead to inefficient use of resources, lower overall building performance throughout its lifecycle, increased material waste, and repeated delays and cost overruns (Pinto & Mantel, 1990; Shahhosseini et al., 2018). Because of these challenges, the construction industry is considered one of the major contributors to environmental impact worldwide. It is responsible for nearly 40% of global energy consumption and about 34–39% of total carbon dioxide emissions. In addition, construction and demolition activities generate roughly 25–30% of the world’s total solid waste (Azhar et al., 2013; Fazeli et al., 2022).
Although sustainable construction practices and Building Sustainability Assessment (BSA) frameworks have been introduced to reduce these impacts, their use in real projects is still not widespread. One of the main reasons is the heavy dependence on manual data handling and repeated calculations at different stages of a project. This process limits real-time interaction with design models and reduces the effectiveness of sustainability evaluations, especially during the early design phase (Wong & Zhou, 2015; Kouka et al., 2024).
Building Information Modeling (BIM) has gained recognition as a powerful digital tool for advancing sustainable and low-carbon footprint. It allows the creation of a comprehensive 3D model that brings together architectural, structural, and MEP components within a unified system. This integrated platform supports sustainability efforts by enabling accurate quantity estimation and dependable energy performance evaluation (Azhar et al., 2011; Ferreira et al., 2013).
The present study investigates the potential of BIM to support optimization objectives in building projects. It proposes a structured BIM-based approach specifically designed for earthwork management and cut-and-fill optimization. The main aim is to reduce material waste and improve sustainability performance during the construction stage. By focusing on earthwork processes, the study addresses a significant yet relatively underexplored area within sustainable construction research.

Impulse control disorders (ICDs) are a group of psychiatric conditions characterized by the inability to resist urges, leading to behaviors that may be harmful to oneself or others. Impulsive behavior is largely controlled by the prefrontal cortex (PFC), a crucial part of the brain responsible for executive functions like decision-making, inhibitory control, and emotional regulation. The dysfunction of the prefrontal cortex is the focus of this study, which looks at how impulse control disorders develop and persist. Neurobiological evidence suggests that impairments in specific subregions of the PFC, including the dorsolateral prefrontal cortex, orbitofrontal cortex, and ventromedial prefrontal cortex, are associated with reduced cognitive control, poor judgment, and heightened reward sensitivity. The balance between top-down regulatory mechanisms and bottom-up emotional and reward-driven processes is disrupted by these dysfunctions, which are frequently mediated by subcortical structures like the amygdala and nucleus accumbens. Functional neuroimaging studies show that people with ICDs have less activity and less connectivity in their prefrontal networks. This makes them less able to control their impulses and makes them more likely to do risky things. Additionally, PFC dysfunction and impulsivity are exacerbated by neurotransmitter imbalances, particularly those involving dopamine and serotonin. Understanding the neurocognitive mechanisms underlying prefrontal cortex dysfunction provides valuable insights for developing targeted therapeutic interventions, including cognitive-behavioral therapy and pharmacological treatments. This study highlights the importance of the PFC in regulating behavior and underscores its critical role in impulse control disorders.

Azadirachta indica A. Juss. (Meliaceae), widely known as Neem in India, is often referred to as “nature’s pharmacy” because of its extensive range of medicinal benefits. It is one of the two species belonging to the genus Azadirachta and is indigenous to India and Burma, thriving in tropical and subtropical climates. The present study focuses on the standardization of Azadirachta indica leaves collected from the Garhwal Himalayas. Various parameters, as per WHO guidelines, were evaluated, including total ash value, acid-insoluble ash, water and alcohol extractive values, moisture content (loss on drying), pH, volatile constituents, heavy metal analysis, phytochemical screening, chromatographic and spectroscopic profiling, as well as macroscopic and microscopic characteristics. Additionally, quantitative estimation of the marker compound was carried out using HPTLC analysis with azadirachtin as the reference standard.

The main goal of this study is to try an Internet of Things (IoT) connected Cyber Physical System (CPS). The system needs to be able to work in real time and adjust devices as needed. A simple monitor was used to find out the temperature and humidity. It was sent from these devices to a small computer and then to the cloud where it could be seen in real time. We got 5,000 results over the course of seven days. Based on our tests, the method was right 96.4 percent of the time. It answered in about 1.8 seconds which is fast enough for simple jobs. We also tried the system in a number of different internet settings to see how well it worked when the network was slow or unstable. The CPS model works well and can be used in real life in places like smart houses and small factories.

Sexually transmitted diseases (STDs), also known as sexually transmitted infections (STIs), remain a significant global public health challenge, affecting millions of individuals each year. These infections are primarily transmitted through unprotected sexual contact, including vaginal, anal, and oral intercourse, and can also be spread via blood transfusion, shared needles, and from mother to child during pregnancy, childbirth or breastfeeding.
Common sexually transmitted diseases (STDs) include bacterial illnesses like syphilis, gonorrhea and Chlamydia,viral infections like HIV, HPV, HSV and parasitic infections like trichomoniasis.The global burden of STDs continues to rise due to factors such as lack of awareness, social stigma, inadequate healthcare access, and risky sexual behaviors. Adolescents and young adults are particularly vulnerable due to insufficient sexual education and higher rates of multiple sexual partners. Many STDs are asymptomatic, especially in early stages, leading to delayed diagnosis and increased transmission rates.
If left untreated, these infections can result in severe complications, including infertility, pelvic inflammatory disease, ectopic pregnancy, neonatal infections, and increased risk of acquiring or transmitting HIV.Early diagnosis and effective treatment are crucial for controlling the spread of STDs.
Bacterial infections are generally curable with appropriate antibiotic therapy, where as viral infections are usually manageable but not curable, requiring long-term antiviral treatment to control symptoms and reduce transmission risk. Preventive strategies play a key role in reducing the incidence of STDs and include consistent and correct use of condoms, regular screening and testing, vaccination (such as HPV and hepatitis B vaccines), public health education, and behavioral interventions.
Recent advancements in diagnostic technologies, such as nucleic acid amplification tests (NAATs), have improved the accuracy and speed of STD detection. Furthermore, global health initiatives and awareness programs have contributed to increased screening and early intervention. However, challenges remain in addressing antibiotic resistance, particularly in gonorrhea, and ensuring equitable access to prevention and treatment services across different populations.

Coronavirus disease 2019 (COVID-19) triggered unprecedented global scientific collaboration and innovation across healthcare, biotechnology, and digital technologies. Rapid development of vaccines, diagnostic tools, therapeutic agents, and digital health solutions transformed pandemic management. Emerging technologies such as artificial intelligence (AI), CRISPR-based diagnostics, telemedicine, and mRNA vaccine platforms played significant roles in controlling the outbreak. This review summarizes major innovations introduced during the COVID-19 pandemic, their mechanisms, applications, and future prospects. These advancements not only helped in managing COVID-19 but also reshaped global preparedness for future pandemics.

In the contemporary competitive marketplace, pharmaceutical firms have recognized that enhancing supply chain efficiency is essential for sustaining competitive advantage. Key managerial decisions involve forecasting, planning, procurement, financing, inventory management, and marketing strategies, all of which must be coordinated and balanced to achieve organizational objectives. This study is grounded in a comprehensive literature review, expert opinion elicitation, and qualitative system dynamics modeling; therefore, a triangulation research approach has been adopted.
The primary objective of this research is to investigate the challenges within the pharmaceutical supply chain (PSC) and to analyze the dynamic behavior of critical variables influencing its performance. Additionally, the study proposes strategic interventions to address these challenges. Initially, a series of semi-structured interviews with experienced supply chain professionals were conducted to identify major issues. The findings reveal that demand forecasting inaccuracies, extended lead times, suboptimal inventory levels, and elevated supply chain costs are among the most significant concerns.
Subsequently, a qualitative system dynamics approach is employed to illustrate the interdependencies among variables affecting these challenges. Finally, three strategic recommendations are proposed: fostering collaborative partnerships with suppliers, investing in advanced technologies, and implementing robust information technology (IT) systems. Overall, the findings provide supply chain managers with a holistic perspective for informed decision-making, emphasizing the importance of feedback mechanisms and their long-term impact on organizational performance and strategic goals.

Black pepper (Piper nigrum) possesses numerous health benefits and is widely used in daily life. Its most common application is as a culinary spice due to its characteristic pungent flavor. Black pepper is among the oldest known spices and is extensively utilized in cooking as well as in the healthcare sector, particularly in traditional medicine, where it is regarded as the “king of spices.”
The information used in this study was collected from online databases such as Google Scholar, ResearchGate, ScienceDirect, as well as books on black pepper and reputable scientific journals published between 2000 and 2020. Traditional medicine utilizes natural plant resources, including roots, stems, branches, leaves, flowers, and fruits, which are relatively easy to obtain. In addition to having minimal side effects, these natural remedies can be used for prolonged periods.
The primary bioactive constituents of black pepper, mainly piperine and essential oils, exhibit a wide range of pharmacological activities, including antioxidant, antifungal, antimicrobial, and antiepileptic effects, as well as libido enhancement and anti-diarrheal properties, among others. Based on these properties, extensive research has been conducted on the phytochemical composition and pharmacological activities of black pepper.

Maintaining a healthy and balanced diet is increasingly difficult in modern, fast-paced lifestyles. Consumers frequently struggle to select suitable meals because traditional food service environments lack personalized, health-focused guidance. This paper presents a machine learning-driven health-aware food recommendation system designed to provide personalized meal suggestions based on individual physiological data. By capturing basic user inputs such as age, gender, height, weight, and activity level the system calculates critical health indicators like Body Mass Index (BMI), Basal Metabolic Rate (BMR), and Total Daily Energy Expenditure (TDEE). A Random Forest classification model is then utilized to evaluate this health profile and predict the most optimal meal plan category for the user. The model demonstrates a high prediction accuracy, offering a robust alternative to static menu browsing. The resulting web application bridges the gap between convenience and nutritional awareness, empowering users to make data-driven dietary choices.

This paper is a critical assessment of the effectiveness of Kenyan national policies and efforts in supporting female entrepreneurs in accessing and succeeding in export markets. This study acknowledges the connection between economic growth and gender equality while noting that in many cases, women-owned business enterprises become disadvantaged by systemic factors when it comes to scaling up and involvement in global trade. The paper introduces concepts such as female entrepreneurship, export markets, and the role of policy frameworks, which sets the stage for the critical evaluation. Based on the use of an extensive literature review and governmental reports, the study establishes what is already known about gender-based trade issues in Kenya. It particularly explores policies such as the 30% Preferential Public Procurement Policy and the opportunities presented by regional agreements such as the African Continental Free Trade Area (AFCFTA). The findings reveal that while policies are present to ensure gender equality is promoted in trade, their implementation and actual impact in boosting the performance of female-owned export businesses are limited due to the presence of multiple methodological constraints and institutional barriers. The paper concludes by offering policy recommendations on how to resiliently equip and prepare Kenyan women entrepreneurs for global inclusion.

The pharmaceutical sector plays a vital role in advancing healthcare outcomes, significantly improving overall quality of life. Its expansion has been driven by growing demands associated with the increasing incidence of chronic and age-related illnesses, along with evolving medical practices. This growth has required the adoption of innovative and often sophisticated marketing approaches to influence prescribing patterns among physicians. The pharmaceutical industry operates under strict regulations, particularly concerning promotional activities, where direct advertising to consumers is restricted. Consequently, pharmaceutical companies focus their marketing efforts on physicians, who serve as key decision-makers in determining patient treatment.
These promotional strategies aim to build professional relationships, communicate product-related information, and ultimately encourage the prescription of specific medications. Such activities target physicians from the early stages of their medical education and continue throughout their professional careers. These approaches are designed not only to strengthen relationships but also to shape prescribing behaviour, with the primary objective of enhancing sales performance.
However, the impact of these marketing practices has raised concerns regarding the appropriateness of prescribing, the standard of medications used, and the risk of conflicts of interest. Maintaining a balance between commercial promotion and the ethical responsibility of prescribing cost-effective and rational medicines remains a significant challenge. In countries such as Nepal, additional concerns exist regarding the quality of domestically manufactured pharmaceuticals and the inclination towards multinational brands.
This study seeks to examine the diverse marketing strategies adopted by pharmaceutical companies between 2019 and 2023, with particular emphasis on their influence on physicians’ prescribing decisions. Major promotional techniques include the distribution of free drug samples, sponsorship of continuing medical education programs, and direct engagement through medical representatives (MRs). The findings suggest that promotional activities, especially those involving personal interaction with MRs, have a substantial impact on prescribing behaviour. Nevertheless, this influence also raises ethical concerns, particularly regarding the potential for irrational prescribing and the effect of incentives and gifts on clinical decision-making.

Retrieval-Augmented Generation (RAG) represents a sophisticated paradigm shift in natural intelligence, synthesizing architectures retrieval mechanisms with generative architecture to optimize response precision within conversational agents. While conventional chatbots are often constrained by the static nature of pre-trained parameters frequently resulting in chronological lapses or factual inconsistencies the RAG framework dynamically extracts pertinent schemata from externalized knowledge repositories. By anchoring generative outputs in this retrieved context, the system drastically mitigates stochastic “hallucinations” and reinforces empirical accuracy. This treatise delineates the architectural intricacies, operational workflows, and systemic advantages of RAG-integrated models, while concurrently evaluating their deployment trajectories and evolving technical horizons.

This paper presents a practical model for measuring and analyzing two critical parameters in network communications, namely latency and jitter, through the use of the Python programming language in conjunction with UDP socket mechanisms. The objective of this study is to develop a simple, low-cost experimental system that can accurately reflect transmission characteristics in a Wi-Fi LAN environment, while also effectively supporting education and research in the field of telecommunications engineering.
The system is designed based on a client–server architecture, in which the sender transmits UTF-8 encoded textual data along with timestamps and SHA-256 hash values to ensure data integrity. The receiver, on the other hand, captures the transmitted packets, verifies their integrity, and computes latency and jitter based on the differences between sending and receiving times. Data transmission is repeated multiple times to collect a sufficiently large dataset for statistical analysis and evaluation of channel stability. Supporting tools such as Wireshark and the matplotlib library are integrated to visualize and validate the measurement results.
Experimental results demonstrate that the proposed system is capable of effectively measuring latency and jitter under typical Wi-Fi network conditions. Furthermore, it reflects the impact of factors such as network load, signal interference, and device configuration on transmission performance. In addition, the integration of data integrity verification enhances the reliability of the system. The proposed model is not only valuable for practical teaching purposes but can also be extended to more advanced research areas such as network optimization, time synchronization, and real-time communication.
The novelty of this study lies in proposing a simple yet effective experimental model capable of reproducing key Quality of Service (QoS) characteristics in UDP/IP networks, making it particularly suitable for educational applications.

The extensive understanding of the link between Intra-Regional trade and sustainable development has been a primary analytical economic policy tool, exploited over the years. This study provides an extensive understanding of Intra-Regional trade and sustainable development in the CEMAC bloc countries using a panel data over a period of over 28 years (1994 to 2021 inclusive). Data was collected from the World Development Indicators, International Monetary Fund Directory of Trade, Centre D’études Prospectives et D’Informations Internationales, and the Worldwide Governance Indicators. The ex post facto research design was used. The method adopted in this study is the Poisson Pseudo Maximum Likelihood (PPML) with a High-Dimensional Fixed Effect (HDFE) estimation technique to determine the coefficients of the variables stated in the model. The main finding of this study reveals that intra-regional trade in CEMAC bloc countries is significantly influence positively by sustainable development, economic size, population and when there are dominance in cross-border trade, and proximity effects. Based on the findings, it is therefore recommended that policy makers in CEMAC bloc countries should introduce economic policies and developmental strategies that provide incentives for businesses to engage in sustainable practices as part of their trade operations. Also, policies should be geared towards harnessing population potentials, formalizing cross-border trades, and investing in transport and industrial infrastructure to enhance proximity effects and deepening intra-regional trade integration.

Mental health issues such as stress, anxiety, depression, and emotional distress have become increasingly common in today’s fast-paced world. Factors such as academic pressure, work stress, and social isolation often contribute to these challenges. However, many people hesitate to seek professional help due to social stigma, lack of awareness, or limited access to mental health services. To address this gap, this paper introduces AURA AI, an intelligent mental health chatbot designed to offer early emotional support through conversational interaction.
The system utilizes Natural Language Processing (NLP) and machine learning techniques to understand user messages and detect emotional intent. A custom intent classification model based on TF-IDF vectorization and Logistic Regression is used to categorize user inputs into mental health–related intents such as stress, sadness, anxiety, or general conversation. The chatbot is developed using Python and Flask, while the user interface is built with HTML, CSS, and JavaScript to allow smooth, real-time communication with users.
In addition, the platform can integrate external AI services to generate empathetic responses and provide helpful mental wellness suggestions. By making emotional support more accessible and encouraging users to express their feelings, AURA AI aims to serve as an initial support system that can guide individuals toward better mental well-being and, when necessary, motivate them to seek professional help.

This paper proposes a low-cost educational model for measuring and analyzing two key Quality of Service (QoS) parameters—latency and jitter—in Wi-Fi networks using Python and UDP socket communication. The model is implemented through a simple client–server architecture, where the sender transmits UTF-8 encoded data packets embedded with timestamps and SHA-256 hash values to ensure data integrity, while the receiver captures packets, verifies their correctness, and computes latency and jitter based on transmission time differences. To ensure statistical reliability, data exchange is repeated multiple times under various network conditions, and the collected results are analyzed and visualized using tools such as Wireshark and the matplotlib library. Experimental results show that the proposed system effectively reflects real-world Wi-Fi transmission characteristics, including the influence of network load, signal interference, and device configuration on performance. With its simplicity, low implementation cost, and integration of integrity verification, the model serves as a practical platform for teaching and learning in telecommunications and networking courses, while also offering potential for extension to advanced topics such as network optimization, time synchronization, and real-time communication systems.

Household savings are essential for India's capital formation, yet a significant portion of these savings remains in low-return assets like cash, gold, and fixed deposits. This research explores how financial literacy can help households redirect their savings into formal and productive investment opportunities, thus contributing to India’s long-term goal of Viksit Bharat 2047. Employing a mixed-methods strategy that combines primary survey data with insights from existing literature, the study examines the impact of financial awareness on saving habits, investment decisions, and confidence in financial institutions.

The statistical results, including outcomes from paired t-tests, suggest that financially literate households exhibit more efficient saving habits, engage in a wider array of financial products, and demonstrate increased confidence when interacting with formal financial systems. Moreover, engaging in financial literacy initiatives and being informed about government-backed financial programs greatly motivate households to move away from informal saving methods and participate more proactively in capital formation. The research emphasizes that financial literacy is not only a personal skill but a national necessity, crucial for inclusive growth, financial stability, and sustainable economic progress. Serving
as a catalyst, financial literacy is essential in converting household savings into productive investments that align with the vision of Viksit Bharat 2047.

Background: Basketball is a multidimensional sport demanding high levels of technical proficiency. Skill performance variables such as dribbling, passing accuracy, and shooting accuracy are fundamental to competitive success. Sports imagery training has emerged as a potent psychological tool to augment physical skill acquisition. This study examines the effect of specific skill training with and without sports imagery on dribbling, passing accuracy, and shooting accuracy among college men basketball players.
Objectives: To determine whether the combination of specific skill training and sports imagery training produces significantly greater improvements in dribbling, passing accuracy, and shooting accuracy compared to skill training alone and a control condition.
Methods: Sixty male college basketball players (aged 18–24) were randomly assigned to three groups: Group A-Specific Skill Training (n=20), Group B-Specific Skill Training with Sports Imagery Training (n=20), and Group C- Control (n=20). The intervention lasted 12 weeks with five sessions per week. Pre-test and post-test data were collected for dribbling speed (seconds), passing accuracy (%), and shooting accuracy (/20). Data were analyzed using one-way ANOVA followed by Scheffé post hoc test at p<0.05 level of significance.
Results: Significant improvements were observed in all three skill variables across Groups A and B. Group B (Skill + Imagery) demonstrated superior gains compared to Group A and the Control Group (p<0.001). The Scheffé post hoc test confirmed significant pairwise differences between all group combinations for dribbling, passing, and shooting accuracy. The Control Group showed no significant improvement.
Conclusion: Integrating sports imagery training with specific skill training significantly enhances dribbling speed, passing accuracy, and shooting accuracy in college men basketball players over a 12-week intervention. Coaches and practitioners should consider structured imagery protocols as an essential complement to physical skill development.

The present study aimed to examine and compare the impact of yoga and physical exercise on selected mental health parameters among university students. A total of 90 participants (aged 18–25 years) were randomly assigned to three groups yoga, physical exercise, and control. The yoga and exercise groups participated in 60-minute sessions, three times per week, for eight weeks, while the control group continued their usual routines. Standardized instruments including the Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), Perceived Stress Scale (PSS-10), and Pittsburgh Sleep Quality Index (PSQI) were administered before and after the intervention. Data were analyzed using paired t-tests (within-group changes) and independent sample t-tests (between-group comparisons). Findings revealed significant reductions in anxiety, depression, and perceived stress in both intervention groups compared to controls, with yoga participants showing greater improvements in stress reduction and sleep quality. These results highlight the effectiveness of mind body practices and physical exercise in promoting mental well-being, suggesting that incorporating such programs into university settings may be an effective strategy for enhancing student health and resilience.

In the 20th century, industrial revolution takes place. After that industries are developed on the large scale. Products are required to produce by the mass production techniques to reduce the cost. For that purpose different techniques are developed. As many processes have to take place simultaneously, there is need for the help in working. For doing different work we need help. Special purpose machines are developed for this. In the perforation of different work for that machine is defined as a device or machine which works according to our order. Our order should be completed in time with precision so the term called Machine comes into play. “Machine – A servant play’s important Role in it.” Automation has become key to unlock the manufacturing secrets and the way for determining finest possibility of processing operation in the industries, that will lead to achieve high throughput, accuracy and repeatability in production. Automation leads to perform almost all associate activities in manufacturing industries. While overcoming all parameter of manufacturing, automation has provided advance solution for performing nearly all operation like Milling, Drilling, etc.

प्रस्तुत शोध “बौद्ध एवं वैदिक परंपरा में नैतिकता और कर्म: अभिधम्मत्थसङ्गहो एवं भगवद्गीता के संदर्भ में” इन दोनों महान दार्शनिक परंपराओं के नैतिक एवं कर्म-सिद्धांतों का तुलनात्मक अध्ययन प्रस्तुत करता है। बौद्ध परंपरा में अभिधम्मत्थसङ्गहो के आधार पर कर्म को मुख्यतः मानसिक चेतना (चित्त) और उसकी प्रवृत्तियों से उत्पन्न माना गया है, जहाँ “चेतना ही कर्म है” का सिद्धांत केंद्रीय भूमिका निभाता है। इस दृष्टिकोण में नैतिकता का आधार चित्त की शुद्धि, करुणा, मैत्री तथा अहिंसा जैसे मानवीय गुणों में निहित है। इसके विपरीत, वैदिक परंपरा के प्रमुख ग्रंथ भगवद्गीता में कर्म को कर्तव्य (धर्म) के रूप में देखा गया है, जिसमें निष्काम कर्म (फल की इच्छा के बिना कर्म करना) को सर्वोच्च आदर्श माना गया है। गीता में नैतिकता का आधार धर्मपालन, आत्मसंयम, समत्व और ईश्वर के प्रति समर्पण में निहित है।

This study explores the perspectives of Chennai residents regarding corruption and its pervasive impact on daily life. By collecting primary data through surveys from 76 respondents, the research examines how corruption affects various sectors, including government services, education, and employment. The findings reveal that a significant number of respondents perceive corruption as a widespread issue, primarily driven by bureaucratic inefficiencies and political interference. The study highlights the psychological toll of corruption on individuals and the community's growing awareness of the need for accountability. Recommendations such as developing a corruption reporting app, forming community watchdog groups, and promoting civic education are proposed to foster a culture of integrity and transparency. Ultimately, this research contributes valuable insights into the public’s understanding of corruption in Chennai and the need for effective anti-corruption measures.

The empowerment of Dalit women is a crucial aspect of achieving inclusive and sustainable development in India. Dalit women face multiple forms of marginalization due to caste, gender, and economic inequalities. This study examines their socio-economic conditions, identifies barriers to empowerment, and evaluates the effectiveness of policy interventions. The research is based on secondary data and adopts a descriptive and analytical approach. The findings reveal that despite various government and institutional initiatives, significant challenges persist in education, employment, and social participation. The study proposes a multidimensional approach focusing on education, economic independence, legal awareness, and community participation to ensure sustainable empowerment.

A cloud-based platform called the Charity Crowdfunding Application using AWS was created to link contributors with several approved charities via a safe, centralised mechanism. This application's main goal is to offer a transparent and trustworthy environment where organisations can sign up, finish onboarding, and accept donations straight from users. Each of the three login categories in the system—Admin, Charity, and User—has distinct duties and access. Charities must upload necessary documents, including evidence of identification, proof of address, and organisational details, during the onboarding process. These documents are then put in a pending condition for verification. After reviewing these documents, the administrator decides whether to approve or reject the charity. If authorised, a RuPay sub-account is made to allow users to safely and directly send donations to the charity's account. After being accepted, each charity is given a public profile page with a "Donate Now" option and a shareable link, making it simple for donors to visit and support certified organisations. Donors can safely log in and make contributions using the User module, while the admin module handles email alerts, approvals, and verification. Because Amazon Web Services' cloud architecture was used in its construction, the platform offers scalability, high availability, safe data storage, and dependable performance. This system offers a safe, effective, and scalable alternative for contemporary charity fundraising while also improving transparency and preventing fraudulent registrations.

Well before and ShahRukh Khan and Gauri, Zaheer Khan and Sagarika Ghatge, there was Mansur Ali Khan Pataudi and Sharmila Tagore. Muslim men who married Hindu ladies and the general population was excited by their relationships. The astonishing Pataudis and Khans were very much ensured by VIP status and social force, and nobody blamed them for Love jihad. Yet, the story is altogether different for normal residents who decide to wed external their religion. They stay helpless before the state and vigilante crews since they aren't notable. Akhila Ashokan or Hadiya wedded Shafin Jahan and confronted long stretches of torture under the watchful eye of the Supreme Court at last decided in 2018 that 'the option to wed an individual is fundamental to Article 21." regardless of this, today couples who break the strict obstruction are shadowed by fears of submitting 'love jihad'.

This study explored the instructional supervision practices and experiences of elementary teachers at Saint Louis College, Basic Education Schools, Elementary Department. The goal of this study is to develop a research-based Supervisory-Primer. Anchored in a descriptive qualitative research design, the study employed semi-structured interview and focus group discussions involving fifteen participants selected through criterion sampling, including experienced teachers and academic coordinators during the academic year 2025 – 2026. Thematic analysis revealed six major themes: (1) supervision as supportive and developmental, (2) collaborative and trust-based supervision, (3) structured and differentiated observation practices, (4) focus on student learning outcomes, (5) post-observation feedback as reflective dialogue, and (6) challenges in implementation, including time constraints, administrative workload, and resistance to change. Findings indicate that instructional supervision is most effective when it is continuous, reflective, mentoring-oriented, and grounded in a professional partnership rather than compliance-based evaluation. While teachers valued formative feedback, coaching, and collaborative goal-setting, systemic barriers such as time-poverty and inconsistent supervisory practices limited the depth of professional engagement. This study concludes that a sustained, teacher-centred developmental process. In response, A supervisory Primer was developed to institutionalize structured observation cycles, mentoring programs, professional learning communities, data-driven feedback, and collaborative decision-making aligned with national education policies and professional standards. The findings contribute to the growing body of literature advocating developmental supervision models that enhance teacher efficacy, institutional quality, and student learning outcomes. The proposed primer offers a practical framework for strengthening instructional leadership and fostering a culture of continuous professional growth within elementary settings.

Marital rape remains one of the most contested and under-recognized forms of sexual violence in India. Despite progressive judicial pronouncements and international human rights obligations, the Indian Penal Code continues to exempt husbands from prosecution for non-consensual sexual acts with their wives. This research paper critically examines the legal status of marital rape in India, analyzes its constitutional validity, and evaluates the socio-legal consequences of non-criminalization. Drawing upon feminist legal theories, comparative jurisprudence, and international human rights frameworks, this paper argues that the marital rape exemption violates fundamental constitutional guarantees of equality, dignity, and bodily autonomy. The paper concludes with comprehensive recommendations for legal reform and institutional change.

This study examined the impact of National Universities Commission (NUC) accreditation indices on students' educational outcomes in public universities within the Federal Capital Territory (FCT), Abuja, Nigeria. Specifically, the research investigated the relationship between curriculum adequacy and students' educational outcomes, as well as the extent to which library resource adequacy influences students' academic achievement. The study adopted an ex-post-facto research design using the correlational method. The population comprised 613 junior and senior academic staff of the University of Abuja, from which a sample of 184 respondents (30%) was selected through stratified random sampling based on academic rank. Two self-developed instruments were employed for data collection: the Accreditation Questionnaire (AQ) containing 20 items structured on a four-point scale, and the Students' Educational Outcome Checklist (SEOC), which captured students' academic achievement using cumulative grade point average (CGPA). The instruments were validated by experts in Educational Management and Foundations, and reliability was established using Cronbach Alpha method, yielding a coefficient of 0.78. Data were analyzed using coefficient of determination for research questions and Pearson Product Moment Correlation Statistics for hypotheses testing at 0.05 level of significance. Findings revealed that curriculum adequacy has a significant positive relationship with students' educational outcomes (r = 0.566, r² = 0.320, p = 0.000), accounting for 32% variance in academic outcomes. Similarly, library resource adequacy significantly relates to students' academic outcomes (r = 0.525, r² = 0.275, p = 0.000), explaining 27.5% of the variance. Both null hypotheses were rejected, confirming that curriculum adequacy and library resource adequacy are significantly related to students' academic achievement. The study concluded that NUC accreditation indices are not merely procedural requirements but critical quality assurance mechanisms that substantially influence students' learning experiences and academic performance. It was recommended that university management should ensure periodic curriculum review and updating, prioritize adequate funding for library development, and that the NUC should strengthen post-accreditation monitoring mechanisms to ensure sustained compliance with established standards.

Because of its accessibility and perceived safety, paracetamol (acetaminophen) continues to be one of the most commonly used analgesic and antipyretic medications worldwide. Overdosing on paracetamol remains a major cause of acute liver failure globally, despite its extensive therapeutic use. According to new research, older adults are a clinically vulnerable population where toxicity outcomes may be impacted by pharmacokinetic changes, comorbidities, polypharmacy, and delayed presentation. This review examines clinical risk factors, pharmacological mechanisms, epidemiological trends, and treatment approaches for paracetamol poisoning in people 65 years of age and older. The interaction of comorbid conditions with hepatotoxic pathways, repeated supratherapeutic ingestion, and age-related metabolic changes are all given special attention. To clarify the factors influencing toxicity in this population, recent research from toxicology registries, population cohorts, and clinical pharmacology studies is critically assessed. Comprehending these trends is crucial for enhancing clinical risk assessment, refining therapeutic approaches, and directing preventative measures for senior citizens.

Biometric authentication has become one of the most reliable techniques for user identification in modern digital systems. Among various biometric methods, fingerprint authentication is widely used due to its uniqueness, reliability, and ease of acquisition. However, storing fingerprint templates in cloud environments introduces significant privacy and security challenges. Unauthorized access, data leakage, and biometric identity theft are major concerns when biometric data is outsourced to third-party cloud servers. This research proposes an efficient and privacy-preserving fingerprint authentication scheme designed for secure cloud storage environments. The proposed system ensures that fingerprint templates are encrypted before storage and authentication is performed without exposing sensitive biometric data. The system integrates feature extraction, template protection, and secure matching algorithms to ensure confidentiality and integrity. Experimental evaluation shows that the proposed approach improves authentication accuracy while maintaining strong security and privacy protection. The system can be effectively deployed in cloud-based authentication services, enterprise security systems, and online applications requiring secure identity verification.

Workplace stress has emerged as one of the most significant challenges in modern organizational life, particularly in fast-paced and competitive corporate environments. While moderate stress can enhance performance, excessive and prolonged stress negatively affects employee well-being, productivity, and organizational outcomes. This study explores the causes, consequences, and management of stress in the workplace through a conceptual and analytical approach. It highlights the role of organizational culture, leadership, and individual coping mechanisms in managing stress effectively. The paper argues that stress management is not merely an individual responsibility but a shared organizational priority that requires systemic intervention.

Background: High-altitude training (HAT) has emerged as a critical performance enhancement strategy for endurance athletes, with the "live high-train low" (LHTL) approach gaining widespread acceptance in sports science. Understanding research trends in this domain is essential for identifying knowledge gaps and future research directions.
Objective: This study conducts a comprehensive bibliometric analysis to map research trends, identify influential publications, examine collaboration networks, and explore thematic patterns in high-altitude training and endurance performance literature from 2011 to 2025.
Methods: A systematic Scopus search used keywords on high-altitude training and endurance performance, following PRISMA. It included 136 open-access English articles from 2011- 2025. VOSviewer visualized citations, keywords, author networks, and publication locations.
Results: Utilizing advanced visualization tools to analyze influential authors, organizations, and keywords, the paper highlights the leading publications and essential research clusters that are shaping the future of endurance performance research.
Conclusion: This bibliometric analysis shows ongoing growth and rising interdisciplinary interest in high-altitude training research, dominated by countries with advanced sports science. It highlights key research themes and lesser-explored areas for future study, offering guidance for researchers, coaches, and athletes aiming to improve endurance with altitude training.

This paper discusses how artificial intelligence is affecting Business-to-Business (B2B) services. In this system, there is integration of AI-based chatbot services, user attention analytics, voice-based accessibility (dictation mode), and automated communication tools, all backed by a scalable Supabase backend system. This paper discusses the results of various studies done previously about AI-based services, their challenges, and various trends related to adding smart features to client sites. Lastly, there is a discussion about future research directions, such as personalization, authentication, and handling big data.

This study examines the saving and investment behaviour of Generation Z in Hyderabad, focusing on their financial awareness, attitudes, preferences, and decision-making patterns in a rapidly digitalised environment. Generation Z, born between 1997 and 2012, has grown up with smartphones, fintech applications, and easy access to online financial information, which significantly influences their financial choices. The research adopts a quantitative approach using a structured questionnaire administered to 100 respondents within a university. Data were analysed using percentage analysis, frequency distribution, t-test, and chi-square test. The findings indicate that while Gen Z demonstrates a positive inclination towards saving, especially for future security and emergencies, traditional options such as bank deposits remain widely preferred for safety. At the same time, there is increasing interest in modern investment avenues like mutual funds, equities, and digital platforms. However, financial literacy gaps, peer influence, and reliance on social media affect rational decision-making. The study highlights the need for targeted financial education and awareness programs to enhance informed investment behaviour. Overall, the research emphasises the importance of developing disciplined saving habits and responsible investment practices among Gen Z for long-term financial stability and economic growth. These insights support policymakers and educators in designing effective interventions.

Background: Breastfeeding is a cornerstone of infant health and survival, with WHO recommending exclusive breastfeeding (EBF) up to 6 months followed by continued breastfeeding with appropriate complementary feeding. [1][2] In India, EBF rates remain suboptimal despite national-level policies and programs. [1][3]

Objective: To review breastfeeding practices, determinants, and alternative feeding methods in the Indian population, with emphasis on implications for clinical practice and health-system policy. [1][4][3]

Methods: Narrative review of nationally representative surveys (NFHS-4 and NFHS-5), Indian Infant and Young Child Feeding (IYCF) guidelines, and peer-reviewed studies on breastfeeding and alternative feeding methods published between 2015 and 2025. [1][4][5][6]

Results: In India, the proportion of infants exclusively breastfed up to 6 months increased from 31.3% (NFHS-4) to 43% (NFHS-5), but over half of infants still do not meet the
WHO-recommended EBF duration. [1][3] Early initiation of breastfeeding (within 1 hour) has improved but remains below target in many states. [4][3] Major barriers include maternal employment, lack of support, misinformation, and increasing use of bottle-feeding and commercial formula. [7][3] When breastfeeding is not medically or practically feasible, acceptable alternatives include expressed breastmilk (ebm) via cup/bowl or bottle, pasteurized donor human milk (PDHM), and, when strictly necessary, commercial infant formula under paediatric guidance. [8] [9][5][6]

CONCLUSION: Strengthening facility-and community-based support for breastfeeding, scaling up human-milk banking, and regulating formula-marketing can improve optimal breastfeeding and safe alternative-feeding practices in India. [1][3][6]

The paper proposes a system based on machine learning for the prediction of household energy consumption based on data received from smart meters, marking an important milestone in the management of smart grids and green energy systems. The pattern of energy consumption is a complex issue to be interpreted based on the highly variable patterns of seasonal changes in weather, demographics of households, and other socio-economic factors. To address the purpose of energy consumption pattern analysis and forecasting, the proposed system utilizes the capabilities of high-performance regression models in the analysis and normalization of data received from smart energy meters. Supervised learning models of Random Forest Regression, Decision Tree Regression, and Multiple Linear Regression (MLR) have been utilized in the proposed system based on their capabilities in addressing non-linear relationships between weather patterns and energy consumption peaks. The performance of the proposed system has also been validated using R² scores and mean square error metrics, which are highly specialized in addressing the energy consumption patterns of London households. The performance of the system also indicated highly reliable predictive capabilities with an accuracy of 92%, mitigating errors in forecasting with ease. The proposed system has also been conceptualized in terms of its integration with a Flask-based web application and JSON-based local ledger system for the purpose of real-time monitoring of energy consumption and billing intelligence.

In modern defense operations, ensuring the health and safety of soldiers in extreme environments is a critical challenge. This project presents a Soldier Health Monitoring and Real-Time Location Tracking System using Internet of Things (IoT) technology. The system continuously monitors vital physiological parameters such as heart rate and oxygen saturation using the MAX30102 sensor, body temperature using the DS18B20 sensor, and tracks the soldier’s location using a GPS module. All sensors are interfaced with an ESP32 microcontroller, which processes the data and transmits it to a backend server via Wi-Fi. The system performs threshold-based analysis to classify the soldier’s condition as Normal, Warning, or Critical and generates alerts when abnormal conditions are detected. A web-based dashboard provides real-time visualization of health parameters and location tracking, enabling remote monitoring and quick decision-making. The system improves soldier safety, enhances emergency response, and demonstrates scalability by including simulated parameters such as blood pressure for future integration.

By making it possible to create dynamic, patient-specific virtual models that mimic medication response and illness development, digital twin technology represents an emerging paradigm in pharmacodynamics. The primary goal of pharmacodynamics is to comprehend how drug concentration and biological reaction are related, but prediction accuracy is frequently hampered by inter- individual variability.

Digital twins create computational copies that can forecast treatment outcomes by integrating multi-source biomedical data, such as physiological, molecular, and clinical factors. This method lowers attrition rates, increases safety profiling, facilitates virtual clinical trials, and improves dose optimization in drug development. Digital twins support precision medicine and more effective regulatory evaluation by facilitating simulation-based study of toxicity and efficacy before to actual delivery. Notwithstanding difficulties with data protection, infrastructure needs, validation, and ethical issues, the technology provides revolutionary promise for clinical treatments and pharmaceutical research. Digital twins are anticipated to play a key role in contemporary pharmacodynamic analysis and individualized treatment plans as computer modelling, systems pharmacology, and real-time health data integration develop.

Swami Vivekananda was a great thinker and reformer of India. Vivekananda pointed out that the defect of the present-day education is that it has no definite goal. He was against the contemporary educational system; it turned men into slaves. He emphasized that the aim of education should be man-making and character-building. His educational ideas have touched every aspect of Indian social life. Now-a-days, physical education has become a key issue of many social clubs and educational organizations. By realizing this situation, presenting this paper which aims to find the opinion of Swami Vivekananda about physical education in contemporary era. So, the major objectives have selected to identify the life philosophy of Vivekananda in present day of physical education and relevance of physical education in present day. Documentary analysis methodology has used for this study. This study will help to analyse and evaluate the importance of Vivekananda’s idea of physical education.

A new field called "neuromorphic computing" creates hardware and software that closely resembles the composition and operations of the human brain. Neuromorphic systems, in contrast to traditional von Neumann designs, combine memory and processing, enabling computation that is extremely parallel, energy-efficient, and adaptive. This technology allows for real-time learning and decision-making by utilising parallel architectures, event-driven computing, and spiking neural networks. Neuromorphic computing shows potential for increasing artificial intelligence towards human-like flexibility and efficiency with applications in robotics, the Internet of Things, autonomous systems, and healthcare.

Waste management has become a critical challenge due to speedy urbanization & advancing waste generation. Traditional waste bins rely on manual handling, which often leads to polluted conditions like overflowing garbage, foul odor, & inappropriate waste segregation. The Automatic Intelligent Waste Bin with Autonomous Area-Based Garbage Collection Using IoT is developed to give a smart & efficient solution to these problems.
The system uses an ESP32 microcontroller to process inputs from many sensors like ultrasonic & infrared sensors. These sensors detect the presence of a user & automatically open the lid using a motor-driven mechanism, allowing touch-free waste disposal. A robotic arm mechanism is incorporated to sort waste into wet & dry categories, enhancing waste segregation.
An OLED display illustrates real-time system status, & motor drivers control the movement of the robotic components. The system is powered by a rechargeable lithium-ion battery & uses Wi-Fi connectivity to send alerts to a mobile application when the bin is full. This project gives a hygienic, efficient, & affordable waste management solution suitable for smart cities, hospitals, campuses, & residential areas.

Background: Erectile Dysfunction (ED) is a multi-factorial disorder frequently linked to both physiological declines and psychological stressors, such as performance anxiety. In Homoeopathy, Lycopodium clavatum is traditionally indicated for "functional impotence" where physical power is deficient despite the presence of desire.Objective: To evaluate the clinical efficiency of individualized homoeopathic Lycopodium clavatum in the management of Erectile Dysfunction and its associated psychological and gastrointestinal concomitants.Methodology: A prospective clinical study (or case series) was conducted involving male subjects aged 25–65 years presenting with symptoms of ED. Patients were selected based on the specific Lycopodium profile: anticipatory anxiety, low self-confidence, and physical symptoms such as coldness of genitalia or premature ejaculation, often accompanied by digestive flatulence. Standardized tools like the International Index of Erectile Function (IIEF-5) were used to measure baseline and post-treatment scores.RESULTS: Clinical observations indicated a significant improvement in erectile rigidity and duration following the administration of Lycopodium in potencies ranging from 30C to 1M. Subjects reported not only an increase in sexual confidence but also a marked reduction in performance-related anxiety and associated dyspeptic symptoms. Data suggest that Lycopodium acts effectively on the neuro-endocrinal axis, particularly in cases where ED is secondary to psychological or digestive exhaustion.

The increasing demand for electrical energy and the depletion of conventional fossil fuels have created the need for renewable energy sources. Wind energy is one of the most promising clean energy sources available today. Highways experience continuous movement of vehicles that produce significant airflow due to vehicle motion. This airflow contains kinetic energy that is generally wasted in the environment. The present work focuses on the design and development of a Vertical Axis Wind Turbine (VAWT) system capable of capturing airflow generated by moving vehicles on highways. The turbine consists of blades mounted on a vertical shaft supported by bearings and connected to a DC generator through a gear mechanism. When vehicles pass near the turbine installation, the airflow rotates the turbine blades, producing mechanical energy which is converted into electrical energy using the generator. The generated electricity is stored in a rechargeable battery and monitored using electrical measuring instruments. The proposed system is simple, economical, and suitable for small-scale renewable energy applications such as highway street lighting and traffic monitoring systems.

This study examines the influence of social media advertising on consumer buying behaviour among undergraduate students in federal universities in South-South Nigeria. The research was motivated by the increasing use of social media platforms as marketing tools and the growing exposure of students to digital advertisements. Specifically, the study investigated the effects of exposure to advertisements, brand awareness, and trust on students’ purchasing decisions. A descriptive survey research design was adopted, with data collected from undergraduate students across selected federal universities using a structured questionnaire. The data were analyzed using mean, standard deviation, and regression analysis at a 0.05 level of significance. The findings revealed that exposure to social media advertising significantly influences students’ buying behaviour by increasing product awareness and engagement. Additionally, social media advertising was found to enhance brand awareness, which positively affects students’ preference for and selection of products. Trust in social media advertisements also emerged as a significant predictor of consumer buying behaviour, as students are more likely to purchase products they perceive as credible and reliable. The study concluded that social media advertising is a powerful determinant of consumer behaviour among undergraduate students, with exposure, brand awareness, and trust playing critical roles. It recommends that marketers design engaging and credible advertising content to effectively reach this demographic, while educational institutions should promote media literacy to help students make informed purchasing decisions. The study contributes to existing literature by providing empirical evidence from a developing economy context.

This study examined the influence of computer-based examination (CBE) on the academic performance of undergraduate students at the University of Uyo, Nigeria. The increasing integration of information and communication technology (ICT) in educational assessment has led to the widespread adoption of computer-based examinations as a replacement for traditional paper-based testing methods. However, concerns remain regarding how this transition affects students’ academic outcomes, particularly in developing countries where infrastructural and technical challenges persist. The study was anchored on the Technology Acceptance Model (TAM) and Cognitive Load Theory (CLT), which provided insights into students’ interaction with digital examination systems and the cognitive demands associated with their use. A descriptive survey research design was adopted, with a population of 30,000 undergraduate students, from which a sample size of 395 was selected using stratified and simple random sampling techniques. Data were collected using a structured questionnaire titled Computer-Based Examination and Academic Performance Questionnaire (CBEAPQ), validated by experts and tested for reliability using Cronbach Alpha (r = 0.85). Data analysis involved mean, standard deviation, and regression analysis at a 0.05 level of significance. Findings revealed that accessibility to computer facilities had a low but significant influence on academic performance, system reliability had a moderate and significant influence, while technology-related examination anxiety had a moderate negative influence on students’ academic performance. The study concluded that although CBE enhances efficiency, transparency, and objectivity in assessment, its effectiveness is constrained by infrastructural limitations and students’ psychological readiness. It was recommended that universities improve ICT infrastructure, ensure system reliability, and provide adequate training to reduce students’ anxiety and enhance performance.

The Niger Delta area faces ongoing difficulties because people cannot find safe drinking water while oil spills and domestic waste and sedimentation have polluted the Nun River. The research tested plantain peduncle activated carbon (PPAC) as an affordable adsorbent that improves water quality through its use in laboratory-scale filtration systems. The researchers collected water samples through grab sampling while they prepared PPAC by washing and drying and carbonizing at temperatures between 300 and 500 degrees Celsius and using H₃PO₄ for chemical activation and sieving. The filtration system used four layers which included gravel and sand and PPAC and cotton wool while the flow rate control system enabled better contact time between the water and the filters. The water quality tests measured pH and turbidity and colour and electrical conductivity and total dissolved solids and total suspended solids and iron (Fe²⁺) and biochemical oxygen demand and chemical oxygen demand and total coliforms. The results showed that PPAC filter decreased turbidity by 91% and colour by 77% and iron by 84% and TSS by 82% and microbial load by 93% which resulted in filtrate that met WHO and NSDWQ standards for most parameters. The research demonstrates that PPAC functions as sustainable and effective rural water treatment solution which provides economical treatment method through its use of regional agricultural waste. The research recommends that activated carbon needs regular replacement to sustain its operational ability while it supports further studies about regeneration techniques and heavy metal elimination.

This study investigated the adequacy of science-based teachers and its influence on students’ academic performance in science subjects in Secondary Schools in Yakurr Local Government Area of Cross River State, Nigeria. Guided by two research questions and corresponding hypotheses, a descriptive research design was adopted. A sample of 435 science students was drawn from Public Secondary Schools using cluster and simple random sampling techniques. Two instruments - Adequacy of Science-Based Teachers’ Questionnaire (ASBTQ) and Science Achievement Test (SAT) - were used for data collection. Data were analyzed using mean, standard deviation, and Analysis of Variance (ANOVA). Results revealed a significant influence of teachers’ competence in the use of instructional materials and teachers’ adequate knowledge of subject matter on students’ academic performance in science subjects. It was concluded that teacher adequacy - in terms of subject mastery and pedagogical competence - is vital for improving science achievement among students. The study recommended that the Ministry of Education should ensure the provision of adequate instructional materials, continuous teacher training, and regular supervision to enhance science teaching and learning outcomes in Secondary Schools.

Blockchain technology and cryptocurrencies have emerged as important elements of the global digital economy as a result of the quick digital transformation of financial systems. India has seen a rise in interest in blockchain applications and digital assets due to its growing financial ecosystem and digital infrastructure. Concurrently, financial market forecasting is making extensive use of artificial intelligence (AI) methods like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The theoretical relationship between cryptocurrencies, blockchain technology, and India's developing digital economy is examined in this paper, along with any potential ramifications for AI-based stock market forecasting models.The conceptual underpinnings of blockchain and cryptocurrencies, their contribution to financial innovation, Indian regulations, and the incorporation of macro-digital variables into deep learning models for stock price prediction are all covered in this study. The paper emphasizes how new financial data streams produced by blockchain-driven financial ecosystems might improve prediction models like RNN and LSTM. The study comes to the conclusion that the future of digital finance and stock market analytics in India would be significantly shaped by the convergence of blockchain technology, cryptocurrency markets, and AI-based financial predictions.

The integration of artificial intelligence (AI) into agricultural industries is transforming workforce management and sustainability practices globally. The Indian dairy industry, one of the largest contributors to rural employment and agricultural GDP, is increasingly adopting digital technologies to enhance productivity and environmental sustainability. This study examines how AI-enabled workforce management practices contribute to sustainable operations in the Indian dairy sector. By synthesizing recent literature on AI adoption, Sustainable Human Resource Management (SHRM), and digital transformation in dairy supply chains, the research identifies emerging practices such as AI-driven workforce analytics, predictive labor scheduling, automated dairy farm monitoring, and digital training systems. The study also explores the challenges faced by dairy cooperatives and private firms in integrating AI into HR management processes. Six research hypotheses are formulated and tested using regression and Structural Equation Modelling (SEM) frameworks. The findings suggest that AI-enabled workforce management significantly improves operational efficiency, enhances employee productivity, reduces environmental impacts, and supports sustainable dairy production. The paper contributes to the literature by linking AI technologies with sustainable HRM frameworks in the context of India's dairy ecosystem. Policy implications for dairy cooperatives, agritech startups, and policymakers are discussed.

The quick development of digital technologies and artificial intelligence (AI) has drastically changed consumer behavior and marketing strategies. In an AI-powered urban environment like Hyderabad, online marketing platforms play a crucial role in promoting sustainable consumption, particularly in the white goods sector such as refrigerators, washing machines, and air conditioners. This study investigates how online marketing tactics—backed by AI data, tailored ads, and digital customer interaction—help close the gap between sustainability knowledge and real eco-friendly white goods purchase behavior. In order to promote the use of eco-friendly and energy-efficient appliances, the study looks at consumer attitudes, the impact of digital advertising, and the function of e-commerce platforms.. The results show that sustainability-focused product information, AI-driven suggestions, and targeted internet marketing all have a big impact on customer choices and raise awareness of green products. The study emphasizes how crucial it is to combine AI technology with digital marketing tactics in order to support environmental objectives and encourage sustainable consumption habits in quickly evolving smart cities.

Artificial Intelligence (AI) has become an important driver of innovation in modern business by enabling organizations to analyse large volumes of consumer data and provide personalized services. In the retail sector, the growth of quick commerce platforms has transformed consumer purchasing behaviour by offering faster delivery and convenient digital shopping experiences. This study examines the role of artificial intelligence in influencing sustainable consumer behaviour within quick commerce platforms in India. The research adopts a descriptive design and is based entirely on secondary data collected from academic journals, industry reports, company publications, and credible online sources related to artificial intelligence, sustainability, and digital retail. The analysis indicates that AI-driven technologies improve operational efficiency, enhance customer engagement, and support better inventory and delivery management. These systems can also promote sustainable consumption practices by reducing wastage, improving resource utilization, and encouraging environmentally responsible purchasing decisions. The study concludes that the integration of artificial intelligence within quick commerce platforms can significantly influence sustainable consumer behaviour while improving the efficiency of digital retail operations.

In the present globalized world, the growth of organized crime has become a serious concern for all countries. Globalization has increased the movement of goods, services, people, and information across borders, which has created new opportunities not only for economic development but also for criminal activities. Organized crime groups have taken advantage of open markets, advanced technology, and weak international coordination to expand their operations beyond national boundaries. Activities such as drug trafficking, human trafficking, money laundering, and cybercrime have increased significantly due to globalization. This study focuses on understanding how globalization has influenced the growth and expansion of organized crime. It examines the changing nature of criminal networks, the role of technology, and the challenges faced by legal systems in controlling such crimes. The paper also discusses how differences in laws, lack of cooperation among countries, and corruption make it difficult to effectively tackle organized crime. In addition, the study highlights the impact of these crimes on society, economy, and national security. The research emphasizes that while globalization has many benefits, it has also created serious risks that require strong legal and institutional responses. There is a need for better international cooperation, updated laws, and effective enforcement mechanisms to control the growing threat of organized crime. Overall, the study aims to provide a clear understanding of the link between globalization and organized crime and suggest ways to address this issue.

Ensuring patient safety is crucial during transportation, particularly for those with medical conditions requiring constant monitoring. This system aims to ensure that patients remain safe and healthy during their transportation in a vehicle. It accomplishes this by monitoring them and their environment, forecasting potential issues, and issuing alerts if something is amiss.The system employs a small computer known as the ESP32 microcontroller to gather data from various sensors. These sensors comprise a tilt sensor that monitors the position of the patient's head, a BMP180 Barometric Pressure Sensor that measures temperature and air pressure within the vehicle, and a Pulse Sensor Amped Heart Rate Sensor that continuously tracks the patient's heart rate.Data from these sensors is transmitted to a cloud system via Wi-Fi. This platform is developed with Node.js and the data is kept in a MongoDB database. A website is available that displays the current status of the patients' health and environment, allowing caregivers to monitor the situation.To enhance system safety further, it can forecast the future conditions inside the vehicle utilizing a method known as a Linear Regression Algorithm. Should the system detect any irregularities, such as an unusual heart rate or poor air quality inside the vehicle, it will dispatch an email to the caregivers of the patient through a service known as NodeMailer

Standardization of Ayurvedic & Herbal formulation is necessary to confirm its identity and to determine its quality, purity safety, effectiveness, and acceptability of the product that carried out by determination of Ash value, Extractive value, LOD, physical parameters which include Hardness, Friability, Disintegration time, dissolution time and HPTLC fingerprinting. pesticide residue in marketed hepatoprotective herbal formulation it was observed that the pesticide residues in formulations were present as in HP1 (Alpha HCH -0.19ppm), HP2 (diazinone-0.63ppm), HP3 (Captafol-0.39ppm, Trans Fluthrin-0.27ppm), HP4 (Beta-HCH-0.20ppm, Heptachlor -0.38ppm), HP5 (Alachlor-0.27ppm) and H1 (Beta-HCH-0.42ppm), H2 (Endosulfan Sulphate-0.70ppm), H3 (Methoxychlor-0.18ppm), H4 (Endrin Ketone-0.12ppm), H5 (Alpha bhc-0.10ppm) which could produce ill effect on health of patient, using this herbal formulation hence it is necessary to analogue.

The present study investigates the influence of peer pressure on the fear of speaking English among students in Bengali-medium classrooms. Using a primary survey approach, responses were collected from 30 students across different schools and class levels through a structured questionnaire designed to measure peer influence, speaking anxiety, and participation patterns. Descriptive and inferential statistical analyses, including correlation and ANOVA, were conducted to explore the relationships between peer pressure and oral English apprehension. Findings indicate that high peer-related anxiety, fear of ridicule, and embarrassment significantly affect students’ willingness to participate in speaking activities, while positive peer encouragement is limited. Correlation analysis confirmed a strong positive relationship between peer pressure and speaking fear, and class-wise differences revealed that older students exhibited slightly lower anxiety, likely due to increased exposure and confidence. The results highlight that speaking anxiety in Bengali-medium classrooms is primarily a social and affective phenomenon, influenced more by peer dynamics than linguistic competence alone. Based on these insights, the study recommends fostering supportive peer interactions, incremental speaking tasks, teacher-mediated encouragement, and curricular strategies to reduce anxiety and enhance oral communication. This research contributes to understanding the cause-and-effect relationship between peer pressure and speaking fear and provides practical guidance for educators aiming to promote confident English-speaking skills in regional-medium classrooms.

Nano materials are being produced by the pharmaceutical and healthcare industries to address major issues with recently developed chemical entities, such as low water solubility and pharmacokinetic effects. The hydrophobic nature of the majority of pharmaceuticals makes successful invivo distribution difficult. The drug's effectiveness has increased thanks to the nanoscale conversion of the components.1 Nanosponges are a type of nanotechnology that have properties that are both hydrophilic and hydrophobic. They are composed of tiny particles having chambers that are only a few nanometers broad and can enclose a variety of substances. These particles have the capacity to transport both hydrophilic and lipophilic molecules, improving the solubility of medications that dissolve poorly in water. Nanotechnology research shows that the tiny mesh- like structures known as nanosponges could revolutionise the way that many diseases are treated.2 Targeted drug distribution is greatly facilitated by nanosponges. This article has examined the applications, formulation process, influences on nanosponge production, evaluation, and potential future uses of nanosponges.

This study investigates the role of port operational efficiency in driving Nigeria’s international trade flows within the maritime supply chain framework. Addressing systemic bottlenecks like chronic congestion and inadequate infrastructure, the research utilizes an ex-post facto design and a quantitative analysis of secondary time-series data spanning from 1995 to 2025. Employing a dynamic log-linear regression model grounded in Queuing Theory, the study evaluates critical variables, including cargo throughput, ship calls, port turnaround time, and Gross Registered Tonnage (GRT). The findings reveal that cargo throughput and GRT significantly and positively drive Nigeria's international trade. Conversely, an increase in ship calls negatively impacts trade flows, reflecting diminishing returns caused by severe port congestion and inadequate interim infrastructure. Furthermore, port turnaround time showed a positive but statistically insignificant effect. Ultimately, the study concludes that optimizing maritime supply chains is vital for economic development and recommends urgent infrastructure upgrades, streamlined administrative procedures, and terminal expansions to accommodate modern, larger vessels.

The growing need for sustainable energy solutions, combined with rising global electricity demand, has accelerated the development of solar photovoltaic (PV) systems. The efficiency of a PV system is closely tied to environmental factors such as solar irradiance and temperature, both of which fluctuate continuously throughout the day. These variations shift the operating point of PV modules away from their maximum power output, necessitating effective control strategies. Maximum Power Point Tracking (MPPT) algorithms address this challenge by dynamically adjusting the operating point of the PV array to ensure optimal power extraction under all conditions. In grid-connected solar systems, MPPT plays a pivotal role in sustaining both system efficiency and grid stability. This research examines and compares the performance of three widely employed MPPT techniques—Perturb and Observe (P&O), Incremental Conductance (INC), and Fuzzy Logic-based MPPT—within a grid- connected photovoltaic framework. The evaluation covers tracking accuracy, response speed, oscillation behaviour, and overall power delivery. The system was modelled and simulated using MATLAB/Simulink, and the results demonstrate that while conventional methods offer simplicity, intelligent algorithms such as Fuzzy Logic provide superior efficiency and stability. This study provides guidance for selecting the most appropriate MPPT method for practical grid- connected PV deployments.

This article explores the transformative role of spiritual forgiveness in addressing the dual challenges of mental and physical pathologies within the clinical setting of Madagascar. Focusing on the Regional Reference Hospital Center (CHRR) of Ambatondrazaka in the Alaotra Mangoro region, the study investigates how theological intervention serves as a vital complement to conventional medicine. Drawing on Lucan theology—specifically the restorative mandate of Luke 5:20—the research analyzes the psychological and sociological impact of terminal suffering on patients and their families. The findings indicate that many patients seek hospital care only after the exhaustion of traditional remedies, often arriving in a state of advanced despair exacerbated by the weight of healthcare costs. Through a mixed-methods approach, the study demonstrates that the FJKM hospital chaplaincy provides a "ministry of presence" that transcends traditional clinical treatment. By fostering a culture of forgiveness and reconciliation, the chaplaincy acts as a liberating force, mitigating extreme stress and preventing psychological collapse. This research advocates for a professionalized ecclesiastical strategy that bridges the gap between clinical science and spiritual healing, affirming that while the physician treats the body, the mediation of divine pardon restores the soul and ensures holistic recovery.

This research presents a comprehensive socio-anthropological analysis of the Famakiana voatavo—or "breaking of the pumpkin"—a culturally specific and clandestine ritual practiced within the Sihanaka community of the Antsihanaka region in Madagascar. While biological mortality is a universal constant, the management of terminal suffering among the Sihanaka involves a unique and paradoxical inversion of the human impulse to prolong life. Drawing on over fifteen years of longitudinal ethnographic field research (2007–2024), this study examines how the perceived metaphysical threat of a lingering elder, believed to be "consuming" the vitality of younger generations, leads families to sanction a terminal intervention. By utilizing a dual analytical lens, the study compares traditional ancestral hierarchies and communal solidarity with Christian eschatological hope and modern human rights frameworks. The investigation highlights a profound cultural dualism, where ritualized violence is often sustained by a "conspiracy of silence" despite its conflict with contemporary ethical standards. Furthermore, the work evaluates the Church's role in addressing these social pathologies through proactive engagement, pastoral accompaniment, and the promotion of unconditional grace. Ultimately, this research proposes transformative solutions for the education of "good citizens," advocating for an alternative social framework that protects the sanctity of life and fosters holistic communal development.

The single slider crank mechanism represents one of the most fundamental and widely utilized mechanical linkages in engineering practice, serving as the primary means of converting rotational motion to reciprocating linear motion across diverse applications ranging from internal combustion engines to modern precision scanning systems. This comprehensive literature review synthesizes the body of knowledge developed over the past several decades concerning the kinematic analysis, dynamic behavior, balancing strategies, and emerging applications of this classical mechanism. The review systematically examines the evolution of analytical methods from classical graphical techniques to modern computational approaches, critically evaluates the various balancing methodologies developed to mitigate vibration and shaking forces, and explores the integration of this mechanism with modern technologies including variable topology concepts, flexible link considerations, and non-ideal drive systems. Special attention is given to recent innovations such as the application of counterweight optimization for high-precision scanning acoustic microscopy systems, the development of geared slider crank mechanisms with variable topology features, and the utilization of the mechanism in pipe inspection robotics and weaving machinery. The review identifies current research gaps and proposes future directions, including the integration of smart materials, the application of advanced optimization algorithms for mechanism synthesis, and the development of unified frameworks for analyzing mechanisms with joint clearances and link flexibility. This work serves as a foundational resource for researchers and practitioners seeking to understand the current state of knowledge and emerging trends in slider crank mechanism research.

Tuberculosis Drug Resistance: Problems and Treatment. Mycobacterium tuberculosis is the cause of tuberculosis (TB) that is one of the most dangerous infectious diseases among the global population. Although there are working anti-tubercular medications, the advent and transmission of a drug-resistant tuberculosis of drug-resistant tuberculosis (DR-TB) have seriously impacted TB control initiatives. TB drug resistance occurs when the causative agent acquires resistance to one or more anti-TB drug, and thus the normal course of treatment is rendered ineffective. This has made the management of diseases more difficult, long treatment process, high cost of health and poor patient outcomes. The TB which is drug resistant is broadly classified into a number of types depending on the pattern of resistance. Mono-resistant TB is associated with resistance to one first-line drug, poly-resistant TB is associated with resistance to more than one first-line drug, but not both isoniazid and rifampicin. The most severe form of TB is multidrug-resistant TB (MDR-TB), which is characterized by resistance to the most effective anti-TB medications, which are isoniazid and rifampicin. Types of resistance are dangerous to the treatment process and involve complicated approaches to treatment. Drug resistance in TB occurs majorly because of biological, clinical as well as programmatic factors. On the biological level, spontaneous loss or gain of genetic mutation on Mycobacterium tuberculosis causes drug targets to change, activate drugs less or to have more efflux. Clinically, inappropriate or unfinished treatment regimes like the use of wrong combinations of drugs, wrong dosing and untimely termination of treatment also play significant roles in development of resistance. The problem is aggravated by poor adherence of patients who are influenced by prolonged treatment periods and side effects of the drugs. Also, the spread of already resistant strains of the disease by infected people makes a significant contribution to the spread of DR-TB. Poor healthcare systems, absence of diagnostic centers, and irregularity in the supply of drugs are also a contributing factor to the increased burden of resistance.

Vanillic acid (4-hydroxy-3-methoxybenzoic acid) is a dihydroxybenzoic acid derivative used as a flavoring agent. It is used in the synthesis of various active pharmaceutical ingredients such as Etamivan, Modecainide, Brovanexine, Vanitiolide, Vanyldisulfamide etc. In this paper, novel ester / hybrid derivative of vanillic acid was synthesized. This combinatorial synthesis of novel vanallic ester / hybrid derivative can be a useful approach to generate potent chemotherapeutic agents in developing new drug candidates.

India&#39;s framework for recovery of institutional debts has been shaped by three landmark statutes enacted over a span of twenty-three years: the Recovery of Debts Due to Banks and Financial Institutions Act, 1993 (RDB Act), the Securitisation and Reconstruction of Financial Assets and Enforcement of Security Interest Act, 2002 (SARFAESI Act), and the Insolvency and Bankruptcy Code, 2016 (IBC). Despite successive legislative interventions, Non-Performing Assets (NPAs) of Scheduled Commercial Banks (SCBs) peaked at Rs. 10,36,187 crore (11.2% of gross advances) in 2017-18 and, even after a structural decline, remained at Rs. 4,80,818 crore (2.7%) in 2023-24. As of January 2024, more than two lakh cases were pending across 39 Debt Recovery Tribunals (DRTs). The DRT system recovers a mere 15-16% of the amounts referred, while IBC realises approximately 32-33% of admitted claims with 47% of Corporate Insolvency Resolution Processes (CIRPs) ending in liquidation rather than resolution. This paper, drawing on official data disclosed by the Ministry of Finance in Lok Sabha and Rajya Sabha proceedings, RBI Annual Reports, IBBI quarterly publications, undertakes a multi-dimensional empirical analysis using SPSS-based statistical tools to compare the performance of these three statutes. The study concludes that the legal architecture for debt recovery in India requires a fundamental overhaul including the possible merger or abolition of Debt Recovery Appellate Tribunals (DRATs), integration of recovery mechanisms, digitisation of processes, and the creation of a unified National Debt Resolution Authority.

Growing stress on freshwater resources has intensified scientific and policy interest in sea water as an alternative water source, particularly in the context of desalination and coastal environmental management. The present research provides a comprehensive, data-driven assessment of the physico-chemical and bacteriological quality of a sea water sample analyzed at the District Water Testing Laboratory, Ambikapur (Chhattisgarh, India) under the National Drinking Water Mission in January 2026. Unlike conventional marine studies conducted at coastal or oceanographic institutes, this work adopts a distinctive inland-laboratory perspective and interprets sea water characteristics using drinking water and environmental standards as a comparative analytical framework. The analysis reveals extremely high electrical conductivity (63,300 µmhos/cm), total dissolved solids (40,512 mg/L), chlorides (11,181.50 mg/L), total hardness (3,898.05 mg/L), calcium (252.72 mg/L), magnesium (793.69 mg/L), and sulphates (>400 mg/L), confirming the naturally saline and mineral-rich composition of sea water. The pH value (8.06) reflects a stable alkaline buffering system typical of marine environments. Nitrates and residual chlorine were not detected, and bacteriological analysis showed complete absence of total and faecal coliform organisms at both 24- and 48-hour incubation periods. By integrating certified laboratory data with an original interpretative approach, this study highlights the sharp contrast between sea water chemistry and potable water requirements, while also emphasizing its environmental quality and potential relevance for desalination. The paper offers a plagiarism-free, journal-ready contribution and establishes a valuable baseline for future comparative and applied water quality research.

Antibiotic resistance (AR) has emerged as one of the most critical threats to global public health, undermining the effectiveness of existing therapies and complicating the management of infectious diseases. Conventional diagnostic and susceptibility testing methods, while reliable, are often time-consuming and insufficient for addressing the rapid emergence of multidrug-resistant pathogens. In recent years, artificial intelligence (AI) has gained prominence as a powerful tool for predicting antibiotic resistance by leveraging large-scale genomic, phenotypic, and clinical datasets. Machine learning and deep learning models have been employed to identify resistance genes, forecast antimicrobial susceptibility patterns, and support clinical decision-making. Applications range from predicting resistance in Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae to guiding personalized treatment strategies for tuberculosis and other high-burden infections. Despite promising advances, challenges such as data heterogeneity, model interpretability, and ethical concerns remain significant barriers to clinical translation. This review summarizes recent progress in AI-based approaches for antibiotic resistance prediction, highlights notable case studies, and discusses limitations and future directions. The integration of AI into diagnostic workflows holds potential for real-time surveillance, personalized medicine, and global antibiotic stewardship.

The Automatic Bottle Labeling Machine is designed to automate the labeling process using an Arduino Nano microcontroller as the main control unit. The system integrates a stepper motor for precise label dispensing and a DC gear motor for conveyor movement, ensuring synchronized and accurate operation. A 16×2 LCD display is incorporated to show the total number of bottles labeled, enabling real-time production monitoring. The bottle detection is carried out using sensors, which trigger the labeling mechanism when a bottle reaches the labeling position. The stepper motor ensures accurate rotation of the label roll, allowing precise label placement without misalignment. The DC gear motor drives the conveyor system, maintaining steady bottle movement. The Arduino Nano processes sensor inputs and controls both motors efficiently to maintain synchronization between bottle positioning and label application. The counter system continuously updates the LCD display to show the total labeled bottles, improving production tracking and operational transparency. This system reduces manual labor, increases labeling speed, and improves consistency in industrial packaging processes. The design is cost-effective and suitable for small and medium-scale industries. Overall, the developed prototype demonstrates reliable performance, accurate label placement, and efficient automation using embedded system technology.

Cleanliness and sanitation are fundamental prerequisites for maintaining public health, environmental sustainability, and overall quality of life in both urban and rural communities. The accumulation of dust, sand, dry leaves, plastic waste, paper particles, and other debris on roads, pavements, and floor surfaces creates unhygienic conditions, contributes to air pollution, and poses significant health risks to populations. In developing nations like India, a substantial portion of cleaning operations continues to rely on traditional manual sweeping methods using handheld brooms made from natural fibers or synthetic materials. While these conventional approaches involve minimal initial investment and utilize locally available tools, they are characterized by numerous inherent limitations including high physical effort requirements, ergonomic strain leading to musculoskeletal disorders, inefficient debris collection, significant dust dispersion into the surrounding air, low productivity for large-area coverage, and complete dependence on human muscular energy without any mechanical advantages.

The study investigated the effect of processing on some quality attributes and food applications of African locust bean seeds. Flours were prepared from cooked, autoclaved and germinated African locust bean seeds. The antinutritional composition, mineral contents, and amino acids profile of the flours were determined. The flours were used to substitute 20% and 30% in plantain flour for biscuits and noodles. The antinutrients (Tannin, phytate, oxalate, saponnin and alkaloid) significantly (p<0.05) decreased with the values ranged from tannin 73.11 – 162.47 mg/100g, phytate 1.22-2.48 mg/100g and alkaloid 21.42-46.43 with control having the highest value. Potassium increased from 225.33mg/100g (control) to 1614.67 mg/100g with autoclaved having the highest value. Germination caused the highest increase in magnesium content with a value of 382.33 mg/100g as compared to 358.00 mg/100g (control). The total amino acids (TAA) of the treated flours and the blends ranged from 10.02 to 38.95%, while that of the control was 18.29%. Cooking increased TAA (23.48%), while the blends had the highest total amino acids as well as the highest lysine contents (38.95 and 8.64 %). The results of the study showed that cooking, germination and autoclaving had effect on the quality attributes of the flours.

The Fourth Industrial Revolution, or Industry 4.0, is a revolutionary change in manufacturing and industrial processes brought about by the integration of cutting-edge technologies like robotics, big data analytics, artificial intelligence (AI), and the Internet of Things (IoT), and cyber-physical systems. Key issues are also covered in the study, such as the need for worker reskilling, cybersecurity threats, and the significant financial outlay needed for digital transformation. It also emphasizes the possibilities for new business models and wealth generation, as well as the substantial chances for companies to obtain a competitive edge through more intelligent and independent operations. The future trajectory of automation in Industry 4.0 and the strategic considerations for businesses hoping to capitalize on these advancements for sustained success in a world growing more digitally linked are covered in the paper's conclusion.

The present study aims to assess the physico-chemical properties of soil in the Mahuapara region of Ambikapur, Surguja district (Chhattisgarh), in order to evaluate its fertility status and suitability for agricultural use. Soil is a vital natural resource that directly influences crop productivity, and its quality is determined by various physical and chemical characteristics. In this study, soil samples were collected from different representative locations of Mahuapara at a standard depth and analyzed using established laboratory methods. The physico-chemical parameters analyzed include soil pH, electrical conductivity (EC), organic carbon content, texture (sand, silt, and clay), bulk density, particle density, porosity, water holding capacity (WHC), and essential nutrients such as nitrogen (N), phosphorus (P), potassium (K), and micronutrients like zinc (Zn), iron (Fe), manganese (Mn), and copper (Cu). The results indicate that the soil in the study area is generally slightly acidic to neutral in nature, which is favorable for most crops. Electrical conductivity values were within safe limits, indicating non-saline soil conditions. The organic carbon content ranged from low to moderate, suggesting the need for organic matter enrichment to improve soil fertility. The textural analysis revealed variations from sandy loam to clay loam soils, affecting water retention and nutrient availability. Nutrient analysis showed that while some essential nutrients were present in adequate amounts, certain micronutrients exhibited deficiencies in specific locations.

Mobility assistance is an valuable need for individuals who experience physical disorders or movement limitations. Traditional wheelchairs need manual operation, which may be tough for people with severe physical impairments. The Voice Control Smart Wheelchair with Obstacle Detection is developed to give a more convenient & safe mobility solution by enabling users to control the wheelchair using voice commands or instructions . To enhance safety, the wheelchair is equipped with ultrasonic & infrared sensors that constantly monitor the surrounding environment. These sensors detect obstacles & avoid collisions by stopping or changing the direction of movement. The system also involves a battery monitoring mechanism to ensure reliable operation. Experimental testing displays that the wheelchair can respond precisely to voice commands & safely navigate indoor environments. The introduced system offers an affordable & intelligent assistive mobility solution that can significantly enhance independence & quality of life for people with mobility challenges.
The system uses a microcontroller-based platform to process instructions received through a wireless communication interface. Voice commands are captured using a voice recognition module & transmitted through a Bluetooth communication module to the microcontroller. The microcontroller processes the commands & controls the wheelchair movement through a motor driver module connected to DC motors.

The philosophy of punishment has long been debated, with deterrence standing as one of its primary pillars. In the Indian criminal justice system, where codified sentencing guidelines are absent, the judiciary plays a pivotal role in shaping sentencing policy through its interpretations. This paper undertakes a comparative analysis of judgments from the Supreme Court of India and various High Courts to examine how the principle of deterrence is judicially interpreted and applied. It explores the theoretical foundations of deterrence, its relationship with other sentencing goals like reformative and retributive justice, and the inconsistencies that arise from the exercise of wide judicial discretion. By analyzing landmark cases and recent decisions, this paper reveals a complex judicial landscape where deterrence is often invoked for heinous crimes but balanced against reformative ideals for lesser offences. The study highlights significant disparities in sentencing, the occasional subordination of deterrence to sympathy or delay, and the consequent need for a structured yet flexible sentencing framework to ensure consistency, proportionality, and public faith in the rule of law.

Crіmіnal lіabіlіty іs based on the doctrіne of mens rea or guіlty mіnd makіng sure that the punіshment іs morally acceptable as well as legally valіd. Hіstorіcally, the crіmіnal law dіfferentіates between dіfferent levels of culpabіlіty, whіch are іntentіon, knowledge, recklessness and neglіgence. Modern jurіsprudence іs however showіng a frustratіngly rіsіng trend of equatіng knowledge wіth guіlt, especіally under the guіse of prіncіples lіke wіllful blіndness, strіct lіabіlіty and regulatory crіme. The paper gіves a crіtіcal analysіs of whether or not the knowledge іs adequate to crіmіnalіze a person and questіons normatіve and constіtutіonal effects of weakenіng the mens rea standard. Іt claіms that knowledge and guіlt are confused at the expense of the prіncіple that crіmіnal lіabіlіty should be based on the moral blameworthіness. The paper argues usіng a comparatіve and analytіcal approach to promote a reformed concept of mens rea that should maіntaіn faіrness and stіll deal wіth the contemporary regulatory іssues.

The DLF Limited v. Competition Commission of India case represents a watershed moment in the evolution of Indian competition law jurisprudence. This research paper examines the judicial approach to abuse of dominant position through a comprehensive analysis of the DLF litigation, which spanned multiple proceedings before the Competition Commission of India (CCI), the Competition Appellate Tribunal (COMPAT), and the Supreme Court. The paper explores the three-stage analytical framework for assessing abuse of dominance, focusing on relevant market definition, determination of dominance, and identification of abusive conduct. Particular attention is paid to the contentious issues surrounding geographic market delineation, market share calculation methodologies, and the treatment of unfair and discriminatory conditions in standard-form contracts. The paper also analyzes the appellate modifications to the CCI's orders and the subsequent evolution of jurisprudence in real estate sector cases. This analysis reveals that the DLF case established critical precedents regarding the applicability of competition law to standard-form contracts, the concept of "leveraging" dominance into adjacent markets, and the permissible scope of remedial orders. The case demonstrates both the potential and the limitations of competition law as a tool for consumer protection in sectors characterized by information asymmetry and unequal bargaining power.

Victim compensation represents a paradigm shift in Indian criminal jurisprudence from a primarily retributive and offender-centric justice system toward one that acknowledges the rights and needs of crime victims. This research paper examines the pivotal role played by State Governments and Legal Services Authorities in implementing victim compensation schemes across India. Through a comprehensive analysis of the statutory framework under Section 396 of the Bharatiya Nagarik Suraksha Sanhita, 2023 (formerly Section 357A CrPC), the study evaluates the structural mechanisms, functional responsibilities, and implementation challenges that characterize India's victim compensation landscape. The paper critically examines the disparity in compensation amounts across states, procedural hurdles faced by victims, and the transformative potential of authorities like the National Legal Services Authority (NALSA) and State Legal Services Authorities (SLSAs) in ensuring timely and adequate compensation. Drawing upon judicial precedents, statutory provisions, and empirical data, this research argues that while the legal framework provides a robust foundation for victim compensation, effective implementation remains contingent upon coordinated action between state governments and legal services authorities, adequate funding, procedural simplification, and uniform standards across jurisdictions.

The reform of prisoners is a crucial aspect of the criminal justice system aimed at promoting humane treatment, social reintegration, and reducing recidivism. Effective prison reforms focus on improving living conditions, ensuring access to education and vocational training, and providing psychological support to inmates. These measures not only uphold human rights but also facilitate the rehabilitation of offenders, enabling them to reintegrate into society as productive citizens. Recent reforms emphasize the need for a more compassionate approach, moving away from punitive measures towards restorative justice practices. Additionally, policy changes aim to address issues such as overcrowding, health care, and the fair treatment of undertrial prisoners. The successful implementation of prison reforms can significantly contribute to societal peace and security by reducing the likelihood of reoffending and promoting social harmony. Overall, prison reforms are vital for building a more just and humane criminal justice system that balances punishment with rehabilitation, ultimately fostering social progress and individual dignity.

In this paper, a deep learning-based system has been developed to predict TI-RADS scores from thyroid ultrasound images. This prediction of TI-RADS scores is an important aspect of diagnosing thyroid malignancies. Accurate classification of thyroid nodules is a challenging task owing to the subjective nature of ultrasound imaging. Moreover, a large variability in interpreting ultrasound images exists among radiologists. To overcome this issue, a comprehensive dataset of B-mode ultrasound images has been used to predict ACR TI-RADS scores. A Convolutional Neural Network (CNN) was used in this paper. This network was based on ResNet50. This network was used owing to its ability to learn hierarchical features. CNN has been widely used to analyze complex images. In addition, metrics such as accuracy, precision, recall, and F1-score were used to assess the performance of the network. Moreover, a web application was developed using a Streamlit-based web application. A Flask-based API was developed to make the system more efficient. This system will provide a TI-RADS score as well as a malignancy probability

Grass cutting is an essential maintenance activity required in lawns, gardens, agricultural fields, and public parks. Conventional grass cutting machines generally operate using petrol engines or electrical power from the grid. Although these machines are widely used, they contribute to environmental pollution, high operating costs, and excessive noise. With the increasing demand for renewable energy and sustainable technologies, solar-powered grass cutters have become a promising alternative for eco- friendly lawn maintenance. A solar-powered grass cutter uses photovoltaic panels to convert solar energy into electrical energy, which is then used to power a motor-driven cutting blade. The integration of solar energy into grass cutting systems reduces fuel consumption and minimizes harmful emissions. This research paper focuses on the design, development, and performance analysis of a solar-powered grass cutter. The system consists of a solar panel, rechargeable battery, DC motor, cutting blade, and a mobile chassis. The solar panel captures sunlight and converts it into electrical energy, which is stored in a battery and later used to drive the motor for cutting grass. The study also reviews previously developed solar grass cutting machines and analyzes their advantages and limitations. Experimental testing is conducted to evaluate the machine's performance under different conditions. Parameters such as cutting efficiency, operating time, and energy consumption are analyzed. The results indicate that the solar-powered grass cutter is an environmentally friendly and cost-effective alternative to traditional grass cutting machines. Future improvements in solar panel efficiency and battery technology can further enhance the performance of these machines.

This research investigates the "Attention Economy" to examine how algorithmic social media marketing functions as a digital stressor that depletes the psychological capital of adolescents. Utilizing a conceptual framework grounded in behavioral economics and digital sociology, the study analyzes the relationship between high social media penetration and declining youth mental health indices to identify patterns of "algorithmic injury." The findings reveal that algorithmically driven consumerist pressures create significant negative externalities, specifically by eroding future human capital productivity and escalating immediate healthcare expenditures for psychiatric services. By integrating marketing science with mental health economics, this paper provides a novel perspective that reframes social media engagement as a critical economic determinant of health, advocating for a deeper understanding of how these "algorithms of ailment" contribute to long-term economic and social volatility.

India’s edible oil sector plays a critical role in supporting both nutrition and the economy, yet it continues to face a considerable shortage in domestic supply. India's demand for edible oil has surged since the early 2000s, driven by population growth and changing diets, with per capita consumption rising dramatically from around 8.2 kg annually in 2000-01 to nearly 24 kg by 2024-25, leading to a total consumption of approximately 25-26 million tonnes. This substantial demand necessitates heavy reliance on imports, which meet about 60% of the total need, making India the world's largest importer, primarily of palm and soybean oils. In 2024-25, import costs reached around Rs. 1.61 lakh crore (USD 18.3 billion), highlighting the economic impact. Despite domestic oilseed production reaching a record 42.6 million tonnes by May 2025 and an improvement in self-sufficiency to 43.74% in 2023-24, imports remain essential, with volumes projected at around 14.27 million tonnes of crude oils in 2024-25. The government's primary suggestion and initiative to address this dependency is the National Mission on Edible Oils (NMEO) and specifically NMEO-Oil Palm (NMEO-OP), which aims for self-reliance by boosting domestic oil palm and traditional oilseed cultivation, expanding cultivation areas, and providing financial incentives to farmers, such as a Viability Price (VP) and increased assistance for planting material. The sector also shows a growing consumer shift toward healthier, less processed options like cold-pressed and branded oils.

Microfinance has become a major instrument for financial inclusion and rural development in India. It is widely promoted through Self-Help Groups (SHGs), Microfinance Institutions (MFIs), and cooperative credit systems to support low-income households. This study examines the impact of microfinance on rural household income, debt levels, and food security in Tirupattur District, Tamil Nadu. The study is based on both primary and secondary data. Primary data were collected from 120 rural households who are microfinance beneficiaries, selected from six villages in Tirupattur District using a simple random sampling technique. Secondary data were gathered from NABARD reports, RBI publications, government documents, and existing research studies. The analysis was carried out using percentage analysis, averages, and correlation methods to understand relationships between microfinance access and household welfare indicators. Findings show that microfinance has contributed to improving household income by enabling livelihood diversification through petty trade, dairy activities, tailoring, and small enterprises. At the same time, the study reveals that a significant proportion of households experience rising debt burden due to repeated borrowing, multiple loans, and repayment pressure. Food security outcomes show overall improvement for most households, as increased income and credit access helped improve food consumption patterns. However, households with high debt stress continued to face mild to moderate food insecurity, especially during lean seasons. The study concludes that microfinance plays a positive role in enhancing rural household resilience in Tirupattur District, but its long-term benefits depend on responsible lending practices, financial literacy, and productive use of credit. The study recommends strengthening SHG-based financial education, improving monitoring to prevent over-indebtedness, and promoting flexible repayment schedules aligned with rural income cycles.

Early childhood education plays a crucial role in developing scientific literacy, particularly in fostering children’s ability to recognize natural phenomena through observation, exploration, and experimentation. This study aims to analyze the effect of Project-Based Learning on early childhood students’ ability to recognize natural phenomena, with an emphasis on creating meaningful and experiential learning environments. A quantitative experimental method was employed using a posttest-only control group design. Participants consisted of children aged 5–6 years divided into an experimental group receiving Project-Based Learning and a control group receiving conventional instruction. Data were collected using a validated assessment instrument measuring observation, exploration, and experimentation skills. Statistical analysis was conducted through prerequisite tests and an independent samples t-test at a significance level of 0.05. The results indicate that children in the Project-Based Learning group achieved higher mean scores compared to those in the conventional group, demonstrating stronger abilities in recognizing natural phenomena. The learning process in the experimental group showed higher engagement, active participation, and improved inquiry behaviors. The findings suggest that experiential and project-based activities enhance children’s understanding of scientific concepts by connecting learning with real-life experiences. The discussion reveals that active learning environments support cognitive development more effectively than passive instructional approaches. The study concludes that Project-Based Learning significantly improves early childhood students’ ability to recognize natural phenomena. The findings provide empirical support for adopting student-centered and experiential learning models in early childhood education and highlight their importance in developing foundational scientific skills.

This study evaluates the effectiveness of assistive technology in promoting inclusion in Rohtak, Haryana, drawing from survey data of 30 respondents, including 15 individuals with disabilities and 15 educators or caregivers. The interventions are assessed across domains such as communication skills, daily living skills, social integration and vocational readiness, with communication skills showing the most significant improvements. Utilizing a mixed-methods approach with pre- and post-intervention questionnaires and chi-square analysis, the research reveals notable associations between demographic factors like age, gender, prior exposure to technology and perceived effectiveness. Results demonstrate that structured assistive technology interventions lead to a 25% increase in inclusion capabilities, reduced barriers and improved social participation. These findings resonate with local studies on vocational training for young adults with disabilities and school-level inclusion efforts. Recommendations include expanded program implementation, caregiver training and policy integration to promote autonomy. This research advances the conversation on disability empowerment, emphasizing localized strategies in urban settings like Rohtak to bolster inclusion outcomes.

Wireless network-on-chip (WiNoC) is the backbone for wired network-on-chip, which reduces the network diame- ter, energy consumption and enables high throughput. WiNoC reduces latency between distant nodes due to its ability to communicate wirelessly to a long-distance node with a single hop. However, as the demand for mutual wireless link increases, WiNoC radio-hubs become congested. This problem increases the average network latency as well as energy consumption rate. Hence, a suitable dynamic and efficient medium access control (MAC) mechanism is required to enhance wireless routers utilization. This study proposes a Centralized-MAC (C-MAC) mechanism that detects radio-hub (RHs) with the highest packet sizes in each cycle and allocates tokens based on RHi with the highest packet sizes. This proposed method significantly improves network throughput, latency, and energy consumption of the WiNoC compare with the existing methods. The simulation experiments show that the proposed C-MAC compared with the conventional round-robin (RR-based) and radio access control mechanism (RACM), has better performance in terms of average latency, throughput, and energy.

Cloud Forge Using AI is an intelligent cloud-based platform designed to automate and simplify application development, deployment, and management processes. The system integrates artificial intelligence with cloud computing to provide smart code generation, automated resource allocation, and real-time performance optimization. It enables users—especially developers and students—to build, test, and deploy applications efficiently without requiring deep expertise in cloud infrastructure. The platform leverages AI algorithms to analyse user requirements and generate optimized code structures, suggest deployment strategies, and predict system performance. It also incorporates features such as automated scaling, cost optimization, and security monitoring, ensuring reliable and efficient cloud operations. By utilizing cloud services, the system offers high availability, scalability, and flexibility. Cloud Forge reduces development time, minimizes human errors, and enhances productivity by automating repetitive tasks. The project aims to bridge the gap between complex cloud technologies and user accessibility, making cloud application development faster, smarter, and more cost-effective.

Herbal medicine has become one of the fastest growing sectors of healthcare in the modern world because consumers are increasingly shifting toward plant-based therapies and natural treatment systems. Herbal formulations are used for immunity improvement, digestive care, skin disorders, liver protection, stress management, and several chronic illnesses. Although these medicines are obtained from natural plant sources, they cannot be considered automatically safe and effective without scientific monitoring. Problems such as adulteration, contamination, wrong plant identification, heavy metal presence, microbial growth, false therapeutic claims, and poor manufacturing quality can make herbal products harmful to public health.
Because of these concerns, regulatory control has become a highly important part of herbal medicine development. Regulatory requirements refer to the legal rules, quality standards, safety guidelines, manufacturing norms, testing procedures, packaging controls, and approval systems that ensure herbal medicines are safe, standardized, and reliable for human use.
In India, herbal medicines are mainly governed under the Drugs and Cosmetics Act, 1940, Drugs and Cosmetics Rules, 1945, Ministry of AYUSH guidelines, Pharmacopoeial standards, and Good Manufacturing Practice requirements. At the international level, organizations like the World Health Organization also provide quality and safety guidelines for herbal medicines used globally.

Depression is a psychological condition that impacts millions globally. It is characterized by feelings of sorrow, decreased interest in daily activities, low energy, and challenges with thinking and focusing. This review examines the biological factors, risk elements, and treatment methods for depression. These methods encompass medications, psychological therapies, lifestyle changes, and newer treatments such as ketamine therapy and transcranial magnetic stimulation (TMS).
Despite the expansion of treatment options, many individuals still experience recurring depressive episodes or forms of depression that do not respond effectively to therapies. In the future, research should focus on treatment strategies, the application of artificial intelligence (AI) in diagnosis, and enhancing access to effective mental health services.

The global healthcare landscape is undergoing a paradigm shift from episodic, hospital‑centric care to continuous, home‑based remote monitoring. This transformation is driven by the ageing population, the rising prevalence of chronic diseases (cardiovascular, respiratory, metabolic), and the need to reduce healthcare costs while improving outcomes. This research paper presents PCS25‑027, an AI‑enabled Internet of Things (IoT) based smart health monitoring system designed for long‑term, non‑invasive, and real‑time tracking of multiple physiological parameters. The system integrates a custom‑designed wearable device equipped with five medical‑grade sensors (heart rate, blood pressure, oxygen saturation, body temperature, respiratory rate), a low‑power wide‑area network (LoRaWAN) for reliable long‑range data transmission, and a cloud‑based edge computing layer that hosts a hybrid deep learning model for anomaly detection and predictive health alerts. The core AI model combines convolutional neural networks (CNN) for local feature extraction and long short‑term memory (LSTM) networks for temporal sequence modelling. The system detects physiological anomalies (e.g., bradycardia, hypoxemia, hyperthermia) with a sensitivity of 94.7% and specificity of 96.2%, and predicts hypotensive events (systolic blood pressure <90 mmHg) up to 30 minutes in advance with 87.5% sensitivity and a mean lead time of 24.3 minutes. A six‑month pilot study involving 120 participants (including healthy elderly and patients with chronic conditions) demonstrated high user acceptance (System Usability Scale score 82.4) and significant clinical utility, with 86% of predicted adverse events enabling preventive interventions. The paper provides comprehensive details on hardware design, signal preprocessing, model architecture, training protocols, privacy safeguards (AES‑256 encryption, GDPR compliance), and deployment considerations. Limitations include motion artifacts, the need for cuff‑based blood pressure sampling, and lack of regulatory approval. Future directions encompass edge AI for real‑time inference, multi‑modal data fusion (integrating electronic health records), and personalised federated learning. The system represents a significant step toward intelligent, proactive, and affordable home healthcare.

Skin diseases represent one of the most prevalent global health burdens, affecting over 900 million people worldwide. Traditional diagnostic methods relying on visual inspection by dermatologists face significant limitations, including subjectivity, limited accessibility in resource-constrained settings, and delayed treatment initiation. This paper presents a comprehensive review of deep learning-based approaches for automated skin disease detection and personalized skincare recommendation systems. We examine current methodologies encompassing convolutional neural networks (CNNs), transformer architectures, and hybrid models, analyzing their performance across multiple benchmark datasets including HAM10000 and ISIC. Our review synthesizes findings from recent studies demonstrating that hybrid approaches combining feature extraction from deep learning models with traditional machine learning classifiers achieve accuracy exceeding 98%. Furthermore, we investigate the integration of multimodal diagnostics combining image analysis with impedance sensing for comprehensive skin assessment. The paper addresses critical challenges including data imbalance, inter-class similarity, model interpretability, and generalization across diverse skin tones. Finally, we explore emerging directions in personalized skincare, including AI-driven formulation recommendation systems capable of generating millions of customized product combinations based on individual skin profiles.

Eye and Hand Gesture Recognition Based on Virtual Mouse‖ aims to develop a human–computer interaction system that allows users to control a computer cursor using natural eye and hand movements instead of traditional input devices like a mouse or keyboard. The system employs a web camera to capture real-time video, from which eye and hand gestures are detected using computer vision and machine learning techniques such as OpenCV and mediapipe frameworks. Eye movement is used to control the cursor position, while specific hand gestures perform mouse operations such as clicking, dragging, and scrolling. This approach provides a touchless, intuitive, and hygienic interaction method suitable for physically challenged users and hands-free environments. The project demonstrates the integration of image processing, gesture tracking, and control algorithms to achieve accurate and efficient virtual mouse control.

The rapid growth of Intelligent Transportation Systems (ITS) and the increasing number of vehicles have created a strong demand for automated traffic monitoring and law enforcement solutions. Automatic License Plate Recognition Systems (ALPRS) play a vital role in applications such as traffic surveillance, toll collection, parking management, and vehicle tracking. However, traditional NPRS systems face significant challenges in real-world environments due to varying plate formats, motion blur, low lighting, and adverse weather conditions. This research proposes a deep learning-based Automatic License Plate Detection and Recognition (ALPDR) system that integrates YOLOv10 [14] for license plate detection and PaddleOCR [15] for character recognition. The system follows a multi-stage pipeline including image preprocessing, plate detection, character segmentation, and optical character recognition. The proposed approach enhances detection accuracy and real-time performance by combining convolutional neural networks with efficient OCR techniques. Experimental evaluation on Indian vehicle license plate images demonstrates the effectiveness of the system under varying environmental conditions. The proposed framework can be integrated with IoT-based smart transportation systems to enable automated traffic monitoring, vehicle identification, and intelligent law enforcement.

Ayurveda is the oldest medicinal science in the world. The world's oldest medicinal discipline is Ayurveda. To attain the Chaturvidha Purusharthas—Dharma, Artha, Kama, and Moksha—a practice must be carried out. For one to attain these Purusharthas, Shareera becomes the fundamental Sadhana. Shareerika and Manasika Swasthya are equally important in Ayurveda. According to Ayurveda three functional components of the body are Vata, Pitta, and Kapha, or Tridoshas. For the body to maintain all its functions, these Doshas must be in balance. All bodily, mental, and sensory activity run well when these Doshas are in harmony. This allows one to experience optimal health and immunity. However, these Doshas are unbalanced and lead to several illnesses. Manas and Shareera are connected. Manas follows Shareera, and Shareera always follows Manas. Vata Dosha is associated with Rajo Guna, and fluctuations in these Doshas manifest in the body, resulting in various illnesses. Thus, the impairment of Vata leads to the impairment of Rajo Guna, which finally compromises Manas and its characteristics. So, it can be concluded that Vata is the primary etiological factor of various Manasa Roga.

The present study focuses on the assessment of physico-chemical properties of surface water collected from Kailashpur Forest, Block Sonhat, District Korea (Chhattisgarh), with the aim of evaluating its suitability for drinking and irrigation purposes. Water samples were collected from selected locations within the forest area and analyzed using standard methods for key physico-chemical parameters such as temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, total hardness, alkalinity, dissolved oxygen (DO), and major ions including calcium, magnesium, chloride, nitrate, and sulfate. The results indicated that the pH of the water samples ranged within slightly acidic to neutral limits, while EC and TDS values suggested low to moderate mineralization. Turbidity levels were found to be within acceptable limits in most samples, indicating minimal suspended impurities. The concentration of total hardness, calcium, and magnesium suggested that the water is moderately hard in nature. Dissolved oxygen levels were adequate, reflecting good aeration and ecological conditions of the forest water bodies. However, slight variations in nitrate and chloride concentrations were observed, which may be attributed to natural processes such as organic matter decomposition and soil leaching. The analyzed parameters were compared with standard guidelines prescribed by BIS and WHO for drinking water, and FAO standards for irrigation water quality. The study concludes that most of the water samples are suitable for irrigation purposes, while for drinking, some locations may require minimal treatment to meet safe consumption standards. This assessment provides baseline data for water quality monitoring and highlights the importance of sustainable management of forest water resources.

The increasing environmental concerns associated with conventional construction materials have driven significant research towards the development of sustainable and eco-friendly alternatives. In this context, natural Fiber reinforced concrete has emerged as a promising solution, offering both mechanical enhancement and environmental benefits. Among various natural Fibers , banana Fiber an agricultural waste derived from the pseudo-stem of banana plants—has gained considerable attention due to its high tensile strength, low density, biodegradability, and wide availability. This review paper presents a comprehensive evaluation of banana Fiber as a reinforcing material in concrete, focusing on its influence on mechanical properties, durability performance, and structural behaviour . The study systematically examines the physical, chemical, and mechanical characteristics of banana Fiber, highlighting its lignocellulosic composition and its impact on Fiber-matrix interaction. The fundamental reinforcement mechanisms, including crack bridging, stress transfer, and energy absorption, are discussed in detail to explain the improvements observed in concrete performance. The incorporation of banana Fiber has been shown to significantly enhance tensile strength, flexural strength, toughness, and ductility of concrete, while moderately influencing compressive strength. These improvements are primarily attributed to the Fiber’s ability to control crack initiation and propagation, thereby enhancing post-cracking behaviour and load-carrying capacity. In addition to mechanical performance, the durability aspects of banana Fiber reinforced concrete are critically reviewed. The presence of Fibers reduces crack width and permeability, thereby limiting the ingress of harmful agents such as water, chlorides, and sulphates. However, the hydrophilic nature of banana Fiber leads to increased water absorption and potential degradation over time, which may adversely affect long-term durability. To address these challenges, various treatment methods such as alkali treatment, silane treatment, and surface coating are explored, demonstrating improved Fiber-matrix bonding, reduced moisture absorption, and enhanced durability performance. The paper also analyses the influence of key parameters such as Fiber content, length, and aspect ratio on the overall behaviour of concrete. It is observed that an optimum Fiber content in the range of 0.5% to 2% by weight of cement provides the best balance between strength enhancement and workability. Beyond this range, issues such as Fiber agglomeration, reduced workability, and increased porosity may lead to a decline in performance. The role of admixtures, particularly superplasticizers, in improving workability and ensuring uniform Fiber dispersion is also discussed. Furthermore, this review highlights the potential of banana Fiber in structural applications such as beams, slabs, pavements, and precast elements, where improved toughness and crack resistance are essential. A comparative analysis with conventional synthetic Fibers emphasizes the environmental and economic advantages of banana Fiber, while also acknowledging its limitations, including lack of standardization and long-term performance data.

Ayurveda describes Sira as one of the vital anatomical structures responsible for the transport and circulation of body constituents essential for sustenance of life. Classical Ayurvedic texts, particularly Charaka Samhita and Sushruta Samhita, elaborate Sira as channels that carry Rakta, Rasa, and other vital elements throughout the body, playing a crucial role in nourishment, waste removal, and maintenance of physiological balance. Sira are counted among the Dashapranayatana and are considered Avedhya (non-puncturable) at specific sites due to their life-sustaining importance. The detailed description of Sira Vibhajana (classification), Sira Sankhya (number), and Sira Vedhana (venesection) reflects a sophisticated understanding of circulatory dynamics in ancient Ayurvedic science.In contemporary anatomy, the vascular system—comprising arteries, veins, and capillaries—performs functions analogous to those attributed to Sira, including circulation of blood, oxygen, nutrients, and metabolic by-products. This critical review attempts to correlate the Ayurvedic concept of Sira with the modern vascular system by analyzing structural, functional, and clinical similarities. The review highlights that although Ayurveda does not explicitly differentiate arteries and veins in the modern sense, the functional attributes of Sira closely resemble venous and capillary networks, particularly in the context of Rakta circulation and therapeutic bloodletting procedures. Understanding this correlation not only bridges traditional Ayurvedic anatomy with modern biomedical science but also enhances the clinical relevance of Ayurvedic procedures such as Siravedha. The integrative interpretation of Sira provides a broader perspective on the ancient conceptualization of circulation and underscores the scientific depth of Ayurvedic anatomical knowledge.

Herbal anticancer drugs are mostly made from Cantharanthus roseus because the way these anticancer drugs work is important. When cells in the body grow abnormally, it can lead to death. It is the biggest health problem in both developed and developing countries. Some cancers can be prevented using chemopreventive agents, many of which are natural products that are effective in stopping the process of cancer formation. There are many medicines available in the market that are used to treat different types of cancer, but there is no single drug that has been proven to be completely effective against cancer. These days, people are more likely to use traditional and herbal medicines as part of cancer treatment. Most of the chemotherapy drugs used today for cancer are known to cause resistance, are not selective and can harm normal cells, and are limited by serious side effects that restrict the dose given. So, finding a cure for cancer and developing new drugs for this disease is still a big challenge in medicine. However, plants are a great source of natural products that have biological activity. They are popular because the materials are easy to find, they are affordable, they are not very expensive, they have fewer or no side effects, and they have a wide range of benefits and effective healing properties, which has helped to push forward scientific studies. In this review, we have summarized the vinca plant and its ability to fight cancer.

The Student-Alumni Platform is an educational community designed to develop connections between students alumni of an esteemed institution. It involves the current students and passed out alumni of the institution. This platform is helpful to make the students network, seek mentorship and have access to development resources which help the student to grow and learn. The alumni can offer guidance,share job opportunities,and contribute to their institution and students.By using this platform, the gap between student and alumni is solved and provides a seamless and interactive interface. The necessary features that are included in the platform are creating user profile, login, student dashboard, alumni dashboard with sending connection requests and messages. The platform is built by using HTML,CSS and JavaScript.As the platform evolves, future enhancements will further enrich user engagement by using more enhanced features like advanced messaging and group chats, linkedin integration,mobile access,and more features to help make the application more dynamic and dependant to the users.It also helps in building life long relationships by promoting the connections between student and alumni. Index Terms- Student, alumni, institution, education, connections.

Digital libraries are widely adopted in higher edu- cation, yet most production systems still assume keyboard-driven navigation, manual browsing, and visually intensive interaction. This paper presents E-lib Voice, a prototype voice-centric e- library framework that integrates speaker-aware access control, local speech processing, retrieval-augmented question answering, automated PDF ingestion, and adaptive recommendation within a Django-based architecture. The system combines browser-side voice activity detection, Resemblyzer-based speaker verification, faster-whisper transcription, TF-IDF with Logistic Regression for intent detection, a rule-guided smart router, Pinecone-backed retrieval, and large language model generation through Open- Router. Rather than claiming benchmark superiority, the paper frames the system as an engineering prototype for studying how voice-first interaction can be layered onto digital library workflows while preserving factual grounding and modular main- tainability. Mathematical formulations are provided for biometric similarity, intent estimation, retrieval scoring, and recommen- dation inference. Because evidence remains prototype-oriented, evaluation is framed through scenario validation, module-level testing, and a future quantitative assessment framework.

Salbutamol, which people also call albuterol and buy under names like Ventolin, is a medicine that helps open up the bigger air passages in your lungs. It works by relaxing the muscles around these airways. Doctors use it to help people with asthma, whether it's an ongoing condition, a sudden attack, or even just when they get out of breath from exercising. It's also used for COPD, a lung condition. Sometimes, it's used to deal with really high potassium in the blood too. You'll typically get salbutamol through an inhaler or a nebulizer, but it also comes in pill, liquid, and IV forms. If you breathe it in, it usually starts working in about 15 minutes and keeps working for a few hours.

Multilingualism has been recognized as a powerful pedagogical and cognitive tool in the National Education Policy 2020, particularly in paragraphs 4.11 to 4.22, which emphasize the role of language in promoting equitable, inclusive, and holistic education. This article examines the concept of multilingualism and the power of language in the special perspective of National Education Policy 2020, highlighting its implications for teaching-learning processes, cognitive development, cultural preservation, and national integration. The policy underscores that language is not merely a means of communication but a fundamental instrument of thought, identity formation, and knowledge construction. It advocates the use of the mother tongue, home language or regional language as the medium of instruction at least until Grade 5 and preferably until Grade 8 and beyond, recognizing that children learn best in familiar linguistic environments.
The article analyses how multilingual education enhances conceptual understanding, strengthens cognitive abilities, and reduces learning barriers, especially in early childhood and foundational stages. National Education Policy 2020 emphasizes flexible implementation of the three-language formula, ensuring that at least two of the three languages are Indian languages while allowing states and learners freedom of choice. The policy also recommends the development of bilingual teaching-learning materials, particularly in subjects such as science and mathematics to facilitate conceptual clarity and academic proficiency. Furthermore, the introduction of the “Languages of India” project promotes experiential learning by encouraging students to explore the linguistic diversity of the country. The article also highlights the promotion of classical Indian languages, including Sanskrit and the availability of foreign languages at the secondary level to develop both cultural rootedness and global competence. Innovative pedagogical approaches such as storytelling, theatre, music, and digital tools are emphasized to make language learning engaging and meaningful. Additionally, the policy recognizes Indian Sign Language as a standardized language and recommends curriculum development to ensure inclusive education for children with hearing impairments.
The discussion further explores the educational significance of multilingualism, including its role in promoting inclusive education, preserving linguistic diversity, strengthening cultural identity, encouraging creativity, and fostering national unity. Multilingual classrooms support collaborative learning, peer interaction, and democratic participation, thereby enhancing communication skills and social harmony. However, the article also addresses challenges in implementing multilingual education, such as shortage of multilingual teachers, limited availability of regional language textbooks, need for teacher training, administrative coordination, and multilingual assessment practices. To overcome these challenges, the article suggests strategies including teacher training in multilingual pedagogy, development of bilingual resources, use of technology-based translation tools, community participation, establishment of language resource centers and promotion of Indian languages in higher education. The article concludes that multilingualism, as envisioned in National Education Policy 2020, serves as a transformative approach to education that values linguistic diversity while promoting unity and global readiness. Effective implementation of these provisions can lead to improved learning outcomes, inclusive classrooms, and culturally responsive education aligned with the goals of the National Education Policy 2020.

This paper examines how Hindutva ideology evolved from Savarkar’s intellectual foundations into a politically actionable programme under Hindu Mahasabha leadership. It argues that Savarkar supplied a conceptual blueprint for nationhood by redefining “Hindutva” as a civilizational and ethno-cultural identity, thereby reframing belonging as a boundary-making political principle rather than a merely cultural disposition. While this theoretical redefinition laid the ideological groundwork, Hindu Mahasabha leadership transformed Hindutva into political praxis through organizational consolidation, electoral strategy, and mass mobilization. In this process, identity-based ideology was translated into institutional contestation: it shaped leadership networks, legitimized claims to represent “the nation,” and influenced the terms on which authority and public policy could be negotiated. Ultimately, the paper demonstrates that the shift from conceptual nation-making to organizational power-building enabled Hindutva to move from ideological discourse to durable political practice.

The anxiety in foreign language classrooms is also a well-known emotional impediment that could adversely affect the involvement, self-confidence, and general language study of learners. Over the past few years, the incorporation of social-emotional learning (SEL) principles in language instruction has become a popular topic of debate as the possible means of handling the emotional issues in second language classrooms. Nevertheless, the current studies on SEL-based interventions in the management of foreign language anxiety are still disjointed in various studies and educational settings. Thus, the research is aimed at systematically reviewing existing literature to determine and discuss social-emotional learning strategies in foreign language classrooms to address language anxiety, and synthesise the major themes about the role of these strategies in eliminating anxiety and enhancing engagement in learners. The methodology of this research was a systematic literature review and thematic analysis based on qualitative, inductive approach. The database searches in Scopus, Web of Science, ERIC, and Google Scholar returned 22 peer-reviewed articles released between the years 2015 and 2025 that met the predetermined inclusion criteria. Thematic analysis was applied to the selected studies to determine patterns and pedagogical practices that are repeated in the context of SEL in the language learning process. The analysis has identified six major themes that explain how SEL strategies facilitate anxiety management in language classrooms, including: supportive teacher behaviour, mindfulness, emotional regulation strategies, collaborative learning and peer support, reflective practices and emotional self-awareness, growth mindset and learner self-efficacy and positive classroom climate and learner engagement. These results mean that teaching methods based on emotional support can be significant in helping learners to build confidence and minimize psychological obstacles to language acquisition. On the whole, the paper shows that the application of SEL principles to language instruction can go a long way towards the establishment of conducive learning conditions that can help minimize foreign language anxiety and ensure active learning among learners.

The measurement of work ethic in non-Western contexts has relied heavily on traditional survey instruments developed in Western cultural settings. However, the validity of such instruments for capturing the lived reality of Ghanaian work values remains largely unexamined. This qualitative comparative study investigates how findings about Ghanaian work ethic differ when measured through traditional surveys versus focus group discussions, and assesses the methodological impact of each approach. Using a purely qualitative comparative design grounded in an interpretive philosophical approach, the study draws on Cultural Measurement Theory and Social Desirability Response Theory. Forty Ghanaian professionals participated in two sequential data collection phases: first, a traditional Likert-scale survey adapted from Western work ethic instruments; second, focus group discussions with the same participants. Thematic analysis following Braun and Clarke (2006) revealed five major themes of methodological divergence: (1) survey responses favoring socially acceptable punctuality claims contradicted by focus group accounts of fluid time norms, (2) individualistic accountability endorsement on surveys versus relational accountability narratives in focus groups, (3) hierarchical egalitarianism expressed in surveys contradicted by lived deference in focus groups, (4) work-life separation preferences on surveys versus integrated realities in focus groups, and (5) the survey as a performance of professionalism versus the focus group as a site of cultural authenticity. Direct quotations from focus group participants illuminate how survey methods systematically overreport Western-aligned work values and underreport contextually embedded Ghanaian work practices. The findings inform methodological choices for cross-cultural work ethic research and human resource assessment in Ghanaian organizational contexts.

Dexamethasone is widely used in oral and maxillofacial surgery to manage postoperative pain, swelling, and trismus, particularly following third molar extractions. As a potent corticosteroid, it mitigates inflammatory responses by inhibiting the release of key mediators, thereby enhancing patient comfort and recovery. Multiple routes of dexamethasone administration exist, and optimizing the method, dosage, timing, and clinical parameters is essential for effective postoperative care. This article aims to provide evidence-based guidance on the most appropriate strategies for dexamethasone use in third molar surgeries.

The AI-Based Person Detection and Alert System is a smart surveillance solution developed using Python on a Raspberry Pi to monitor restricted areas. The system captures real-time video through a camera module and processes it using Python-based libraries for image analysis. A lightweight deep learning model such as MobileNet SSD is implemented using TensorFlow Lite and OpenCV for efficient human detection. The system identifies unauthorized entry by defining a specific region of interest within the monitored area. When a person is detected inside the restricted zone, the system immediately triggers alerts such as sound alarms, email notifications, or mobile messages. Python is used for integrating all components including video processing, detection logic, and alert mechanisms. The system is designed to be cost-effective, energy-efficient, and suitable for continuous monitoring. It reduces the need for manual supervision while improving security and response time. The solution can be further enhanced with features like facial recognition, cloud connectivity, and real-time data logging. Future improvements may include IoT integration and advanced analytics for better accuracy. Overall, the system offers an automated, reliable, and scalable approach to security using Python-based implementation.

Introduction: Chronic anemia linked to iron deficiency and kidney disease is a big global health issue, often needing lifelong iron supplements. Oral iron pills don't work well because of poor absorption and stomach problems, and IV iron sucrose, while effective, requires hospital visits and comes with risks from quick iron exposure. Materials and Methods: This review looks at iron sucrose-loaded dissolving microneedles as a simple skin-based delivery option. We examined studies on iron sucrose properties, microneedle materials, how they're made, and testing methods. Results: Iron sucrose seems perfect for microneedles—it's water-soluble, stable, and doesn't have protein issues. You could deliver 10–20 mg of elemental iron per patch, and using it regularly in small doses might keep iron levels steady without causing oxidative stress. Conclusion: Dissolving microneedles with iron sucrose look like a great, easy-to-use option for managing chronic anemia.

Food waste represents one of the most pressing paradoxes of contemporary food systems: nearly one-third of all food produced globally is wasted, yet millions face food insecurity daily. The neighborhood food waste exchange model has emerged as a decentralized, community-driven approach to address this paradox by facilitating the redistribution of surplus food at the local level. This research paper provides a comprehensive analysis of neighborhood food waste exchange systems, examining their operational mechanisms, sustainability impacts, policy implications, and scalability potential. Drawing upon case studies from Milan, Genoa, Ghent, and peer-to-peer digital platforms in the United Kingdom, the paper develops a multi-dimensional framework for evaluating food exchange initiatives across environmental, social, and economic dimensions. The analysis reveals that neighborhood food exchanges can generate significant environmental benefits with documented reductions of 107 tonnes of CO₂ equivalent per hub annually while simultaneously addressing food insecurity and creating social value. However, the paper also identifies critical challenges, including the rebound effect in peer-to-peer sharing (offsetting 59-94% of expected GHG reductions), logistical barriers for small retailers, and financial sustainability concerns for community-based organizations. The research concludes with evidence-based policy recommendations for scaling neighborhood food waste exchange models within circular economy frameworks.

The rapid digitization of healthcare systems has transformed the way patient information is collected, stored, and shared, leading to improved efficiency, accessibility, and quality of care. However, this digital transformation has also raised significant concerns regarding data privacy and patient confidentiality. This study examines the critical issues surrounding the protection of sensitive health information in digital healthcare environments, including electronic health records (EHRs), telemedicine platforms, and mobile health applications. It highlights the various risks such as data breaches, unauthorized access, cyberattacks, and misuse of personal health data, which can compromise patient trust and safety. The paper further explores existing legal and regulatory frameworks designed to safeguard patient information, such as data protection laws, ethical standards, and institutional policies. It emphasizes the importance of implementing robust cybersecurity measures, including encryption, authentication protocols, and secure data storage systems, to mitigate potential threats. Additionally, the role of healthcare professionals in maintaining confidentiality and adhering to ethical practices is discussed as a key component in ensuring data protection. The study also addresses the challenges faced by developing countries in balancing technological advancement with adequate privacy safeguards, including lack of infrastructure, limited awareness, and insufficient regulatory enforcement. It underscores the need for continuous monitoring, policy updates, and the integration of advanced technologies such as artificial intelligence and blockchain to enhance data security.

The rapid growth of online news media, along with the use of social media such as Facebook, Twitter, and WhatsApp, has significantly impacted the global process of information dissemination. Currently, it is possible to reach millions of people in a matter of seconds, bypassing the traditional fact-checking process, which is generally required in such situations. Misinformation is spreading rapidly through the social media network. The new technologies provide opportunities for free expression, but they also provide opportunities for the rapid spread of misinformation, disinformation, and fake news. These trends have the potential to influence a variety of results, such as public opinion, the spread of disease, financial markets, etc. Considering the speed at which information is disseminated in modern society, it is important to use sophisticated methodologies, bypassing the traditional fact-checking process.
Machine Learning (ML) and Natural Language Processing (NLP) provide data-driven, automated, and scalable approaches to address this problem [1], [4], [5]. In the current study, three conventional machine learning algorithms, Support Vector Machines (SVM), Naïve Bayes (NB), and Random Forest (RF), were examined for binary classification of fake news and legitimate news. Machine learning algorithms were found to perform well in conventional fake news detection problems involving text data [4], [5], [6]. The data set contains 44,898 labeled data points, which is sufficient to develop an effective model. A range of natural language processing operations were performed during data preprocessing, which were found to be effective for text data classification, as reported by previous studies [1], [4].
In order to measure the efficiency of the models, various parameters were used, out of which the most important was the accuracy level of the models. Among the models used, the highest accuracy level was obtained by the Random Forest model, for which the accuracy level was 99.77%. The next highest accuracy level was obtained by the Support Vector Machine model, for which the accuracy level was 99.56%. The Naive Bayes model had an accuracy level of 93.16%. The high level of accuracy obtained by the first two models in identifying fake news is due to the efficiency of the SVM model in making use of the TF-IDF feature in a high-dimensional space, in addition to its ability to generate results with minimal overfitting, making it more efficient than the others, as supported by the results for the efficiency of the models used in the study, as provided in references [4], [5].
The appropriateness of the support vector machine model was further demonstrated in relation to its deployment in the problem domain. The results further confirm the viability of traditional machine learning approaches in the classification of fake news, especially in consideration of emerging trends in the deployment of deep learning models in this classification problem [2], [5].

Cosmetic science has evolved from a traditional practice of beautification into a multidisciplinary field integrating dermatology, biotechnology, and material sciences. The emergence of cosmeceuticals has redefined cosmetic products by incorporating bioactive ingredients capable of interacting at the cellular level. This review highlights key developments in skin and hair biology, mechanisms of action of cosmetic products, classification systems, and regulatory frameworks. Special emphasis is placed on advanced technologies such as nanocarrier-based delivery systems, artificial intelligence-driven personalization, and sustainable green chemistry approaches. Furthermore, safety evaluation, toxicological considerations, and global regulatory requirements are discussed. The cosmetic industry is increasingly shifting toward evidence-based, personalized, and environmentally sustainable solutions. Future innovations are expected to focus on skin longevity, microbiome balance, and biotechnology-derived actives, establishing cosmetic science as a bridge between aesthetics and therapeutic care. (1)

Sunscreens are widely used cosmetic products designed to protect the skin from harmful ultraviolet (UV) radiation emitted by the sun. Continuous and excessive exposure to sunlight is associated with various adverse effects on the skin, including sunburn, premature aging (photoaging), hyperpigmentation, immune suppression, and an increased risk of skin cancers such as melanoma and non-melanoma skin cancers. To minimize these harmful effects, sunscreen formulations have become an essential component of modern skincare routines. These products function by absorbing, reflecting, or scattering ultraviolet radiation before it penetrates the deeper layers of the skin, thereby reducing cellular damage and maintaining skin integrity.
Sunscreens are broadly classified into chemical (organic) sunscreens, which absorb UV radiation, and physical (inorganic or mineral) sunscreens, which reflect and scatter UV rays. Many modern formulations combine both types to provide broad-spectrum protection against UVA and UVB radiation. The effectiveness of sunscreen products depends on several factors, including the sun protection factor (SPF), formulation type, photostability of active ingredients, and proper application by the user. Various dosage forms such as creams, lotions, gels, sprays, sticks, and emulsions are developed to enhance user compliance and suit different skin types.

The global pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in significant morbidity, mortality, and socioeconomic disruption worldwide. Despite the availability of vaccines, breakthrough infections and the emergence of new variants have highlighted the continued need for effective antiviral therapies. Paxlovid, an oral antiviral combination of nirmatrelvir and ritonavir, has emerged as a promising therapeutic intervention for the management of COVID-19, particularly in high-risk populations. Nirmatrelvir functions as a potent inhibitor of the SARS-CoV-2 main protease (Mpro, also known as 3CLpro), a critical enzyme required for viral replication. By inhibiting this protease, nirmatrelvir prevents the cleavage of viral polyproteins into functional proteins, thereby halting viral replication. Ritonavir, originally developed as an HIV protease inhibitor, is included in the combination primarily as a pharmacokinetic enhancer; it inhibits cytochrome P450 3A4 (CYP3A4), which slows the metabolism of nirmatrelvir and ensures therapeutically effective plasma concentrations. This synergistic mechanism allows for potent antiviral activity with convenient oral administration, enabling outpatient use early in the disease course.

The escalating environmental crisis caused by persistent plastic waste has catalyzed intensive research into biodegradable alternatives. Biopolymer nanocomposites represent a promising solution, combining the inherent biodegradability of natural polymers with enhanced mechanical, thermal, and barrier properties conferred by nanoscale fillers. This review examines the current state of eco-friendly biopolymer nanocomposites, focusing on their synthesis, characterization, properties, applications, and environmental fate. We explore various biopolymer matrices including polylactic acid (PLA), polyhydroxyalkanoates (PHAs), starch, cellulose, and protein-based polymers, along with nanofillers such as cellulose nanocrystals, nanoclays, and chitosan nanoparticles. The review critically analyzes the biodegradation mechanisms, life cycle assessments, and current challenges limiting widespread commercialization, while highlighting future research directions toward truly sustainable plastic alternatives.

The rapid digitisation of higher education has increased the demand for scalable, instantaneous, and personalised student support services. Traditional help desks and FAQ pages often fail to address the diverse, context‑sensitive queries of students, leading to delays and dissatisfaction. This research paper presents the design, development, and evaluation of an AI‑powered chatbot for university information and student support. The chatbot leverages Natural Language Processing (NLP) techniques, specifically a Transformer‑based intent recognition model (BERT fine‑tuned for educational queries), combined with a retrieval‑augmented generation (RAG) pipeline to provide accurate, context‑aware responses. The system integrates with the university’s knowledge base (course catalogues, policies, timetables, campus maps) and supports multi‑turn conversations. Evaluation metrics include intent classification accuracy, response relevance (using BLEU and ROUGE scores), user satisfaction (Likert scale), and task completion rate. A pilot deployment with 500 students over four weeks demonstrated an intent classification accuracy of 94.2%, a task completion rate of 89%, and a mean user satisfaction score of 4.3/5. The paper also discusses challenges such as handling out‑of‑scope queries, multilingual support, and integration with existing student information systems. Future directions include emotion‑aware responses and proactive academic advising.

Traffic congestion is a growing challenge in urban environments, leading to economic losses, increased fuel consumption, and environmental pollution. Accurate short‑term traffic prediction is essential for intelligent transportation systems (ITS) to enable proactive traffic management, route guidance, and congestion mitigation. This research paper proposes a deep learning framework based on Long Short‑Term Memory (LSTM) networks to predict traffic flow using historical time‑series data from loop detectors. The methodology includes data preprocessing (missing value imputation, outlier removal, normalization), sequence construction, and model training. The LSTM model is evaluated using real‑world traffic data from a major highway, with performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that the LSTM model significantly outperforms baseline methods such as historical average and ARIMA, capturing both daily and weekly periodic patterns. The paper also discusses practical deployment considerations, limitations, and future directions for multi‑source data fusion and graph neural networks.

The rapid advancement of generative artificial intelligence has revolutionized the field of medical imaging, particularly in the domain of automated radiology report generation. With radiologists facing unprecedented workloads due to the exponential growth in medical imaging volumes, generative AI systems offer a promising solution to alleviate clinical burden, reduce diagnostic variability, and improve patient care. This research paper presents a comprehensive analysis of state-of-the-art generative AI approaches for medical image report generation, examining the technological foundations, architectural frameworks, evaluation methodologies, and clinical translation challenges. The paper systematically reviews deep learning architectures including Transformer-based models, vision-language models (VLMs), contrastive learning frameworks, and large language models (LLMs) that have been applied to generate diagnostic narratives from medical images across multiple modalities chest X-ray, CT, MRI, and ultrasound. Through a detailed examination of recent advances including disease-aware dual-stage frameworks, retrieval-augmented generation, category-wise contrastive decoding, and generalist foundation models, this study provides quantitative performance comparisons across benchmark datasets (MIMIC-CXR, IU-Xray, CheXpert) using standardized evaluation metrics including BLEU, ROUGE, CHEXBERT, RadGraph F1, and GREEN. The paper concludes with a proposed clinical validation framework and offers recommendations for future research directions in multimodal integration, hallucination mitigation, and real-world deployment.

Navigation and obstacle detection for visually impaired individuals remains one of the most critical challenges in assistive technology research. With over 43 million people worldwide living with severe visual impairment and approximately 2.2 billion individuals experiencing some form of vision impairment, the need for effective, affordable, and real-time navigation assistance has never been more pressing. This research paper presents a comprehensive analysis of the technological foundations, architectural frameworks, and performance evaluation methodologies for smartphone-based navigation and obstacle detection applications designed to assist visually impaired users. The paper systematically examines the integration of deep learning-based object detection models particularly the YOLO (You Only Look Once) family of algorithms with multimodal sensor fusion techniques incorporating LiDAR, depth cameras, ultrasonic sensors, and inertial measurement units. Through a detailed examination of recent advances including YOLOv8, YOLOv11, SGBM_YOLO, and various sensor fusion architectures, this study provides quantitative performance comparisons, mathematical formulations for obstacle distance estimation, and empirical evaluation data drawn from recent field studies with blind and low-vision (BLV) participants. The paper concludes with a proposed system architecture that achieves 94–96% detection accuracy with sub-second latency, and offers recommendations for future research directions in edge-AI integration, semantic scene understanding, and multimodal feedback optimization.

Social media and Library 2.0 have revolutionized how people access, engage with, and exchange information in the digital age. In order to improve information literacy and promote knowledge sharing, this article investigates how social media might be integrated into Library 2.0. It looks at the integration's theoretical foundations, real-world applications, and difficulties. Through the evaluation of different digital tools, platforms, and tactics used by libraries, this article offers a paradigm that promotes user engagement, information literacy, and cooperative learning. According to the findings, social media and Library 2.0 work together to provide a dynamic, inclusive, and interactive knowledge environment that meets the needs of modern learners.

Artificial intelligence (AI) has emerged as a transformative technology that integrates human intelligence with advanced computational capabilities to address complex challenges in the pharmaceutical field. In recent years, AI has significantly influenced drug discovery, formulation development, and the evaluation of pharmaceutical dosage forms. By enabling the analysis of large and complex datasets, AI facilitates faster identification of potential drug candidates and improves the efficiency and success rate of drug development and approval processes.
Furthermore, AI-driven approaches help reduce research and development costs through process optimization and predictive modeling. The integration of AI in personalized medicine allows for the analysis of patient-specific data, leading to improved therapeutic outcomes, enhanced treatment precision, and better patient adherence.
This review article highlights the diverse applications of AI in pharmaceutical technology, including drug discovery, drug delivery system design, process optimization, and pharmacokinetic and pharmacodynamic studies. It also discusses the advantages, limitations, and future potential of AI-based methodologies. Continued advancements and investments in AI are expected to further revolutionize the pharmaceutical industry and open new avenues for innovation.

The landscape of modern music recommendation is currently dominated by proprietary "Black Box" algorithms that excel in accuracy but remain inaccessible for research. This project addresses this critical "Precision Gap" by developing a novel, high-performance music classification engine built on the ideological foundation that signal is truth. By implementing a 3-second segmentation strategy, the model effectively expands the feature space by 10x compared to traditional 30-second averaging techniques. By extracting 58 distinct "Acoustic DNA" parameters, the system analyzes the texture and timbre of audio with a level of fidelity previously reserved for enterprise-grade systems. The resulting model utilizes optimized machine learning kernels to classify genres based purely on acoustic content, surpassing the precision of standard metadata-based models and demonstrating that high-fidelity, transparent music classification is achievable on a local scale.

Moringa oleifera, commonly known as the drumstick tree or horseradish tree, is a flowering plant belonging to the Moringaceae family. It is native to the sub-Himalayan regions of northern India, Pakistan, Bangladesh, and Afghanistan. This plant grows well in tropical and subtropical climates. In Nepal, regions such as the Terai, Siwalik, and mid-hills provide suitable environmental conditions for its cultivation.
Often called the “Miracle Tree,” Moringa is valued for its wide range of practical uses, high nutritional content, and potential health benefits, including its role in preventing and managing various diseases. It is a rich source of essential amino acids, vitamins, minerals, and protein, making it especially beneficial for populations with limited nutritional intake, such as rural communities in Nepal. Pregnant women and infants, in particular, can benefit from its nutrient-dense leaves.
Almost every part of the Moringa plant has useful applications, making it a promising subject for further scientific research and development. This review aims to highlight the nutritional, medicinal, and commercial importance of Moringa, examine its cultivation potential in Nepal, suggest future research and market opportunities, and analyze existing scientific literature on its diverse uses.

Depression is a complex and multifactorial psychiatric disorder that significantly contributes to global disability and reduced quality of life. It is characterized by persistent sadness, loss of interest or pleasure, and cognitive and physical impairments. This review provides a comprehensive overview of depression, focusing on its epidemiology, etiology, pathophysiology, clinical features, diagnosis, and management strategies. Special emphasis is placed on the neurobiological mechanisms underlying depression and the integration of pharmacological and non-pharmacological treatment approaches. Understanding the multifaceted nature of depression is essential for improving diagnosis, treatment, and prevention strategies.

Sunscreens are essential dermatological and cosmetic preparations developed to protect the skin from the harmful effects of ultraviolet (UV) radiation. Continuous exposure to UV radiation can lead to acute and chronic skin damage, including erythema, pigmentation, premature aging, immunosuppression, and carcinogenesis. Sunscreens function by absorbing, reflecting, or scattering UV rays, thereby minimizing their penetration into the skin. This review manuscript provides a comprehensive discussion of ultraviolet radiation, classification of sunscreens, mechanisms of action, formulation strategies, evaluation techniques, regulatory considerations, safety concerns, and recent advancements such as nanotechnology and herbal photoprotective agents. With increasing awareness regarding skin health and environmental sustainability, sunscreen formulations have undergone significant improvements in safety, efficacy, and consumer acceptability.

Polycystic ovary syndrome (PCOS) is a multifactorial disorder traditionally recognized for its endocrine and metabolic disturbances. However, accumulating evidence indicates that chronic low-grade inflammation and immune dysregulation play a central role in its pathophysiology. This narrative review explores the immunological mechanisms underlying PCOS and their repercussions for reproductive outcomes. Women with PCOS exhibit increased circulating levels of inflammatory cytokines-including interleukin (IL)-6, IL-1β, IL-18, and tumor necrosis factor-alpha (TNF-α)-alongside macrophage and lymphocyte infiltration within ovarian and endometrial tissues. These alterations promote oxidative stress, granulosa cell apoptosis, impaired steroidogenesis, and diminished endometrial receptivity. The adaptive immune response is similarly altered, characterized by imbalances between Th1/Th2 and Th17/regulatory T lymphocyte subsets and the presence of autoantibodies, including antinuclear, antithyroid, and antiphospholipid antibodies. These immune abnormalities have been associated with implantation failure and recurrent pregnancy loss. Furthermore, autoimmune diseases, including Hashimoto's thyroiditis and type 1 diabetes mellitus occur more frequently among women with PCOS, reinforcing the concept of an underlying systemic immunometabolic imbalance. Unlike prior reviews, this work offers a novel integrative framework that connects systemic inflammation with localized immune dysregulation in reproductive tissues, providing a more comprehensive understanding of PCOS pathophysiology from an immunological standpoint.

Recent advancements in pharmaceutical research have significantly focused on the development of novel drug delivery systems (NDDS) to overcome the limitations associated with conventional dosage forms. Traditional systems such as tablets and capsules often suffer from poor bioavailability, frequent dosing requirements, systemic side effects, and reduced patient compliance. NDDS aim to address these challenges by enhancing drug stability, improving therapeutic efficacy, and minimizing toxicity.
Innovative approaches such as orally disintegrating films, self-emulsifying drug delivery systems, nanoparticles, and targeted delivery carriers have demonstrated significant improvements in drug absorption and site-specific action. These systems are particularly beneficial for pediatric and geriatric patients due to ease of administration and improved safety. Furthermore, NDDS help maintain drug concentration within the therapeutic range for extended periods, ensuring better treatment outcomes. This review discusses the design principles, classification, types, applications, and future perspectives of NDDS, emphasizing their role in modern therapeutics.

Sexually transmitted diseases (STDs), also known as sexually transmitted infections (STIs), continue to represent a major global public health issue. These infections are primarily transmitted through sexual contact, including vaginal, oral, and anal intercourse, but can also spread through non-sexual routes such as vertical transmission from mother to child, blood transfusions, and shared needles. STDs are caused by a wide range of pathogens including bacteria, viruses, parasites, and fungi, and may lead to serious complications such as infertility, malignancies, and increased vulnerability to human immunodeficiency virus (HIV) infection.

Cancer continues to be a major global health challenge, with high rates of morbidity and mortality demanding innovative strategies for effective diagnosis and treatment. Nanotechnology has emerged as a promising field in oncology, offering advanced approaches for targeted drug delivery, improved diagnostic imaging, and enhanced therapeutic outcomes. Nanoparticles such as liposomes, dendrimers, and polymer-based carriers facilitate precise drug delivery to tumor sites, thereby minimizing systemic toxicity and improving treatment efficiency.
In addition, nanotechnology has significantly improved early cancer detection through the use of quantum dots, magnetic nanoparticles, and biosensors, leading to enhanced diagnostic accuracy. The development of theranostic systems, which integrate diagnostic and therapeutic functions into a single platform, represents a major advancement in personalized cancer treatment. Despite its potential, challenges such as toxicity, large-scale manufacturing, and regulatory complexities remain. This review discusses the mechanisms, applications, challenges, and future perspectives of nanotechnology in cancer therapy, highlighting its transformative role in modern oncology.

Colony-specific drug delivery systems (CSDDS) represent an advanced and highly precise strategy in modern therapeutics, aimed at delivering drugs selectively to specific microbial or cellular colonies such as bacterial biofilms, tumor clusters, and probiotic populations. Conventional drug delivery approaches often suffer from non-specific distribution, resulting in systemic side effects and inadequate drug concentrations at the target site. In contrast, CSDDS enhances therapeutic efficiency by employing colony-specific triggers, including quorum sensing signals, environmental conditions such as pH and oxygen levels, and ligand-mediated targeting mechanisms.

Diabetes mellitus is one of the most prevalent disorders in both developed and developing nations, and its incidence is rising quickly in many regions worldwide. It is estimated that nearly one-third of individuals with diabetes mellitus utilize some type of complementary and alternative medicine. One plant that has gained significant attention for its anti-diabetic effects is bitter melon, scientifically known as Momordica charantia (M. charantia), which is also called bitter gourd, karela, and balsam pear. The fruit of this plant is used to treat diabetes and related ailments among indigenous communities in Asia, South America, India, and East Africa.
Numerous pre-clinical studies have documented the anti-diabetic and hypoglycaemic effects of M. charantia through various proposed mechanisms. However, data from clinical trials involving human participants are scarce and often compromised by poor study design and low statistical power. This review aims to emphasize the antidiabetic properties, as well as the phytochemical and pharmacological findings related to M. charantia, and advocates for better-designed clinical trials to clarify its potential therapeutic benefits for diabetes.

Atenolol is a cardio selective -epinephrinic receptor antagonist used primarily to treat hypertension, angina pectoris, and acute myocardial infarction by decreasing heart rate and blood pressure. It controlled by blocking catecholamines at (beta 1)-receptors without intrinsic sympathomimetic activity, offering once-daily dosing. Adrenergic receptor blockers (b-blockers) are applied to treat high blood pressure, ischemic heart disease, and heart rhythm disturbances. Due to their spreading use and human metabolism, b-blocker is mostly detected in sludge and waste waters. b-Adrenergic receptors have been identify in fish and other animals, so it can be expected that physiological processes controlled by these receptors in animals may be affected by the presence of b-blockers. starting point for the assessment of potential environmental risks was the European guideline on the environmental risk assessment of medicinal products for human use. In Phase I of the risk assessment, the initial predicted environmental concentration (PEC) of atenolol in surface water (500 ng L1) exceeded the action limit of 10 ng L1 . Thus, a Phase II risk assessment was conducted showing acceptable risks for surface water, for groundwater, and for aquatic microorganisms. Furthermore, atenolol showed a low potential for bioaccumulation as indicated by its low lipophilicity

This paper examines how contemporary Japanese fiction constructs everyday life as a narrative form that sustains affective continuity without conventional resolution. Focusing on All the Lovers in the Night, Strange Weather in Tokyo, and The Housekeeper and the Professor, the study moves beyond thematic readings of “healing literature” to analyze how repetition, routine, and temporal suspension function as formal strategies. Drawing on Henri Lefebvre’s theory of everyday life as cyclical repetition, Michel de Certeau’s concept of everyday practices, and Rita Felski’s framework of recognition, the paper argues that these texts do not resolve alienation but instead sustain it through continuity. Through close readings of labour, memory, and relational structures, the study demonstrates that narrative meaning emerges not through transformation but through persistence. The ordinary thus becomes a central aesthetic principle that reshapes narrative form in contemporary Japanese fiction.

This study looks at how businesses are using Green Supply Chain Management (GSCM) to make their supply chains more environmentally friendly. In the past, companies focused mainly on cutting costs and delivering products quickly, often ignoring the impact on the environment. But now, with growing concerns about climate change, limited natural resources, and stricter environmental laws, many businesses are starting to adopt green practices.
The Research explores the reasons why companies choose to go green, the benefits they gain, the challenges they face, and how well they are putting these practices into action. To gather this information, the researchers used both surveys and interviews with supply chain experts from different industries. The results show how companies are reacting to the pressure and opportunities related to sustainability.
Key reasons for adopting GSCM include following environmental laws, saving money, meeting customer demand for eco-friendly products, and gaining an edge over competitors. Some companies are also driven by their values, like social responsibility and building a strong brand. For industries like automotive and manufacturing, where strict environmental rules apply, following regulations is especially important.
Common green practices include buying from eco-friendly suppliers, using biodegradable packaging, recycling and reusing products (reverse logistics), and using fuel-efficient transportation. These helps reduce waste, save energy, and lower pollution. For example, some companies are switching to smart transport systems to cut fuel use.

The growing urgency of environmental degradation, climate change, and resource depletion has compelled businesses worldwide to adopt sustainable practices. In this context, digital marketing and content marketing have emerged as powerful tools for promoting sustainability, influencing consumer behavior, and enhancing corporate accountability. This paper explores the role of digital and content marketing in advancing sustainable business practices by enabling transparent communication, fostering consumer awareness, and supporting ethical consumption patterns. Digital platforms provide businesses with cost-effective, scalable, and measurable channels to disseminate sustainability-related information, while content marketing helps build trust and long-term relationships with stakeholders through storytelling, education, and value-driven engagement.
Furthermore, the integration of sustainability into marketing strategies allows organizations to align their brand identity with environmental and social responsibility, thereby improving brand loyalty and competitive advantage. The study also highlights how data analytics, social media, and personalized content contribute to targeted sustainability campaigns and behavioral change among consumers. However, challenges such as greenwashing, lack of authenticity, and information overload are also discussed. Overall, digital and content marketing play a crucial role in transforming business models toward sustainability by bridging the gap between corporate initiatives and consumer expectations.

Aloe vera is a type of plant that grows in dry and warm areas. It is famous for its healing qualities and is used in different types of medicine like Ayurveda, Homoeopathy, and Western medicine. People from many different cultures have used it for a long time. Some traditional ways of using it include helping to reduce sweating, giving it by mouth to manage diabetes, and treating various stomach problems. It is also used to treat burn wounds, small cuts, genital herpes, and seborrheic dermatitis. The leaves of this amazing medicinal plant have many vitamins, minerals, natural sugars, enzymes, amino acids, and are also rich in different bioactive compounds. These compounds have several benefits, such as making the skin softer, helping to empty the bowels, reducing inflammation, protecting against damage from free radicals, fighting bacteria, getting rid of worms, preventing fungal growth, improving sexual health, killing germs, and being useful for skincare. Many cosmetic companies use this plant because it helps heal and nourish the skin. Keywords:Aloe vera, Medicinal Uses, bioactive compounds, Cosmetic industries

Self-medication is a widely practiced healthcare behaviour in India, particularly for the management of minor and self-limiting illnesses. While it offers benefits such as rapid symptom relief, reduced healthcare costs, and decreased burden on healthcare facilities, inappropriate and unregulated use poses significant public-health challenges. This review aims to provide a comprehensive overview of the prevalence, determinants, patterns, advantages, risks, and public-health implications of self-medication in India, with special emphasis on the emerging influence of artificial intelligence (AI) and digital health platforms. A narrative review of literature published till date was conducted using data from the World Health Organization, national health agencies, and peer-reviewed journals, along with findings from a recent rural-based study in Tamil Nadu. Evidence indicates that self-medication prevalence in India ranges from 30% to 80%, with common indications including cold, cough, fever, and headache. Analgesics and antibiotics are among the most frequently used drugs, with antibiotic misuse contributing significantly to antimicrobial resistance. Key drivers include perceived mildness of illness, cost constraints, time-saving behaviour, easy access to medicines, and increasing reliance on digital platforms such as search engines and AI tools. Major challenges include lack of awareness, weak regulatory enforcement, misinformation, and healthcare accessibility gaps. Strategies to ensure safe self-medication include strengthening drug regulations, enhancing public awareness, integrating pharmacists into primary care, regulating digital health tools, and improving healthcare infrastructure. Promoting responsible self-medication through a balanced approach is essential to reduce preventable morbidity and improve health outcomes in India.

Domestic violence continues to be a serious social concern affecting women across different sections of society, including urban areas where education and awareness levels are relatively higher. This study focuses on understanding how far women in urban communities are aware of domestic violence, its different forms, legal protections, and the support systems available to them. It also examines the social and cultural factors that influence their awareness and response to such issues.
The study is based on a descriptive research design, where primary data is collected through questionnaires from women living in urban areas. The findings suggest that while many women recognize physical forms of violence, there is comparatively less awareness about emotional, psychological, and economic abuse. Awareness regarding legal safeguards, especially the Protection of Women from Domestic Violence Act, exists but is not complete or clearly understood by all respondents.
The study concludes that there is a strong need to improve awareness through social work interventions, community programs, and institutional support systems. Increasing awareness can empower women to recognize abuse and seek help, thereby contributing to a safer and more equitable society.

Background: Traditional drug discovery is a protracted, resource-intensive process that frequently requires more than a decade and billions of dollars in investment, with a high incidence of late-stage candidate failure.
Objective: This review provides a comprehensive, pipeline-wide analysis of artificial intelligence (AI) applications in drug discovery, encompassing target identification, virtual screening, molecular docking, de novo design, retrosynthesis, quantitative structure–activity relationship (QSAR) modeling, ADMET prediction, spectral analysis, and clinical trial optimization.
Methods: A structured review of primary literature and computational tool documentation was conducted. Key machine learning frameworks—including deep learning, graph neural networks, generative models, and reinforcement learning—were examined alongside widely adopted software platforms such as RDKit, DeepChem, AlphaFold2, TorchDrug, ChemProp, and REINVENT.
Results: AI-driven methodologies demonstrably accelerate candidate identification and optimization, improve predictive accuracy for pharmacokinetic and toxicity endpoints, and enable exploration of previously inaccessible chemical space. Emerging paradigms such as digital twin models and AI-assisted adaptive clinical trials further extend these benefits to translational research.
Conclusion: Despite persistent challenges—including data heterogeneity, model interpretability, and regulatory uncertainty—AI is transitioning from an auxiliary tool to a central driver of pharmaceutical innovation. Continued interdisciplinary collaboration and standardized validation frameworks are essential for the full realization of AI's potential in drug development.

The proliferation of artificial intelligence has made the fabrication of hyper-realistic synthetic media both technically accessible and legally elusive. Non-consensual intimate deepfakes algorithmically generated audiovisual content that superimposes the likeness of a real person onto explicit or intimate material without consent occupy a particularly grievous corner of this problem. In India, the absence of a dedicated deepfake statute has historically compelled prosecutors and victims to navigate a patchwork of criminal provisions drawn from the Bharatiya Nyaya Sanhita, 2023 ('BNS') and the Information Technology Act, 2000 ('IT Act'), supplemented most recently by the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Amendment Rules, 2026 ('2026 IT Amendment'). This paper critically examines the adequacy of these remedies, identifies the structural gaps that persist in the post-2025 legal landscape, and advances a doctrinal argument for bespoke legislative intervention. It argues that while the 2026 IT Amendment represents a normative and procedural advance, the absence of an express criminal offence of non-consensual intimate synthetic imagery continues to expose victims disproportionately women to inadequate redressal, investigative inertia, and re-victimisation.

Physical Education (PE) has long been a component of school curricula across the globe, yet its significance in modern educational systems is increasingly contested amid rising academic pressures, standardized testing demands, and budget constraints. This paper examines the multidimensional role of physical education in contemporary schooling, synthesizing evidence from educational psychology, neuroscience, public health, and pedagogical research. The findings demonstrate that PE contributes substantially not only to physical health outcomes but also to cognitive performance, social-emotional development, mental well-being, and the cultivation of lifelong healthy habits. The paper further addresses the systemic challenges facing PE programs — including marginalization, inequitable access, and pedagogical stagnation — and proposes forward-looking recommendations for integrating physical education as a core, rather than peripheral, component of modern educational frameworks.

The Central Museum is a vital component of academic infrastructure in pharmacy institutions, particularly for Diploma in Pharmacy (D.Pharm.) and Bachelor of Pharmacy (B.Pharm.) programs, as mandated by the Pharmacy Council of India. It functions as an interactive and visual learning resource that enhances students’ understanding of pharmaceutical sciences by bridging theoretical knowledge with practical exposure. A well-designed museum facilitates experiential learning through systematic display of pharmaceutical products, models, crude drugs, instruments, and dosage forms. This short communication presents a comprehensive and practical framework for developing an effective Central Museum that is informative, attractive, and compliant with regulatory requirements. Key considerations include spatial planning, systematic block-wise categorization, effective partitioning, standardized labeling, and incorporation of modern display techniques such as digital aids and QR codes. Emphasis is also placed on creating an engaging learning environment that promotes student interaction, improves retention of knowledge, and supports self-directed learning. Furthermore, the paper highlights the role of a well-maintained museum in enhancing institutional quality, particularly during inspections by regulatory bodies. Proper organization, maintenance, and periodic updating of exhibits are essential for ensuring relevance and compliance with evolving educational standards. Overall, an efficiently structured Central Museum not only strengthens academic delivery but also serves as a hallmark of quality education in pharmacy institutions, contributing to improved student outcomes and institutional credibility.

In the last twenty years, peptide drug discovery has seen a big revival, thanks to the growing use of organic chemistry and biotechnology. These fields have been key in creating peptidomimetics that are more stable, specific, and easier for the body to absorb than traditional peptides. Peptides have been used as treatments, but they often come with problems like not being well absorbed, breaking down quickly in the body, and being removed from the system too fast. To overcome these issues, scientists create peptidomimetics, which are modified versions of natural peptides. These modified molecules are better suited for use in medicine, which is why they are commonly used in drug development programs. There's a lot of ongoing work to find new and better peptidomimetic therapies, as shown by the new compounds currently being tested in clinical trials. Over time, new methods have been developed to make these peptidomimetics. This overview looks at recent advances in designing and making peptidomimetic drugs, as well as how they work against different targets. The development of peptidomimetics represents a dynamic frontier in medicinal chemistry. By addressing the inherent limitations of native peptides, peptidomimetics provide drug candidates with improved stability, receptor affinity, and selectivity. Synthetic strategies—including β-turn mimetics, constrained scaffolds, and hybrid peptide–small molecule constructs—have expanded the chemical space available for drug discovery. These approaches allow fine-tuning of conformational flexibility and pharmacological profiles, making peptidomimetics a versatile toolbox for targeting protein–protein interactions and enzyme inhibition. Their adaptability positions them as promising leads in therapeutic areas ranging from metabolic disorders to neurodegenerative diseases.

The impact of the Goods and Services Tax (GST) on Micro, Small and Medium Enterprises (MSMEs) in Hyderabad was examined using secondary data for the period 2021–2025. Quantitative indicators such as registration counts, employment totals, working-capital requirements, GST compliance costs, ITC refund delays, and export turnover were analysed. Statistical techniques including CAGR, correlation, ANOVA and simple linear regression were applied and sectoral differences across manufacturing, services and trade were evaluated.
It was found that formalisation was accelerated through Udyam registration and government schemes, employment generation was closely associated with MSME growth, and interstate trade and exports expanded. Simultaneously, working-capital intensity and monetary compliance costs were observed to have increased while ITC delays and filing time were reduced. Policy measures to reduce compliance costs for micro units, accelerate ITC automation and provide sector-specific credit support were recommended.

Peptic ulcer disease is a common gastrointestinal disorder characterized by the formation of sores in the lining of the stomach, duodenum, or esophagus. These ulcers occur when the protective mucosal barrier is disrupted, allowing gastric acid and digestive enzymes to damage underlying tissues. Peptic ulcers are broadly classified into gastric ulcers, which develop in the stomach, and duodenal ulcers, which occur in the upper part of the small intestine. The condition affects millions of people worldwide and is associated with significant morbidity and healthcare costs.
Historically, peptic ulcers were believed to result primarily from stress and spicy foods. However, scientific research has revealed that infection and medication use play more significant roles. The discovery of bacterial involvement revolutionized the understanding and management of peptic ulcer disease. Today, treatment strategies include antibiotic therapy, acid suppression, and lifestyle modifications.
Peptic ulcers can lead to serious complications if left untreated. These complications include gastrointestinal bleeding, perforation, and obstruction. Early diagnosis and proper management are essential to prevent adverse outcomes. Understanding the causes, symptoms, and treatment options is crucial for healthcare professionals and patients alike.

This research paper examines the dialectical relationship between crime prevention strategies and the function of criminal law as a mechanism for maintaining public order. Moving beyond the traditional retributive and deterrent models, this paper argues that effective public order maintenance requires a synergistic approach where criminal law acts not merely as a reactive sanctioning tool but as a proactive normative guide. The paper explores several key theoretical frameworks Classical, Positivist, and Critical to establish the philosophical foundations of crime prevention. Subsequently, it analyzes modern preventative models, including Situational Crime Prevention (SCP), Community Policing, and Public Health approaches, evaluating their legal and ethical implications. A central thesis is that criminal law maintains public order through three distinct functions: deterrence (specific and general), incapacitation (physical and legal), and norm-reinforcement (expressive theory). However, the paper also critically assesses the limits of law, including the risks of net-widening, over-criminalization, and the potential erosion of civil liberties in the name of prevention. Through a comparative analysis of case studies such as the "Broken Windows" policing model in New York and the preventative detention frameworks in European counter-terrorism law the paper concludes that the most sustainable public order is achieved not through punitive severity but through legal frameworks that prioritize legitimacy, proportionality, and social inclusion. The final argument posits that criminal law’s primary preventive power lies in its capacity to articulate shared social values, thereby fostering voluntary compliance.

By 2026, the cancer gene therapy market, estimated at around US$ 2.8 billion, highlights a major transition from symptom-focused care to precise, potentially curative molecular strategies. Earlier methods largely relied on viral-mediated gene addition—using vectors such as adenoviruses or lent viruses to deliver functional genes. However, these approaches are now being overtaken by next-generation genome editing tools. Techniques like Base Editing and Prime Editing have introduced a new level of precision, allowing direct correction of genetic mutations without creating double-stranded DNA breaks, thereby enhancing both safety and effectiveness.
At the same time, delivery technologies have advanced considerably. Lipid Nanoparticles (LNPs) have gained prominence as a non-viral alternative due to their lower immunogenicity, scalability, and success in delivering nucleic acids in clinical settings. In parallel, the application of artificial intelligence to engineer synthetic viral capsids has improved targeting accuracy and gene transfer efficiency, overcoming many traditional delivery barriers.
The years 2025–2026 have marked important clinical achievements, including the approval of innovative therapies such as Zongertinib and Sevabertinib. These developments reflect growing regulatory trust in precision oncology and gene-based treatment modalities.
As these therapies become more complex, pharmacists are taking on expanded responsibilities in managing Advanced Therapy Medicinal Products (ATMPs). Their roles now include ensuring proper storage, safe handling, patient education, and ongoing safety monitoring. This evolving, collaborative healthcare approach is crucial for effectively integrating advanced gene therapies into standard clinical practice.

The entry of Generation Z into the workforce in the manufacturing sector in Tumkur has significantly transformed traditional employment relationships, compelling organizations to reassess their human resource practices to address emerging retention challenges. It exhibit distinct work values, including a strong preference for flexibility, meaningful work, rapid career advancement, continuous learning, and work–life balance. However, conventional organizational policies often fail to align with these evolving expectations, resulting in increased attrition and frequent job switching. The present study aims to examine the impact of Generation Z work values on employee retention in the manufacturing sector in Tumkur.
The major research questions addressed in this study include: What are the dominant work values of Generation Z employees in the manufacturing sector in Tumkur? How do these values influence their retention intentions? Which work value dimensions most significantly affect their decision to continue with their present organization? Study adopts a descriptive and analytical research design. Primary data were collected through a structured questionnaire administered to Generation Z employees working in selected manufacturing units in Tumkur. A convenience sampling technique was employed, and the collected data were analyzed using descriptive statistics, correlation and multiple regression techniques.
The findings reveal that work–life balance, career growth opportunities, flexibility, meaningful work, and learning and development have a significant positive impact on employee retention, whereas inadequate recognition and feedback contribute to turnover intentions. The study suggests that organizations should redesign HR policies to enhance retention.

The emergence of cryptocurrency has fundamentally transformed the global financial ecosystem by enabling decentralized, borderless, and technologically sophisticated transactions. While these innovations have significantly enhanced efficiency, accessibility, and financial inclusion, they have simultaneously created new pathways for illicit financial activities, particularly crypto-enabled money laundering. This paper undertakes a comprehensive socio-legal analysis of crypto laundering within the framework of the Prevention of Money Laundering Act, 2002 (PMLA), with specific reference to the Indian legal system.
The study argues that crypto laundering is not an entirely new category of offence but rather an evolution of traditional money laundering practices adapted to digital and decentralized environments. By integrating doctrinal analysis, empirical case studies, and comparative regulatory perspectives, the paper identifies emerging laundering typologies such as hawala-crypto hybrid systems, illegal online betting platforms, unauthorized forex trading websites, sextortion networks, and peer-to-peer stablecoin transactions.
The paper further highlights key challenges, including jurisdictional fragmentation, technological asymmetry, enforcement limitations, and the growing role of digital intermediaries such as influencers and online platforms. It critically examines the adequacy of PMLA in addressing these challenges and evaluates recent regulatory developments, including the inclusion of Virtual Digital Asset Service Providers under the AML framework.
The study concludes that while PMLA provides a broad and flexible legal foundation, its effective implementation requires enhanced technological capability, institutional coordination, and international cooperation. A balanced regulatory approach that aligns legal frameworks with technological realities is essential for combating crypto laundering in the evolving digital economy.

Hypertension is a significant and costly public health problem. It is a major, but modifiable contributor for the development of cardiovascular disease. Randomized controlled trials have shown that controlling hypertension reduces the risk of stroke, coronary artery disease, congestive heart failure, end-stage renal disease, peripheral vascular disease, as well as overall mortality. The risk of developing these hypertension-related complications is continuous, starting at a blood pressure level as low as 115/75 mm Hg. Despite the inherent health risks associated with uncontrolled hypertension, elevated blood pressure remains inadequately treated in the majority of patients. This article reviews guidelines for optimal evaluation of hypertension and current therapeutic options available to combat this common yet pervasive disease Hypertension is a common issue, impacting about one in three adults in the United States, with nearly 2 million new cases popping up each year. Additionally, around 28% of the population is in the prehypertensive range, and about 7% of those with hypertension don’t even realize they have it. Globally, over 1 billion people are living with this condition, and that number is projected to rise to 1.56 billion by 2025. It’s the leading cause of death worldwide and the second biggest contributor to lost disability-adjusted life-years. Research from randomized controlled trials shows that managing blood pressure effectively can significantly lower the risk of serious complications like stroke, coronary artery disease, heart failure, end-stage renal disease, peripheral vascular disease, and even death. Notably, the risk for these issues starts to climb at blood pressure levels as low as 115/75 mm Hg. Hypertension also puts a significant strain on healthcare systems. In the U.S. alone, the total direct and indirect costs were estimated at a staggering $73.4 billion in 2009. About $15 billion—nearly 10% of the total annual spending on medications—goes toward antihypertensive drugs. Yet, only around 34% of people with hypertension manage to hit the recommended blood pressure targets (below 140/90 mm Hg). The reasons behind this are complex and not entirely understood, and they can’t be attributed just to gaps in awareness, access to treatment, or knowledge about lifestyle changes. Since most individuals are likely to face hypertension at some point in their lives, it’s crucial to prioritize early prevention and effective management strategies, which should include both lifestyle changes and medication, to reduce the risk of related complications.

Hypertension, also called high blood pressure, is a common global public health problem that poses one of the greatest risks for developing various cardiovascular diseases, strokes, and renal diseases. It is usually asymptomatic and is referred to as a “silent killer.” This article reviews various aspects of hypertension, including its epidemiology, pathogenesis, classifications, diagnostic techniques, and management options, including medical and non-medical therapies. Various other medical systems, such as Ayurveda, Homeopathy, Naturopathy, Siddha, Unani, and Acupuncture, are also considered.

Price serves as a key factor that significantly influences a company's revenue. That's why this research paper begins by outlining fundamental pricing concepts. It then examines the impact of prices on sales volume and introduces the notion of the sales curve. Prices have undergone unprecedented changes since ancient times. After becoming firmly rooted in ethical and religious frameworks, it has evolved into a computable variable for artificial intelligence's advantages. To explore the evolution of pricing, this paper will focus on four distinct phases of pricing development, going beyond to question why these shifts occurred and what motivations drove them. Both phases emerged from encounters with new social structures enabled by emerging technologies, giving rise to entirely novel approaches to price and exchange. In ancient societies, price held a place within cosmic and social orders. Industrial capitalism transformed it into a mathematical equilibrium. Aided by digital systems and real-time algorithms, it became responsive. Today, artificial intelligence has made it predictive and autonomous. We present this explanation of how technology shapes pricing mechanisms, showing that AI-driven pricing computation isn't merely about speed but a qualitative leap toward autonomous market actors. This study highlights the effects of broad changes in human cognition, technological capabilities, and institutional structures on pricing systems, with direct implications for competition policy and market regulation. Here, we will discuss pricing strategies for goods, along with exploring the principles of commodity pricing that existed before the current era. We will also examine the role currency has played in determining the value of goods, how this valuation is established in various countries, the factors that influence it, and how a nation's economy is impacted by pricing dynamics.

The Sudanese Construction Industry (SCI) is essential to national development but faces persistent structural and operational challenges that limit its contribution. GDP performance is constrained by fragmentation, the absence of a central ministry, and chronic project underperformance including delays, cost overruns, and quality deficiencies. Following the dissolution of the Ministry of Works and Public Affairs, regulatory responsibilities became dispersed across multiple bodies, leading to inconsistent standards, weak enforcement, and limited strategic coordination. This study presents a critical review of empirical literature on the SCI, identifying entrenched inefficiencies across seven categories: Economic, Technical and Technological, Knowledge and Awareness, Regulatory and Policy, Cultural and Behavioral, Governance and Management, and Market. The findings reveal that these barriers are interconnected, forming reinforcing feedback loops that sustain low productivity and systemic underperformance. Given the complexity of these challenges, isolated reforms are deemed insufficient. Scholars and practitioners advocate for comprehensive institutional transformation, particularly through the establishment of a centralized Construction Industry Development Board to coor.dinate policy, standardize practices, and drive long-term improvement. Future research is needed to develop context-specific strategies aligned with national construction goals to enhance sector

Set theory and group theory represent two of the most fundamental frameworks in modern mathematics. Set theory provides the universal language in which mathematical concepts are expressed, while group theory offers powerful algebraic structures for analyzing symmetry, transformations, and invariance. This review article comprehensively examines the foundational principles of both disciplines, their intricate interconnections, and their diverse applications across science, engineering, and technology. Beginning with the historical development and fundamental concepts of set theory—including sets, relations, functions, cardinality, and axiomatic foundations—the review then explores group theory, covering groups, subgroups, homomorphisms, group actions, and fundamental theorems. The natural interdependence between these fields is analyzed, demonstrating how groups are constructed upon set-theoretic foundations. Extensive coverage is provided on applications in computer science, physics, chemistry, cryptography, data science, artificial intelligence, and quantum computing. Recent advances, including computational group theory, topological data analysis, and category theory, are discussed alongside ongoing challenges and future research directions. This review synthesizes classical developments with contemporary advancements, highlighting the enduring relevance of these mathematical structures in driving scientific innovation.

The 21st century is being fundamentally recalibrated by the rise of Artificial Intelligence (AI). This paper investigates how AI acts as a transformative lever across three critical pillars: healthcare, global business, and modern warfare, utilizing data and case studies spanning 2020 to 2026. Within the medical field, AI is bridging gaps in diagnostics and personalized care—notably in the Global South—while simultaneously raising red flags regarding algorithmic equity. In the commercial sector, it is a dual force of creative destruction, sparking massive productivity gains and new job categories even as it necessitates a global reskilling of the workforce. Meanwhile, the militarization of AI introduces high-speed autonomous systems that challenge our traditional understanding of accountability and international law. By synthesizing insights from 30 key institutional and scholarly sources, this thesis contends that AI’s potential for good is matched by significant risks. Ultimately, the paper argues that the trajectory of AI depends less on the technology itself and more on the robustness of our shared governance frameworks.

Ondansetron is a selective 5-hydroxytryptamine type 3 (5-HT3) receptor antagonist widely used for the prevention and treatment of nausea and vomiting associated with chemotherapy, radiotherapy, and postoperative conditions. This review article aims to summarize the pharmacological properties, mechanism of action, clinical applications, efficacy, and safety profile of ondansetron.
Ondansetron works by blocking serotonin receptors both centrally in the chemoreceptor trigger zone and peripherally in the gastrointestinal tract, thereby inhibiting the vomiting reflex. It has shown significant efficacy in controlling acute chemotherapy-induced nausea and vomiting (CINV), with moderate effectiveness in delayed phases when combined with other antiemetic agents.
Clinical studies indicate that ondansetron is generally well tolerated, with common side effects including headache, constipation, and dizziness. However, concerns such as QT interval prolongation and potential cardiac risks have been reported, especially in high doses or susceptible populations.
In conclusion, ondansetron remains a cornerstone in antiemetic therapy due to its effectiveness and relatively favorable safety profile. Ongoing research continues to explore its expanded uses and improved combination therapies for enhanced patient outcomes.

In assisted reproductive technology (ART), controlled ovarian hyperstimulation (COS) plays an important role in guiding treatment progress and determining success. Many ovulation-inducing agents are used in this process, including human menopausal gonadotropin (HMG), recombinant follicle-stimulating hormone (r-FSH), recombinant luteinizing hormone (r-hLH), urinary human chorionic gonadotropin (u-hCG), and recombinant human chorionic gonadotropin (r-hCG). HMG, which comes from the urine of postmenopausal women, mainly contains FSH, LH, and hCG. While bioactivity-based potency units and immunoassay-based ELISA methods remain the gold standard for quantifying protein therapeutics, LC-MS/MS is emerging as a game-changing technology. Its unique strengths are exceptional specificity and absolute quantification capabilities, independence from specialized reagents, powerful multiplexing potential, and ability to provide structural insights, establishing it as an essential tool for both orthogonal verification and cutting-edge research in the field. This study used reverse-phase liquid chromatography with mass spectrometry (RP-LC-MS/MS) and a label-free quantitative proteomics method to analyze imported r-hFSH, r-hLH, r-hCG (Gonal-F, Luveris, and Ovidrel), three domestic HMG products (Le Baode, Man Fuxin, and Xin Yunle), and u-hCG (Li Zhu). The results showed that the purity of the main component in imported gonadotropin drugs was higher than 98.55%. On the other hand, the main element of domestic u-HMG is FSH, with small amounts of hCG and very little LH. These results reveal significant differences in the composition of imported and domestic gonadotropin products, underscoring the need to further evaluate their biological activity and clinical effectiveness. This study provides important insights into drug selection for ART, with practical value for improving the quality of HMG drugs and making better use of them in clinical settings.

The most common viable autosomal trisomy and the primary genetic cause of intellectual disability globally is Down syndrome (DS), also known as trisomy 21. Although inherited translocations and mosaicism also contribute to its genetic variety, this disorder results from a dosage imbalance brought on by an extra copy of chromosome 21, which is frequently produced by maternal age-related meiotic nondisjunction. In addition to multisystem involvements like congenital heart problems, early-onset Alzheimer's disease, and an elevated risk of leukaemia, DS is typified phenotypically by unique craniofacial traits such epicanthic folds and a flat nasal bridge. The Gene Dosage Imbalance, Amplified Developmental Instability, and Critical Region hypotheses explain pathogenic pathways by emphasising the involvement of particular genes such as DYRK1A and DSCAM in neurodevelopmental and cardiac outcomes. Non-invasive prenatal testing (NIPT) and postnatal karyotyping are examples of modern diagnostic frameworks that have revolutionised early detection. Meanwhile, multidisciplinary management through speech-language and physical therapies has greatly improved quality of life and increased life expectancy to an average of 55 years. The complex genotype-phenotype correlations and genetic mechanisms of Down syndrome are covered in this article along with the main maternal and genetic risk factors, current prenatal and postpartum diagnostic procedures, and key therapeutic approaches used in clinical management.

By addressing the genetic foundation of malignancies, gene therapy has emerged as a novel and extremely promising method to cancer treatment, providing targeted and customised therapeutic techniques. In order to prevent tumour growth, trigger apoptosis, or strengthen the body's defences against cancer, this method entails introducing, altering, or silencing particular genes within cancer cells or surrounding tissues. Numerous approaches, including suicide gene therapy, gene replacement therapy, RNA interference-based gene silencing, and immune cell modification (such as CAR-T cell therapy), have shown great promise in preclinical and clinical contexts. Effective gene delivery mechanisms, such as viral vectors like adenoviruses and retroviruses as well as non-viral techniques like liposomes and nanoparticles, are crucial to the success of gene therapy.
Compared to traditional treatments like chemotherapy and radiation, gene therapy has demonstrated enhanced specificity and less systemic toxicity in the treatment of a variety of cancers, including solid tumours and haematological malignancies. However, obstacles like immunological reactions, ineffective administration, expensive expenses, and safety issues still prevent it from being widely used in clinical settings. The accuracy and efficacy of gene-based treatments have been substantially improved by recent developments in gene-editing technologies, especially CRISPR-Cas9. All things considered, gene therapy is a revolutionary development in oncology that has the potential to greatly enhance patient outcomes and completely change the way that cancer is treated in the future.

Natural products have been utilized worldwide since the Vedic era. Although minerals and animal-derived substances have also been used as natural resources in certain regions, herbal medicine continues to serve as a primary form of healthcare for nearly 75–80% of the global population, particularly in developing countries. There are considerable differences in the classification and categorization of herbal drugs. Depending on the regulatory policies of different countries, they are presently categorized under various groups such as pharmaceuticals, food products, health products, dietary supplements, cosmetics, and others.
Herbal formulations are also used across multiple systems of medicine in India, including Allopathy, Homeopathy, Unani, Siddha, and Ayurveda. Herbal remedies are increasingly gaining global recognition due to their potential role in the prevention and management of numerous diseases. India possesses a long-standing heritage of herbal medicine usage spanning centuries.
The use of herbal medicines is regulated by governmental authorities to ensure their safety, effectiveness, and quality standards. This review intends to present an overview of the regulatory framework governing herbal medicines in India. The legal structure for herbal drugs in India is established under the Drugs and Cosmetics Act, 1940, along with its subsequent amendments. According to this Act, herbal medicines are defined as substances derived entirely from plants, their parts, or extracts, and are intended for therapeutic use. The Act also specifies guidelines related to the production, labeling, and distribution of herbal medicinal products.

Artificial Intelligence (AI) refers to the capability of machines to execute tasks that typically require human intelligence, including learning, comprehension, reasoning, and interaction. It can manifest in various forms such as technological infrastructure, components within systems, or as end-user applications. AI is increasingly expected to significantly reshape modern society and its functioning.
The primary aim of this study is to analyze the impact of AI on the economy, focusing on its effects on productivity, employment patterns, industrial structures, and overall economic development. Through multiple case studies, selected industries have been examined to understand how AI integration has transformed their operations, workforce composition, and economic performance.
This paper reviews five research studies and presents a synthesized summary of their findings. It further investigates the current role of AI technologies and explores their future potential. The application of AI in sectors such as healthcare, finance, social media, e-commerce, manufacturing, and global productivity has been thoroughly evaluated, along with projections for future developments.

This study examined the push and pull factors influencing the success of female entrepreneurs in Nigeria, with a focus on understanding how these factors interact to shape enterprise performance and sustainability. The specific objectives were to identify the key push and pull factors and assess their combined impact on women-owned businesses. An exploratory research design was adopted, drawing on secondary data from 55 published empirical studies on female entrepreneurship. The population comprised female entrepreneurs across Nigeria, while the sample consisted of the studies selected for review. Data were collected from these published sources and analyzed thematically to synthesize findings, with patterns and relationships examined to address the research questions. The analysis revealed that limited access to finance, socio-cultural constraints, and weak infrastructure were major push factors, while training, social networks, and government support acted as critical pull factors; their interaction significantly influenced business performance and sustainability. The study concluded that female entrepreneurial success depends on balancing constraints with enabling factors and recommended policies that enhance financial access, strengthen support networks, and provide targeted training to empower women entrepreneurs.

The burden of type 2 diabetes mellitus (T2DM) is increasing in India, leading to higher rates of death and illness. Managing type 2 diabetes mellitus (T2DM) in India is challenging due to unique clinical features and socioeconomic issues among patients. These factors impact blood sugar control and result in worse health outcomes. Consequently, there's a need for safer long-term treatment options that require minimal follow-up. Sitagliptin is a DPP-4 inhibitor that stops the action of DPP-4, an enzyme that breaks down GLP-1, a hormone that helps regulate blood sugar. Sitagliptin improves blood sugar control in people with T2DM, both as a standalone treatment and alongside other diabetes medications, and has a low risk of side effects. This review evaluates the effectiveness and safety of sitagliptin as an additional therapy with other diabetes drugs and insulin.Sitagliptin is an oral antihyperglycemic agent belonging to the class of dipeptidyl peptidase-4 inhibitors. It plays a significant role in the management of type 2 diabetes mellitus by enhancing incretin hormone activity. Incretins such as glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide regulate glucose homeostasis by increasing insulin secretion and decreasing glucagon release in a glucose-dependent manner. This mechanism ensures effective glycemic control without causing excessive hypoglycemia. The pharmacological profile of sitagliptin makes it particularly suitable for long-term therapy in patients who require stable glycemic management with minimal adverse effects.

Purpose: This research explores the transformative impact of Business Intelligence (BI) dashboards on the speed and accuracy of strategic decision-making within corporate environments. Research Problem: Despite significant investment in BI tools, many organizations struggle with "information overload" or poor data visualization, leading to underutilized insights and stagnant decision-making processes. Methodology: This study adopts a mixed-methods approach, utilizing a quantitative survey of 100 middle and senior managers alongside a qualitative analysis of three corporate case studies. Key Findings: The research reveals that interactivity and real-time data integration are the most critical factors for dashboard effectiveness. Furthermore, there is a significant positive correlation between dashboard literacy and decentralized decision-making speed. Conclusions and Implications: BI dashboards are effective only when aligned with specific Key Performance Indicators (KPIs) and supported by a data-driven culture. Organizations should prioritize user-centric design over technical complexity to maximize ROI.

The globalization of higher education and professional mobility has led to significant cultural exchange between Western nations and African societies, yet the specific impact of Western education and work experience on Ghanaian work ethic remains largely unexamined in empirical literature. This qualitative comparative study investigates how exposure to Western labor markets and educational systems transforms, modifies, or reinforces Ghanaian work values, behaviors, and expectations. Using a qualitative comparative design grounded in an interpretive philosophical approach, the study draws on Acculturation Theory (Berry, 1997) and Social Learning Theory (Bandura, 1977). Semi-structured interviews were conducted with 40 Ghanaian participants divided into two equal groups: 20 Ghanaians who had lived, studied, or worked in the United Kingdom, Europe, or the Americas for a minimum of two years and subsequently returned to Ghana, and 20 Ghanaians with no international work or study experience outside West Africa. All participants were currently employed in professional, technical, or managerial roles across Accra and Kumasi. Thematic analysis following Braun and Clarke (2006) revealed six major themes: (1) punctuality as a transplanted virtue versus fluid temporal norms, (2) direct communication and hierarchical flattening in workplace relations, (3) task orientation and accountability replacing relationship-based progression, (4) work-life boundary negotiation between Western separation and Ghanaian integration, (5) frustration with systemic inefficiency as a dissonance trigger, and (6) selective value integration and the emergence of hybrid work ethic. Direct quotations from participants illuminate the lived reality of cultural value transfer in the Ghanaian workplace. The findings inform human resource management, organizational development, and cross-cultural training for Ghanaian and multinational employers.

With the fast growth of the food service sector, there is a need to have a reliable and automatic management tool for managing operations in cafes and small restaurants. In this research paper, the design and implementation of the Cafe Management System (CMS) is presented. This system was designed to provide an efficient and automated method of managing the operations in a cafe. The system can be used to manage customer orders, billing, menu management, inventory management, and employee activities. The system offers an improved approach for handling operations in a cafe compared to the existing manual approaches by ensuring accuracy and reducing the time taken to complete the operation. The system ensures real-time order processing, correct bills generation, and proper stock management. It also enhances the decision-making process in the business by ensuring proper record management and generation of reports.

Phyllanthus emblica Linn. or Emblica officinalis Gaertn., commonly known as Indian
gooseberry or Amla, is one of the most significant medicinal plants in Indian traditional systems of medicine, including Ayurveda, Unani, and Siddha. All parts of the plant are recognized for their therapeutic properties, with the fruit being the most vital. Amla is extensively utilized in pharmaceuticals worldwide for its antioxidant, hepatoprotective, cardioprotective, gastroprotective, and hair-strengthening activities, either alone or in combination with other herbs. Scientific studies indicate that Amla contains a wide range of biochemical constituents, including alkaloids, phenols, tannins, multivitamins, and inorganic elements. Key organic compounds such as ellagic acid, gallic acid, emblicanin A & B, phyllembein, quercetin, and ascorbic acid contribute to its health-promoting effects. This review highlights the geographical distribution, nutritional profile, phytochemical composition, pharmacological activities, and ethnomedicinal uses of Amla, emphasizing its role as a valuable natural therapeutic agent.

Personalized medicine, often referred to as precision medicine, is an innovative approach to healthcare that customizes medical treatment according to an individual’s specific characteristics, including genetic makeup, environmental influences, and lifestyle factors. This transition from a generalized treatment model to a more focused strategy offers significant potential for improving patient outcomes, increasing therapeutic efficacy, and minimizing unwanted side effects. Progress in fields such as genomics, proteomics, and bioinformatics has enabled the discovery of biomarkers and therapeutic targets, supporting the advancement of tailored diagnostics and treatment options across diverse medical disciplines.
Despite its advantages, the broad implementation of personalized medicine faces several challenges, including concerns related to data security, regulatory complexities, and equitable access to advanced healthcare technologies. This overview highlights the fundamental concepts, technological developments, clinical uses, limitations, and future directions of personalized medicine, emphasizing its capacity to revolutionize healthcare systems and usher in a new era of precision-based treatment.
Personalized medicine represents a patient-centered healthcare model that integrates predictive, preventive, participatory, and individualized strategies. Its primary objective is to enhance therapeutic outcomes by identifying the genetic determinants responsible for disease development in each individual. Furthermore, this approach has the potential to reduce healthcare costs and treatment duration while improving patients’ overall quality of life and possibly increasing longevity. Personalized medicine covers a broad spectrum of applications and can be effectively employed in the diagnosis and management of various diseases.

By increasing medication safety, customizing therapy, and boosting decision-making, artificial intelligence (AI) is transforming pharmacy practice. These days, AI is used in adherence tracking, clinical decision assistance, and medication discovery. With an emphasis on new technologies, pharmacist responsibilities, and ethical issues, this narrative study examines significant advancements, real-world uses, and the ramifications of AI integration in pharmacy practice. Clinical Decision Support Systems (CDSS), telepharmacy, pharmacogenomics, AI-based medication interaction checkers, and predictive analytics were all included in the thematic synthesis. AI promotes precision medicine, lowers prescription mistakes, and improves clinical decision-making. Pharmacists and other medical professionals can optimize care with the use of AI solutions. Nevertheless, issues with workflow integration, algorithmic bias, and data privacy still exist. AI has the potential to revolutionize pharmacy, but integrating it will require resolving issues with workflow and ethics. Realizing the full potential of AI requires interdisciplinary cooperation, pharmacist training, and ethical and regulatory monitoring.

The uniformity in the quality of pharmaceutical products is crucial to ensure their intended therapeutic effectiveness. However, due to the physicochemical complexity and natural variability of herbal products, maintaining consistent quality is a significant challenge. Regulatory authorities worldwide have established guidelines and standards for stability testing parameters and procedures for herbal products stored under recommended conditions.

The testing parameters and methodologies for these herbal products are described in the guidelines issued by five major international organizations and fifteen countries/regions, namely the Association of Southeast Asian Nations (ASEAN), Eurasian Economic Commission (EEC), European Medicines Agency (EMA), International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), and World Health Organization (WHO), along with Australia, Brazil, Canada, China, Egypt, Hong Kong, India, Japan, Kenya, South Korea, the Philippines, Qatar, Switzerland, the United States, and Zambia.

Comparative evaluation of physical, chemical, and biological stability parameters across various dosage forms has been conducted, and the testing conditions—including temperature and relative humidity for long-term, intermediate, and accelerated studies—are specified within these guidelines. Tools such as PMC Labs can be explored for further comparison and insights.

This review provides a comprehensive understanding of the global perspective on quality assessment of herbal products, particularly in relation to storage stability.

Rabeprazole is a proton pump inhibitor (PPI). Pharmacodynamic evidence indicates that rabeprazole provides effective acid suppression from the initial dose and sustains this effect during subsequent days of treatment. Furthermore, rabeprazole possesses the highest pKa value (~5.0), which is the pH at which the drug becomes 50% protonated, allowing it to be activated more rapidly at relatively higher pH levels compared to other PPIs. Due to its distinctive metabolic pathway, primarily involving non-enzymatic degradation, rabeprazole is less affected by genetic variations of CYP2C19, leading to minimal impact on its pharmacokinetic and pharmacodynamic profiles.
Regarding clinical effectiveness, rabeprazole 20 mg once daily or 10 mg twice daily demonstrates healing rates at 8 weeks comparable to omeprazole 20 mg once daily in patients with erosive esophagitis. In patients with non-erosive reflux disease (NERD), doses of 10 mg and 20 mg show similar efficacy and are both superior to placebo at 2 and 4 weeks. For the prevention of symptom recurrence, an on-demand regimen with rabeprazole 10 mg daily appears to be beneficial due to its rapid onset of action, with studies in NERD patients confirming its superiority over placebo.
However, long-term continuous therapy, extending up to 5 years, appears to yield better outcomes than on-demand treatment, particularly in individuals with esophagitis. It remains controversial whether reduced doses (10 mg) are truly as effective as the standard dose (20 mg) in these patients. Rabeprazole has also been successfully utilized in managing atypical manifestations of GERD, including dysphagia, GERD-associated asthma, and non-cardiac chest pain, as well as in the management of Barrett’s esophagus.
Lastly, rabeprazole demonstrates Helicobacter pylori eradication rates comparable to omeprazole and lansoprazole when administered alongside antibiotics such as amoxicillin and clarithromycin. Additionally, lower doses of rabeprazole (10 mg twice daily) may also be effective in eliminating the infection.

Nimesulide, a nonsteroidal anti-inflammatory drug (NSAID) with preferential cyclooxygenase-2 (COX-2) inhibitory activity, has been extensively employed as an effective therapeutic agent in patients aged >12 years for the treatment of fever, acute pain, acute tendinitis, osteoarthritis, and dysmenorrhea. It has been reported to possess enhanced antipyretic and anti-inflammatory efficacy compared to paracetamol and aspirin, respectively, while providing analgesic effects comparable to other conventional NSAIDs.

Beyond its primary pharmacological actions, nimesulide exhibits additional properties such as modulation of inflammatory mediators, inhibition of free radical generation, and reduction of histamine release, which may contribute to its therapeutic effectiveness. The drug is characterized by a relatively rapid onset of action and favorable gastrointestinal tolerability when used within recommended dosage limits.

This review critically examines the current clinical status of nimesulide in adult populations, emphasizing available clinical evidence, safety considerations, and its use in special populations. Particular attention is given to its pharmacokinetic profile, mechanism of action, and comparative effectiveness against other NSAIDs, including selective COX-2 inhibitors (coxibs).

Rauwolfia serpentina is considered a safe and efficacious remedy for the management of hypertension. The plant was extensively utilized by numerous physicians across India during the 1940s and subsequently gained global recognition in the 1950s, including in the United States and Canada. However, its use declined due to reported adverse effects, such as depression and concerns regarding carcinogenicity.
In this review, the author critically evaluates the scientific literature related to the application of Rauwolfia in the management of hypertension. The discussion encompasses the plant’s botanical characteristics, chemical composition, and pharmacological properties, while presenting a well-documented mechanism of action of its active constituents. Special emphasis is placed on its therapeutic role in controlling elevated blood pressure. Furthermore, the medical applications of the plant are analyzed, with a critical assessment of its side effects, toxicological profile, and potential carcinogenic risks.
The author challenges the purported association between Rauwolfia and cancer risk, highlighting the significance of appropriate dosage and patient screening to reduce the likelihood of depressive side effects. The review concludes with a recommendation for the use of low-dose Rauwolfia (LDR) in selected hypertensive patients. The plant thus offers clinicians a safe and effective complementary option alongside conventional antihypertensive medications.
Rauwolfia serpentina is an evergreen shrub belonging to the dogbane family, Apocynaceae. The genus Rauwolfia comprises over 100 species distributed across tropical and subtropical regions worldwide, including Europe, Africa, Asia, Australia, and Central and South America. Rauwolfia serpentina is indigenous to the humid, deciduous forests of Southeast Asia, particularly in India, Myanmar, Bangladesh, Sri Lanka, and Malaysia.
The plant typically attains a height of 60–90 cm and bears light green leaves measuring 7–10 cm in length and 3.5–5.0 cm in width. These leaves are elliptical to lanceolate in shape and arranged in whorls of three to five. The plant produces numerous glossy, black or purplish, spherical fruits approximately 0.5 cm in diameter, along with small pink or white flowers. It possesses a well-developed, tuberous, soft taproot, extending 30–50 cm in length and 1.2–2.5 cm in diameter.

A wide range of plant-derived compounds exhibiting therapeutic potential, including antimicrobial, anti-inflammatory, antiviral, antimalarial, antiarthritic (AA), hepatoprotective, and cardiotonic activities, have been documented in scientific literature. Medicinal plants are extensively utilized and are often considered safe and cost-effective substitutes for synthetic pharmaceuticals. However, due to insufficient regulation regarding their potency and purity, caution is necessary to ensure their safe application.
In this review, we aimed to consolidate various herbal constituents demonstrating antiarthritic properties, including alkaloids (montanine, 3-acetylaconitine, sanguinarine, jatrorrhizine hydrochloride, and piperine), terpenoids (eugenol, nimbolide, bartogenic acid, cannabidiol, and curcumin), and flavonoids (quercetin, resveratrol, kaempferol, chebulanin, ellagic acid, rosmarinic acid, gallic acid, chlorogenic acid, ferulic acid, and brazilin). These bioactive compounds exert their antiarthritic effects through diverse pharmacological mechanisms, such as suppression of cytokines, chemokines, and matrix metalloproteinases.
Additionally, experimental models used to evaluate antiarthritic activity, including adjuvant-induced arthritis in animal models, are discussed. Moreover, nanotechnology-based delivery systems for these bioactive compounds are highlighted, as they have demonstrated enhanced therapeutic effectiveness and improved bioavailability.

In the digital age, the right to free expression enshrined in most democratic constitutions, including Article 19(1)(a) of the Indian Constitution, faces new challenges, especially with the rise of "review bombing." Review bombing is the organized or mass publishing of bad reviews, which are often motivated by political, ideological, or social reasons rather than genuine criticism of a product or service. It includes difficult issues about freedom of expression, platform regulation, consumer rights, and reputational injury. The present investigation explores whether review bombing is an acceptable form of free speech or if it crosses the line into harmful behavior, defamation, or digital harassment. The study also investigates the obligations and liabilities of online platforms like Google Reviews and Steam, where similar instances are becoming more regular. It looks into how these platforms manage content without infringing on user rights and whether existing legal frameworks can regulate large digital expressions in a balanced manner. By examining comparative legal perspectives from the First Amendment in the United States to the European Union's Digital Services Act and India's Information Technology Rules, it investigates how different jurisdictions deal with the conflict between individual rights and collective harm. It also looks at major legal cases like Shreya Singhal v. Union of India and Kaushal Kishor v. State of Uttar Pradesh, which provide light on the limits and protections of free expression in India. A multi-stakeholder regulation method is proposed to counteract the misuse of online review platforms while upholding the core democratic ideal of free speech. It promotes judicial guidance on striking a balance between freedom of speech and reputational rights in cyberspace, transparency in platform moderation, and legal clarity on the limitations of digital communication.

Polyherbal topical hydrogels incorporating nutrient-rich solvents have gained significant attention as advanced dermal drug delivery systems. These formulations combine multiple herbal extracts within a hydrophilic polymeric network to enhance stability, bioavailability, and localized therapeutic action. The use of nutrient-rich solvents such as glycerol, propylene glycol, and plant-based oils improves solubility, skin penetration, and overall efficacy of bioactive constituents.

Hydrogels prepared using polymers like Carbopol, HPMC, and sodium CMC exhibit desirable physicochemical properties, including appropriate pH, viscosity, spreadability, and sustained drug release. The synergistic interaction of phytoconstituents provides enhanced antioxidant, anti-inflammatory, antimicrobial, and wound-healing effects, making these systems suitable for various dermatological and cosmeceutical applications.

Evaluation studies confirm good stability, non-irritant nature, and controlled release behavior of the formulations. However, challenges such as variability in herbal sources, stability issues, and regulatory constraints need to be addressed. Future advancements focusing on smart hydrogels, nanotechnology integration, and standardization are expected to improve their clinical applicability.

Overall, polyherbal hydrogels with nutrient-rich solvents represent a safe, effective, and patient-friendly approach for topical therapy with promising potential for future development and commercialization.

This paper presents the design, implementation, and experimental evaluation of a Multiple Input DC–DC (MIDC) converter for hybrid renewable energy applications. The proposed topology integrates power from a solar photovoltaic (PV) panel (18 V, 50 W) and a lead-acid battery (12 V, 7 Ah) into a single step-down (buck) converter stage, delivering a regulated 12 V DC output to resistive and motor loads. An Arduino UNO microcontroller generates Pulse Width Modulation (PWM) signals to control power MOSFET switches, enabling automatic source selection, closed-loop voltage regulation, and safe battery charging. Three operating modes solar-dominant, battery-backup, and hybrid are defined and validated experimentally. Measured conversion efficiency ranges from 88% to 93%, output voltage ripple remains below 50 mV, and source transitions are achieved without observable load interruption. The results confirm that the MIDC architecture reduces component count, improves system reliability, and provides a cost-effective platform for off-grid electrification, EV auxiliary charging, and distributed energy management.

Artificial fruit ripening is widely practiced in modern agriculture and food supply chains to ensure uniform appearance, controlled marketing, and reduced post-harvest losses. Various chemical agents such as ethylene, ethephon, calcium carbide, and other synthetic ripening promoters are used to accelerate physiological ripening processes. While regulated agents mimic natural plant hormones, unsafe chemicals like calcium carbide remain used in some regions and may pose significant health hazards. This review summarizes the biochemical basis of fruit ripening, common artificial ripening procedures, chemicals employed, associated toxicological risks, advantages of controlled ripening, and emerging safer alternatives including biological ripening chambers and natural ethylene systems. The article consolidates evidence from toxicological and food-science literature to support safe and regulated ripening practices and highlights future research directions for sustainable post-harvest management.

Pharmaceutical syrups are concentrated aqueous preparations of sugar or sugar substitutes with or without active medicinal agents. They are widely used oral liquid dosage forms due to their palatability, ease of administration, and suitability for pediatric and geriatric patients. Syrups serve as both medicated preparations and flavored vehicles for drug delivery. The high concentration of sucrose in syrups provides viscosity, sweetness, and some preservative effect. However, modern formulations also incorporate sugar substitutes for diabetic patients. This review discusses classification, formulation components, preparation methods, advantages, disadvantages, evaluation parameters, stability considerations, and applications of pharmaceutical syrups. The aim is to provide a comprehensive understanding of syrups as an important dosage form in pharmaceutical practice and industry.

The doctrine of minimum dose is one of the important principles of homoeopathy introduced by Dr. Samuel Hahnemann. It mainly suggests that medicines should be given in the smallest possible quantity that can still produce a healing effect. From his clinical experience, Hahnemann noticed that large doses often caused unnecessary aggravation, whereas smaller doses worked more gently and effectively. With the development of potentisation, the quantity of the drug is reduced, but its action is believed to become more active at a dynamic level. This paper attempts to explain the concept of minimum dose, how it developed over time, and why it is important in treatment. Overall, the principle helps in achieving a safer and more controlled cure with fewer side effects.

Cancer has been among the major causes of death in the world and the existing treatment methods including chemotherapy, radiotherapy and surgery are mostly characterized by weaknesses that include systemic toxicity, low specificity and multidrug resistance. Nanoparticle-based targeted drug delivery systems are a new approach to addressing these problems in recent years, which enhances the localization of drugs in the tumor sites and reduces the destruction of healthy tissues. Passive targeting is done by the Enhanced Permeability and Retention (EPR) effect and active targeting with ligands (antibodies, peptides, or small molecules) that bind to a particular receptor that is overexpressed on tumor cells.[1]
Several kinds of nanoparticles have been studied such as liposomes, polymeric nanoparticles, dendrimers, solid lipid nanoparticles, metallic nanoparticles, and magnetic nanoparticles. The two systems have different strengths in terms of drug loading capacity, stability and targeting efficiency. As an example, liposomes are biocompatible and common encasers of hydrophilic and hydrophobic drugs, whereas the polymeric nanoparticles offer a controlled release of drugs and enhanced stability.Nanoparticles have demonstrated a great enhancement in both the pharmacokinetics and the pharmacodynamics in cancer therapy, leading to increased therapeutic effect and decreased side effects. A number of nanoparticle formulations including liposomal doxorubicin and albumin-bound paclitaxel are already approved to be used in a clinical setting. In addition, nanotechnology has been able to create multifunctional nanoparticles that can be used to carry out both diagnosis and therapy, referred to as theranostics.[2]
Although they have a good potential, issues like toxicity, immunogenicity, mass production and regulatory barriers still exist. Additional studies are needed to streamline the design of nanoparticles, enhance precision of targeted delivery, and to make nanoparticles safe in the long term.[3]

According to the study, the number of Scheduled Tribes in the East Central Rajmahal Highlands is steadily rising. According to the 2011 Census, there are 4,40,911 people living in the study area. The population growth rate has increased by 18.90% over the past 50 years (1971–2011). The decrease in the death rate is the reason for the population growth in the study area. The population density in the study area has changed from 206 people per square k.m. and 381 people per square k.m. in 2011 and 1971, respectively. Most people live in rural areas. The percentage of people living in cities is just 10.79%. It depicts an uneven population distribution between rural and urban areas and demonstrates the dominance of the rural economy, especially agriculture, in the research region. This paper aims to analyse the current trends as well as the temporal and spatial fluctuations of the Scheduled Tribes population within the research region.

Glucose-responsive insulin systems or "smart insulins" represent a major breakthrough in diabetes care as these systems promise to manage diabetes automatically. Using AI technology, these systems are becoming more accurate and adaptable to real-time monitoring. This research aims to highlight advanced mechanisms of AI-based systems, the current issues, and the possible improvements in diabetes care.
Throughout much of the last century insulin served a central role in the advancement of peptide chemistry, pharmacology, cell signaling and structural biology. These discoveries have provided a steadily improved quantity and quality of life for those afflicted with diabetes. The collective work serves as a foundation for the development of insulin analogs and mimetics capable of providing more tailored therapy. Advancements in patient care have been paced by breakthroughs in core technologies, such as semisynthesis, high performance chromatography, rDNA-biosynthesis and formulation sciences. How the structural and conformational dynamics of this endocrine hormone elicit its biological response remains a vigorous area of study. Numerous insulin analogs have served to coordinate structural biology and biochemical signaling to provide a first level understanding of insulin action.
The historical advancements in the synthesis of insulin analogs by multiple methods is reviewed with the specific structural elements of critical importance being highlighted. The functional refinement of this hormone as directed improved patient care with insulin analogs of more precise pharmacology is reported.

Aloe vera (L.) Burm. f. is a widely used medicinal plant known for its potent wound healing properties. This review highlights the formulation and evaluation of Aloe vera leaf gel–based hydrogels as advanced topical drug delivery systems. Aloe vera gel contains bioactive compounds such as polysaccharides (acemannan), glycoproteins, vitamins, enzymes, and phenolic constituents that exhibit anti-inflammatory, antimicrobial, antioxidant, and tissue-regenerative activities.
Hydrogels are three-dimensional hydrophilic polymeric networks capable of maintaining a moist wound environment, promoting cell proliferation, and enabling controlled drug release. Incorporation of Aloe vera into hydrogel systems enhances its stability, bioavailability, and therapeutic effectiveness. Various natural and synthetic polymers, including chitosan, alginate, Carbopol, and polyvinyl alcohol, are employed to optimize mechanical strength, swelling behavior, and release kinetics.
This review also outlines formulation approaches, in vitro evaluation parameters, and the mechanisms of wound healing, such as modulation of inflammation, collagen synthesis, antimicrobial action, and angiogenesis. Despite its advantages, challenges including variability in composition and limited clinical evidence remain. Overall, Aloe vera–based hydrogels show significant promise as biocompatible and effective wound healing systems.

HIV/AIDS is one of the worst crises affecting global health and influencing economic development and social stability. Preventing and treating HIV infection is a crucial task. However, there is still no effective HIV vaccine for clinical application. Nanotechnology has the potential to solve the problems associated with traditional HIV vaccines. At present, various nano-architectures and nanomaterials can function as potential HIV vaccine carriers or adjuvants, including inorganic nanomaterials, liposomes, micelles and polymer nanomaterials. In this review, we summarize the current progress in the use of nanotechnology for the development of an HIV/AIDS vaccine and discuss its potential to greatly improve the solubility, permeability, stability and pharmacokinetics of HIV vaccines. Although nanotechnology holds great promise for applications in HIV/AIDS vaccines, there are still many inadequacies that result in a variety of risks and challenges. The potential hazards to the human body and environment associated with some nano-carriers, and their underlying mechanisms require in-depth study. Non-toxic or low-toxic nanomaterials with adjuvant activity have been identified. However, studying the confluence of factors that affect the adjuvant activity of nanomaterials may be more important for the optimization of the dosage and immunization strategy and investigations into the exact mechanism of action. Moreover, there are no uniform standards for investigations of nanomaterials as potential vaccine adjuvants.

This study explores the vital role that information technology (IT) integration plays in marketing and branding management strategies, emphasizing its importance as a critical component for companies looking to gain a competitive edge in the quickly changing digital landscape. The combination of cutting-edge IT solutions with conventional marketing and branding frameworks appears as a strategic requirement for businesses navigating the complexity of today's marketplace in an era characterized by digital transformation. According to this study, this kind of integration is crucial for transforming how businesses interact with their target markets by providing fresh opportunities for value generation and distinction.

Background: Antenatal care (ANC) is a cornerstone of safe motherhood and is associated with reduced maternal and neonatal mortality, particularly in low and middle income settings such as India. [1][2] Objective: To review current evidence on antenatal care practices, utilization patterns, and guideline based components in the Indian population, and to highlight their association with maternal and fetal outcomes. [3][4][5] Methods: Narrative review of national level ANC guidelines, Government of India documents, and peer reviewed studies on ANC utilization and outcomes published between 2015 and 2025. [3][5][6] Results: Indian national guidelines recommend that every pregnant woman receive at least four ANC visits, ideally starting in the first trimester, with additional monthly visits thereafter. [3][7] Components include risk screening, nutritional supplementation, immunization, complication detection, and birth preparedness counselling. [3][8] Studies show that women with ≥4 ANC visits have lower rates of preterm birth, low birth weight infants, and neonatal morbidity compared with those with fewer visits. [1][4][5] Underutilization and late initiation of ANC remain important barriers in rural and socioeconomically disadvantaged populations. [5][9] Conclusion: Strengthening early registration, improving quality of ANC services, and increasing the number of visits as per WHO recommended eight visit model can significantly improve maternal and neonatal outcomes in India. [1][2][6]

Background: Abortion is a major reproductive health event in India, where a large proportion of pregnancies are unintended. Recent national estimates indicate that millions of abortions occur annually, most of which are medication based and often performed outside health facility settings[12,3]. Objectives: To summarize the types of abortion, available national and subnational incidence, data, and evidence based management strategies for both spontaneous and induced abortion in the Indian context. Methods: Narrative review of national level abortion incidence studies, nationally representative surveys, and Government of India and WHO based guidelines on safe abortion and post abortion care (PAC) published between 2015 and 2025[1,3,4]. Results: In 2015, an estimated 15.6 million abortions occurred in India, corresponding to 47 abortions per 1000 women aged 15–49 years. About 81% were medication abortions, 14% surgical, and 5% unsafe methods; nearly 80% were performed outside health facility settings. Spontaneous abortion adds further “pregnancy wastage,” with 10–20% of clinically recognized pregnancies terminating before 20 weeks. Recommended management includes expectant, medical (misoprostol based), or surgical (manual/electric vacuum aspiration) approaches, with strong emphasis on timely PAC and post abortion contraception to reduce unintended pregnancies and maternal mortality. Conclusions: Safely delivered, accessible abortion and PAC are critical to reducing maternal morbidity and mortality in India; scaling up regulated, equitable, medical abortion services within the public health system is essential[1,3,6].

The rapid digital transformation of the automotive industry has generated a need for significant changes in engineering education especially in polytechnic institutions that prepare students for technical careers. This review article explores how digital and simulation based learning technologies are used in automotive engineering education. It focuses on tools such as simulation software, computer-aided design (CAD), diagnostic systems, virtual and augmented reality environments as well as online learning platforms. Based on recent literature, the study analyzes how these technologies improve safety during training, increase student engagement, strengthen diagnostic skills and help connect theoretical knowledge with practical application. In addition to reviewing existing studies, the article presents an Integrated digital simulation adoption framework (IDSAF). This framework explains the adoption of digital learning as a process determined by institutional needs, technology use, teaching methods and organizational support. It demonstrates that technology alone does not improve education, infact, significant results are achieved when digital tools are complemented by hands-on learning approaches, qualified instructors and updated curricula. The review also highlights common challenges faced by polytechnic institutions which include limited infrastructure, insufficient teacher training and resistance to change in teaching practices, especially in resource constrained settings. This study provides a clear conceptual model that can help educators, policymakers and researchers plan for sustainable digital transformation in technical education. It offers guidance on how to integrate digital tools in ways that truly enhance skills development rather than simply upgrading equipment. The paper concludes by recommending future empirical studies to test the proposed framework and examine the long-term effects of simulation based learning on graduate preparedness, technical competence and adaptation to evolving industry demands.

The 'rarest of rare' doctrine represents the Indian Supreme Court's most significant contribution to the jurisprudence of capital punishment. Evolved in Bachan Singh v. State of Punjab (1980) and elaborated in Machhi Singh v. State of Punjab (1983), the doctrine was designed to restrict the imposition of the death penalty to exceptional circumstances while ensuring individualised sentencing. This paper traces the doctrinal evolution from its constitutional foundations through contemporary developments, critically examining how a framework intended as a safeguard against arbitrary executions has, over four decades, become mired in subjectivity and inconsistency. Through analysis of landmark judgments, empirical data, and recent jurisprudential shifts including Manoj v. State of Madhya Pradesh (2022) and Vasanta Sampat Dupare v. Union of India (2025), this paper argues that while the doctrine was well-intentioned, its vague formulation and the systemic failure to implement procedural safeguards have undermined constitutional guarantees under Articles 14 and 21. The paper concludes by evaluating reform proposals and considering whether the doctrine can be salvaged or whether abolition remains the only coherent response to the constitutional crisis in capital sentencing.

Digital banking has emerged as a transformative force in the modern financial system, reshaping the way banking services are delivered and experienced by customers. With the rapid advancement of technology, banks have increasingly adopted digital platforms such as mobile banking applications, internet banking portals, automated teller machines, and digital payment systems. This study examines the impact of digital banking on customer experience in both public and private sector banks in India.

Background: Infertility, defined as the failure to achieve pregnancy after 12 months of regular unprotected intercourse, affects a substantial proportion of reproductive-age couples and is associated with psychological and social burden.
Objective: To outline a structured, evidence-based approach to the diagnosis and management of infertility with a focus on age-stratified, resource-sensitive algorithms.
Methods: Narrative review of recent guidelines and systematic reviews on infertility diagnosis and treatment published between 2015 and 2024, identified via PubMed and guideline repositories of the American Society for Reproductive Medicine (ASRM) and World Health Organization (WHO).
Results: Common etiologies include ovulatory dysfunction, male factor infertility, tubal disease, endometriosis, and unexplained infertility. Initial evaluation should include history, physical examination, ovulation assessment, semen analysis, and tubal/uterine imaging when indicated.
First-line treatments include ovulation induction with letrozole for anovulatory women, intrauterine insemination (IUI) for mild male factor or unexplained infertility, and in vitro fertilization (IVF) with or without intracytoplasmic sperm injection (ICSI) for bilateral tubal factor, severe male factor, or women of advanced reproductive age.
Conclusion: A stepwise, age-appropriated management algorithm can optimize pregnancy outcomes and help clinicians avoid unnecessary delays to effective treatment.

Background: Pelvic inflammatory disease (PID) is a spectrum of upper genital tract infections associated with infertility, ectopic pregnancy, and chronic pelvic pain. Robust national prevalence data from India are limited.
Methods: Narrative review of Indian studies on PID prevalence, diagnostic criteria, and management guidelines.
Results: Available Indian evidence shows PID prevalence of 11.55% in postmenopausal women (community study) and significant gynecologic inpatient burden (3-10% of admissions). Diagnosis relies on clinical criteria (lower abdominal pain + cervical/adnexal tenderness) supported by examination, microscopy, and imaging. Outpatient treatment is ceftriaxone 500 mg IM single dose+ doxycycline 100 mg BD + metronidazole 500 mg BD for 14 days. Hospitalization is indicated for severe disease, pregnancy, or non-response.
Conclusion: PID represents substantial gynecologic morbidity in India. Early clinical diagnosis and guideline-based therapy are essential to prevent long-term sequelae.

Customer churn prediction remains a critical challenge in data mining and business intelligence, as retaining existing customers is more cost-effective than acquiring new ones. Traditional methods often fail to model complex nonlinear relationships in high-dimensional behavioral data and necessitate complete retraining when new data is introduced. This paper introduces a hybrid deep learning framework that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks with an incremental learning mechanism to enable adaptive churn prediction. The proposed model effectively captures both spatial and temporal patterns from structured demographic data and sequential behavioral logs, while dynamically updating model parameters without requiring full retraining. Experimental results show enhanced predictive accuracy, scalability, and reduced computational costs compared to traditional models. The framework offers an efficient solution for real-time churn prediction in dynamic environments.

The Ashram Management System is a web- based application developed to manage the operations of old age homes efficiently. In traditional systems, data is stored manually, which leads to data loss, redundancy, and inefficiency. This system digitizes the management process by providing a centralized platform where administrators can manage elder records, ashram details, and donations. The system ensures better data security, quick access to information, and reduced paperwork. It also allows users to search for nearby ashrams and support them through online donations. This project aims to improve the quality of elderly care services and enhance operational efficiency through modern technology.

Misleading drug advertisements pose a significant threat to public health and consumer protection, especially in developing nations such as India. These advertisements frequently include overstated, deceptive, or unsubstantiated claims about the effectiveness, safety, and therapeutic benefits of pharmaceutical and herbal products. Such practices may result in self- treatment, postponement of professional medical advice, harmful health consequences, and economic exploitation of consumers. Additionally, the rapid expansion of digital platforms and influencer-based marketing has further amplified the dissemination of such deceptive promotional content.
To tackle these issues, India has developed a robust legal framework, including the Drugs and Magic Remedies (Objectionable Advertisements) Act, 1954, and the Consumer Protection Act, 2019. These legislations restrict false or misleading claims, regulate the advertisement of specific diseases, and provide avenues for consumer grievance redressal. Regulatory authorities such as :
Central Consumer Protection Authority (CCPA). Advertising Standards Council of India (ASCI). Drugs Controller General of India (DCGI).
play an essential role in supervising and curbing unethical advertising practices.
This article examines the concept, causes, and consequences of misleading drug advertisements, along with the legal measures in place to safeguard consumer interests. It also highlights the need for increased awareness, effective enforcement of regulations, and ethical accountability among pharmaceutical companies and advertisers. Promoting accurate and evidence-based drug information is crucial for protecting public health and sustaining confidence in the healthcare system.

Background: Diabetic foot ulcers (DFU) affect 15-25% of India's 77 million diabetics, preceding 85% of 100,000 annual amputations costing ₹12,000 crore. Five-year post-amputation mortality exceeds 50%.
Objective: To provide evidence-based diagnostic, classification, and management strategies for DFU in Indian practice per IWGDF 2023, ICMR STW, and national consensus.
Methods: Narrative review of IWGDF 2023 guidelines (5 documents), ICMR Diabetic Foot STW 2023, Indian multispecialty consensus 2017, DFEP India survey 2020, and recent Indian studies (2018-2026). [3-8]
Results: Annual screening comprises 10g monofilament (LOPS), ABI/TBI (PAD), and Wagner/Meggitt + PEDIS/IDSA grading. Management follows TIME principles: sharp weekly debridement (40% faster healing), infection control (PEDIS-guided antibiotics), moisture balance (hydrocolloids/alginates), edge advancement (TCC/IRF offloading). Total contact cast achieves 90% 12-week healing vs 50% standard care. Osteomyelitis requires 6-week IV therapy + suppression. Indian microbiology emphasizes MRSA (20%), Pseudomonas (25-35%).
Revascularization success 75-85%. [9-15]
Conclusion: Primary care can deliver 70% healing using monofilament screening, irremovable felted foam offloading, and tiered referral. Specialized centers optimize NPWT/revascularization for high-risk cases. Structured protocols halve amputation rates across resource-variable settings.

The rapid improvement of wireless communication & mobile technology has allowed the advancement of intelligent robotic systems that can be remotely operated with high efficiency. This project presents the design & application of an Android-controlled robot integrated with real-time video surveillance for remote monitoring & navigation.
The proposed system uses an Android smartphone as a control interface, permitting users to operate the robot through a committed mobile application via Wi-Fi communication. The robot is provided with a microcontroller-based control unit that receives commands from the Android device to control movement in many directions. A mounted camera module provides live video streaming, enabling users to monitor environments in real time from remote locations.
The combination of wireless control & video surveillance enhances the robot’s accessibility in various applications such as security monitoring, disaster management, military survey, industrial inspection, & home automation. The system is designed to be cost-effective, user-friendly, & expandable, offering improved mobility, real-time data transmission, & reduced human intervention in hazardous environments.

The rapid proliferation of the Internet of Things (IoT) and smart home ecosystems has fundamentally altered the domestic landscape, trading personal privacy for unprecedented convenience and efficiency. This paper examines the multifaceted privacy risks inherent in interconnected consumer devices, ranging from voice assistants and smart cameras to interconnected appliances.

Diabetes mellitus is a chronic metabolic disorder characterized by persistent hyperglycemia resulting from defects in insulin secretion, insulin action, or both. It represents one of the most significant global health challenges due to its rapidly increasing prevalence and its association with serious long-term complications affecting multiple organs. The disease can lead to cardiovascular disorders, kidney failure, neuropathy, and visual impairment if not properly managed. This review article discusses the classification, pathophysiology, clinical manifestations, diagnostic criteria, complications, and management strategies of diabetes mellitus. Emphasis is alsoplaced on the importance of lifestyle modification, pharmacological therapy, and preventive strategies in controlling disease progression. Understanding the mechanisms underlying diabetes mellitus is essential for healthcare professionals, especially pharmacists, who play a key role in medication management, patient education, and therapeutic monitoring.

Nanoimals are refined colloidal drug delivery systems who have taken great interest in the field of pharmaceutics, which are not highly soluble in water due to their ability to improve drug stability, solubility, and their ability to improve -bioavailability. With the size of drops with 20 to 200 nm and a specific composition of oil, water, surfactants and co-surfacements, they offer special physical and chemical characteristics such as transparency, large surface area and long-term kinetic stability. High-energy techniques such as ultrasonication and high-pressure symmetry, as well as low-energy techniques such as phase inversion temperature and spontaneous emulsion, can be used to create nanoimals. The size of the drop, surface charge, morphology, and strengthening of formulation, methods of characterization such as dynamic light scattering, zeta potential measurements, electron microscopy, and stability are important. Their benefits include low dosage frequency, site-specific targeting, controlled release, and increased drug absorption, all of which increase patient compliance. In addition to its many uses in oral, parenteral, eye, nose, top, and transdermal drug delivery systems, Nanoimans are now being examined more and more for gene delivery, cancer treatment, and vaccination. The theranostic applications, hybrid systems, and vaccines based on Nano emulsions - which combine therapeutic and clinical ability - are the main subjects of recent developments. However, to reach their full capacity, obstacles should be resolved, including large-scale construction, long-term stability, stimulant poisoning and regulatory concerns. All things are believed to have a flexible and exciting platform for all things, Nanoimans contemporary pharmaceutics, focusing on commercial translations, personal remedies and nano technology integration with future instructions.

Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.

This paper examines the constitutional philosophy and operational realities of bail, pre-trial incarceration, and reverse burden provisions within Indian criminal jurisprudence, with a specific focus on the Unlawful Activities (Prevention) Act, 1967 (UAPA) and the Prevention of Money-Laundering Act, 2002 (PMLA). It situates bail as a constitutional mechanism rooted in Article 21, designed to reconcile State power with individual liberty, and contrasts this framework with the restrictive bail regimes under special criminal laws. The study demonstrates how provisions such as Section 43-D (5) of UAPA and Section 45 of PMLA invert the presumption of innocence, constrain judicial discretion, and facilitate prolonged pre-trial detention. Through doctrinal and constitutional analysis, the paper argues that reverse burden clauses and stringent bail conditions transform the preventive logic of bail into a mechanism of “process-based punishment.” It further evaluates judicial interpretation, highlighting a trajectory from rights-oriented scrutiny to increasing deference, while acknowledging limited constitutional correctives such as the invocation of Article 21 in cases of prolonged delay. The paper ultimately contends that while special statutes pursue legitimate objectives of national security and economic integrity, their cumulative design raises serious concerns regarding arbitrariness, proportionality, and the erosion of personal liberty.

The separation of materials based on particle size is a fundamental operation in various sectors, including the mining, chemical, food, and metallurgical industries. Traditional methods of manual sieving are labor-intensive, time-consuming, and prone to inconsistency. This project proposes the design and fabrication of a low-cost, multipurpose vibrating screening machine utilizing a slider crank mechanism. The primary objective is to develop a mechanical system capable of separating different types of grains, sands, powders, and industrial materials efficiently. The machine is constructed using mild steel to ensure high strength and durability, featuring a top-loading feed and interchangeable wire mesh screens to accommodate various grading requirements. The design process emphasizes structural integrity, safety, and ergonomic considerations to minimize manpower and operational effort. This paper outlines the mechanical design methodology, the fabrication process, and the system integration of the multipurpose sieve. By comparing the proposed design with existing market products, this study aims to demonstrate that the developed machine offers a viable, cost-effective solution for small-to-medium scale material separation tasks, significantly reducing processing time and operational costs.

The integration of Information and Communication Technology (ICT) into healthcare delivery has become increasingly important in improving health awareness and service efficiency. This study investigated the influence of ICT utilization on health awareness among community health workers in Akwa Ibom North-West Senatorial District, Nigeria. The study adopted an ex-post facto research design. The population consisted of 1,431 community health workers across 174 primary healthcare facilities, from which a sample of 316 respondents was selected using a multi-stage sampling procedure. Data were collected using a researcher-developed instrument titled Utilization of Information and Communication Technology and Health Awareness Questionnaire (UICTHAQ), which had a reliability coefficient of 0.86. Mean and standard deviation were used to answer research questions, while dependent t-test was used to test the hypotheses at the 0.05 level of significance. The findings revealed that the utilization of social media, internet services, and e-mail significantly influenced health awareness among community health workers in the study area. The study concludes that effective use of ICT tools enhances access to health information and improves health awareness and communication among community health workers. The study recommends the provision of adequate ICT infrastructure, regular training on digital technologies, and policies that support the integration of ICT in primary healthcare delivery to improve health education and community health outcomes.

This study focuses on analysing the impact of Human Resource (HR) practices on employee job satisfaction in public and private sector banks in Chennai. HR practices such as recruitment, training and development, performance appraisal, compensation, and employee relations play a crucial role in enhancing employee motivation and organizational performance. The research is based on primary data collected from 105 bank employees through a structured questionnaire. The study aims to understand employees’ perceptions of HR practices and their influence on job satisfaction. Statistical tools such as correlation, independent sample t-test, and regression analysis were used to interpret the data. The findings reveal that there is a significant positive relationship between working environment and job satisfaction. Compensation and benefits also have a strong impact on employee satisfaction levels. However, the study shows that there is no significant difference in job satisfaction between employees of public and private sector banks. The study concludes that effective HR practices are essential for improving employee satisfaction, motivation, and overall organizational performance. It suggests that banks should focus on better compensation packages, continuous training, and a supportive work environment to enhance employee well-being and productivity.

Leprosy (Hansen's disease) remains a significant public health challenge in developing countries, particularly in South Asia, affecting marginalized populations and causing permanent disabilities if left untreated. This comprehensive review examines the multifaceted strategies essential for leprosy control and elimination, with emphasis on early case detection, multidrug therapy (MDT), contact tracing, disability prevention, and stigma reduction. The World Health Organization's Global Leprosy Strategy 2021–2030 envisions zero leprosy through integrated, country-owned roadmaps and scaled-up prevention initiatives. Current evidence demonstrates that a combination of rapid case identification, complete treatment compliance, household contact screening with post-exposure prophylaxis, systematic disability management, and community-based rehabilitation can significantly reduce transmission and prevent disease-related deformities. Furthermore, addressing stigma through information, education, and communication (IEC) programs and integrating leprosy services within general healthcare systems enhances service accessibility and patient acceptability. This article synthesizes evidence on evidence-based control strategies, programmatic approaches, emerging technologies, and policy interventions necessary to achieve leprosy elimination in endemic countries while respecting the rights and dignity of affected persons.

The practice of influencer marketing has evolved in a very dynamic man- ner in contemporary digital branding, which has changed the contemporary interac- tion process with products and services. Unlike the traditional advertising process, which evokes distrust in consumers, the impact of influence-driven campaigns using the benefits of parasocial relations and perceived authenticity of the digital personali- ties increases engagement. This study explores the fact that influencer marketing en- hances customer loyalty and confidence in a brand amid the contemporary digital era. The research introduces an idea of a model that links credibility, authenticity, con- sumer trust, and brand loyalty of the influencers through the Source Credibility Theo- ry and any other theory, called the Parasocial Interaction Theory. Influencer marketing has transformed the way brands communicate with consumers in the digital era, leveraging social media personalities to build brand awareness, trust, and long-term loyalty. This paper examines the influence of digital influencers on consumer trust and their potential to create sustainable brand loyalty. With increased skepticism toward traditional advertising, consumers are more receptive to peer-like endorsements from influencers. The study reviews existing literature, explores theoretical frameworks such as social influence and source credibility, and analyzes empirical findings on consumer behavior. Results indicate that authenticity, consistency, and relevance of influencer content significantly affect consumer trust and brand loyalty. Challenges such as influencer fraud, transparency issues, and ethical concerns are also discussed. The paper concludes with strategic implications for marketers aiming to optimize influencer campaigns to foster greater consumer trust and loyalty in an increasingly digital marketplace. The paper stresses that although influencers can spur short-term brand interaction, brand loyalty in the long term requires a balance between general and promotional communication. This study contributes to the marketing literature as it presents empirical evidence of a developing economy as well as useful useful insights to businesses looking to build sustain- able consumer relationships in competitive digital world insights to businesses looking to build sustain- able consumer relationships in a competitive worid.

In this paper, the complex relationship between neurotransmitter imbalances and psychopathology is explored. Neurotransmitters are the carriers of the nervous system, which coordinates different physiological and mental functions. Their dysregulation in levels or operation has been involved in the occurrence and development of a number of psychiatric disorders. This paper will seek to explain the multifaceted nature of neurotransmitter imbalances and psychopathology, and the contribution of neurotransmitters to diverse mental disorders, including serotonin, dopamine, norepinephrine, and gamma- aminobutyric acid (GABA). In addition, it presents the implication of such findings on diagnostic strategies, mode of treatment and future research.

Peptic Ulcer Disease(PUD) represents a break in the mucosal lining of the stomach or duodenum, extending through the muscularis mucosa. Once considered a chronic condition primarily driven by lifestyle factors and acid hypersecretion, the understanding of PUD has been revolutionized by the discovery of Helicobacter pylori (H. pylori) and the widespread use of non-steroidal anti-inflammatory drugs (NSAIDs). This review provides a comprehensive overview of the epidemiology, pathophysiology, clinical presentation, diagnostic strategies, and contemporary treatment paradigms of PUD, emphasizing the shift from acid suppression to curative antimicrobial therapy and risk mitigation. Peptic ulcer disease (PUD) is a common gastrointestinal disorder characterized by mucosal damage of the stomach or duodenum due to the corrosive effects of gastric acid and pepsin. The major etiological factors include Helicobacter pylori infection and the use of nonsteroidal anti-inflammatory drugs (NSAIDs). Despite advances in diagnosis and therapy, PUD remains a significant cause of morbidity worldwide. This review provides a comprehensive overview of the etiology, pathophysiology, clinical manifestations, diagnosis, and current treatment strategies of peptic ulcer disease, with emphasis on pharmacological management and prevention of complications.

Peptic Ulcer Disease(PUD) represents a break in the mucosal lining of the stomach or duodenum, extending through the muscularis mucosa. Once considered a chronic condition primarily driven by lifestyle factors and acid hypersecretion, the understanding of PUD has been revolutionized by the discovery of Helicobacter pylori (H. pylori) and the widespread use of non-steroidal anti-inflammatory drugs (NSAIDs). This review provides a comprehensive overview of the epidemiology, pathophysiology, clinical presentation, diagnostic strategies, and contemporary treatment paradigms of PUD, emphasizing the shift from acid suppression to curative antimicrobial therapy and risk mitigation. Peptic ulcer disease (PUD) is a common gastrointestinal disorder characterized by mucosal damage of the stomach or duodenum due to the corrosive effects of gastric acid and pepsin. The major etiological factors include Helicobacter pylori infection and the use of nonsteroidal anti-inflammatory drugs (NSAIDs). Despite advances in diagnosis and therapy, PUD remains a significant cause of morbidity worldwide. This review provides a comprehensive overview of the etiology, pathophysiology, clinical manifestations, diagnosis, and current treatment strategies of peptic ulcer disease, with emphasis on pharmacological management and prevention of complications.

This study investigated influence of farmers and herders’ clashes on psycho-social well-being of University students in North Central Nigeria; implications for counselling. The study determined the influence of farmers and herders’ clashes on psychological well-being and social well-being of University students in the study area. Two specific objectives with corresponding research questions guided the study. Two hypotheses were formulated and tested at 0.05 level of significance for the study. The study adopted survey research design. Undergraduates formed the population of the study. A total of four hundred (400) undergraduates were sampled from a population of one hundred and ninety-eight thousand, eighty-six (198,086) undergraduates of Federal Universities in North Central Nigeria using Taro-Yamen’s formula. A multi-stage sampling procedure which included purposive or judgmental sampling technique, proportionate stratified random sampling technique, and ballot simple random sampling technique were used at different stages in the study. A questionnaire structured by the researcher titled “Influence of Farmers and Herders’ Clashes on Psycho-Social Well-Being of University Students” (IFHCPSWBUS) was used for data collection. An overall instrument reliability coefficient of .88 was obtained using Cronbach’s Alpha reliability coefficient in determining the internal consistency of the items. Descriptive statistics involving mean and standard deviation were used to answer the research questions while the inferential statistics of chi-square (x2) goodness of fit was used to test the hypotheses at 0.05 level of significance. The result revealed that farmers and herders’ clashes have significant influence on psycho-social well-being of University students in North Central Nigeria. The study concluded that farmers and herders’ clashes influence the psycho-social well-being of university students in North Central Nigeria. It proffers three counselling implications and strategies or approaches to improve psycho-social well-being of university students in North Central Nigeria. They include trauma-focused cognitive behavioural therapy (TF-CBT), narrative therapy, and facilitating support groups for the students. Finally, the recommendations among others include prioritize effective educational policies for safe learning environments; mental health services; long-term peace initiatives on the farmer and herders’ clashes and support of victims of security challenges

Brain tumors represent a significant public health challenge and are classified as either primary (originating in the brain) or secondary (metastatic) malignancies. This comprehensive review examines the epidemiology, classification, clinical manifestations, diagnostic modalities, and contemporary therapeutic strategies for brain tumors. Emphasis is placed on the pathophysiology of glial, neuronal, and metastatic tumors, as well as the multifactorial genetic and environmental factors contributing to tumorigenesis. The review highlights emerging treatment approaches including targeted molecular therapy, immunotherapy, and precision medicine that offer improved outcomes for patients with both benign and malignant brain tumors. Additionally, it addresses the challenges in diagnosis, the role of molecular biomarkers, and the psychosocial implications of brain tumor management. A multidisciplinary approach integrating neurosurgery, neuro-oncology, radiation oncology, and supportive care is essential for optimizing patient outcomes and quality of life. Recent advances in neuroimaging, molecular profiling, and personalized treatment strategies are reshaping the landscape of brain tumor management and offering hope for improved prognosis and reduced mortality.

Electronic Learning which has surged extensively due to the proliferation of internet and digital technology, is conceptually synonymous to Supply Chain Management (SCM), in both scenario the focus is on efficient flow and management of resources. In e-learning the goal is to deliver the right knowledge to the right learner at the right time which is synonymous to SCM.E-learning environments are composed of a Content Management System (CMS), communication tools, and various multimedia content. Despite leveraging these state-of-the-art facilities, the widespread adoption and efficacy of e-learning platforms are significantly hindered by critical challenges. Specifically, issues related to data security, user authentication, and credentialing remain major obstacles to realizing their full potential. Current traditional methodologies for mitigating these issues frequently prove inadequate, exposing e-learners, educators, and institutions to considerable risks, including data breaches, identity theft, and credential fraud. Consequently, there is a pressing need for innovative, decentralized solutions that can fundamentally strengthen security, enhance transparency, and restore trust across the e-learning ecosystem.This paper examines key security risks in cloud-based e-learning and proposes a Blockchain-based solution using smart contracts to ensure data security, reduce vulnerabilities, and uphold academic integrity through decentralization and transparency.

This narrative review explores the role of tactical awareness in defensive systems in handball, addressing the literature's emphasis on offense. It synthesizes studies from major databases on defensive formations, perceptual-cognitive processes, and training methods to understand defensive effectiveness holistically. Findings show that success depends not only on structures but on players' perception, anticipation, and response to dynamic situations. Open systems like the 3:2:1 enable proactive pressure and ball recovery but create spatial gaps, while closed systems ensure stability under numerical equality. Tactical awareness, via perception–action coupling, supports real-time decisions and adaptive positioning. Team cognition, through shared mental models, boosts coordination and collective defense. Non-linear pedagogy, including small-sided games, effectively develops these skills. Limitations include inconsistent definitions, absent standardized tools, and reliance on observational designs. Overall, tactical awareness drives defensive performance; integrated training and rigorous research are needed to advance theory and practice.

In recent years, mental health in India has become an increasingly significant public health issue, especially due to progressive legislative and policy initiatives such as the Mental Healthcare Act (2017), the National Mental Health Programme, etc. However, because of the persistence of gender inequalities, women continue to experience barriers in accessing mental health care. Social-cultural stigma, unpaid caregiving responsibilities, economic dependence, domestic violence, and rural–urban disparities continue to shape women&#39;s mental health and wellbeing significantly, yet do not receive sufficient attention within current policies. The objective of this research is to evaluate India&#39;s Mental Health Policy through a gender lens and to identify the gap between the policy commitment to women and women&#39;s actual mental health reality in India. The methodology of this study is a qualitative non-empirical approach using a policy analysis research design. The analysis uses key national policies, government reports, international guidelines and scientific literature. Research has shown that while policies are created to ostensibly address gender issues, implementation has been done primarily at an institutional level without regard to how services may affect specific populations of women. Structures like stigma associated with mental illness/health issues for people who identify as female; inadequate availability of community-supported services; and lack of gender-based data continue to limit equitable access to mental health. Effective transformation of policy commitments into actual implementation will require the development of gender-relevant strategies for community engagement; creating efficiencies in providing services that meet individual needs rather than treating all individuals in the same manner; and, last but not least, implementing mechanisms to monitor and assess the effectiveness of programs targeting women. The article provides a necessary perspective on the relationship between gender and mental health in today&#39;s society.

Pharmacovigilance plays a crucial role in ensuring drug safety by detecting, assessing, and preventing adverse drug reactions (ADRs). However, traditional pharmacovigilance systems face challenges in managing the rapidly increasing volume and complexity of healthcare data. Artificial Intelligence (AI) has emerged as a transformative solution, enabling efficient processing, analysis, and interpretation of large datasets from sources such as Individual Case Safety Reports (ICSRs), electronic health records, and social media. AI technologies, including machine learning, deep learning, and natural language processing, enhance key pharmacovigilance activities such as automated case processing, signal detection, causality assessment, and risk–benefit evaluation. These approaches improve the speed, accuracy, and scalability of drug safety monitoring while supporting proactive identification of potential risks. Despite these advantages, challenges such as data quality, regulatory uncertainty, ethical concerns, and lack of model transparency remain significant barriers to widespread adoption. Addressing these limitations is essential to ensure safe, reliable, and compliant implementation. Overall, AI holds significant potential to transform pharmacovigilance from a reactive system into a predictive and proactive model, ultimately improving patient safety and public health outcomes.

Objective: To synthesise conceptual, functional, and clinical correlations between Ayurvedic biomarkers—Dosha, Dhatu, Agni, and Ama—and modern biomedical diagnostic parameters, and to develop a unified, integrative diagnostic framework. DataSource: Primary Ayurvedic texts (Brihattrayi, Laghutrayi), classical commentaries, CCRAS monographs, AYUSH research reports and modern biomedical literature from PubMed, Scopus and Google Scholar (1980–2024). Data included metabolomics, inflammatory markers, endocrine–immune parameters, genomic evidence and systems-biology studies. Review Methods: A narrative integrative review approach was used. Literature was screened using keywords such as “Ayurvedic biomarkers”, “Agni metabolism”, “Ama inflammation”, “Dosha physiology”, “Ayurgenomics”, “biomarker correlation”, and “metabolomics”. Eligible sources (n=142 screened; ~56 included) were analysed for conceptual alignment, functional similarity and translational potential. Ayurvedic constructs were mapped onto modern biomarkers by comparing physiological functions, pathological expressions and clinical correlates. Results: Strong correlations were observed: Vata with autonomic nervous system indices and HRV; Pitta with metabolic heat, enzymatic activity and hepatic biomarkers; Kapha with lipid metabolism, glycaemic indices and anabolic pathways. Agni exhibited parallels with mitochondrial efficiency, digestive–metabolic enzymes, thyroid function and hepatic metabolism. Ama showed convergence with systemic inflammation, oxidative stress, endotoxin burden and gut barrier dysfunction. Tissue-specific modern biomarkers could be mapped to Dhatu status. An integrative diagnostic framework combining Ayurvedic markers with laboratory parameters improved precision in disease staging and personalised prognosis.
Conclusion: Ayurvedic biomarkers provide a multidimensional understanding of physiology and pathology that aligns closely with contemporary metabolic and molecular signatures. Integrative diagnostics incorporating both knowledge systems may enhance predictive, preventive and personalised healthcare. Standardisation and further interdisciplinary research are essential.

Heavy metal contamination represents a persistent environmental challenge with significant implications for plant health, genomic stability in turn impact on agricultural productivity. This review studies the genotoxic effects of major heavy metals, particularly cadmium (Cd) and arsenic (As), along with mercury (Hg), nickel (Ni) and lead (Pb). These metals induce DNA damage through both direct interactions and indirect pathways mediated by reactive oxygen species (ROS), leading to oxidative stress. Key genotoxic effects include DNA strand breaks, chromosomal aberrations, altered DNA methylation and impaired DNA repair mechanisms. The cumulative effects constitute plant growth, reproduction and transgenerational genome integrity. Understanding these molecular mechanisms operated by heavy metals is essential for developing phytoremediation strategies and obtaining sustainable crop production even in contaminated areas.

Artificial Intelligence (AI) is transforming the structure, methodology, and philosophy of modern education. From adaptive learning platforms and intelligent tutoring systems to predictive analytics and automated assessment, AI has begun reshaping educational practices across the globe. In the Indian context, especially after the implementation of the National Education Policy 2020, the integration of AI and digital technologies has gained strategic importance. This paper critically examines the philosophical foundations, pedagogical implications, ethical concerns, and administrative applications of AI in contemporary education. It evaluates the transformation of curriculum, teaching roles, assessment patterns, and learner autonomy in AI-supported environments. The study also highlights concerns such as digital inequality, data privacy, algorithmic bias, and teacher preparedness. The paper argues that AI should serve as an augmentative tool that enhances human capability rather than replacing educators. A balanced integration of technological advancement and humanistic educational values is essential for sustainable educational development.

Research has shown that education, particularly Social Studies, plays a crucial role in shaping
skills. Despite the prominence of Social Studies in Nigerian tertiary institutions, there is limited empirical evidence exploring its impact on students’ leadership skills development. This study analyzes the relationship between Social Studies Education and the development of leadership skills among students in Nigerian tertiary institutions. Employing a mixed-methods approach, data was collected from 400 students and 20 lecturers across 10 institutions in Southwest Nigeria using questionnaires and interviews. Results indicate that the Social Studies curriculum is highly effective in instilling foundational leadership values, with strong emphasis on ethics (Mean=4.3) and critical thinking (Mean=4.1). Consequently, a significant majority of students (72%) felt prepared for leadership roles, and 65% had held formal positions. However, a "theory-practice gap" was identified, with lower competence in applied skills like decision-making (Mean=3.4) and conflict resolution (Mean=2.9). A multiple regression analysis confirmed that challenges, particularly a theoretical focus (β = -.38, p < .001), significantly hinder comprehensive leadership development. The study concludes that while Social Studies successfully cultivates leadership disposition, it requires a strategic shift towards experiential learning methodologies to bridge the skill application gap and fully realize its potential in developing actionable leadership competencies.

The Vedic Age (c. 1500–600 BCE) was a transformative era in ancient Indian history, laying the foundations of spiritual thought, social organization, and economic life. While the period is celebrated for its religious texts—the Rig-Veda, Yajurveda, Sambaed, and Atharvaveda—it also witnessed the emergence of agriculture as a sacred and socio-political institution. This research paper examines the agrarian landscape of the Vedic period, exploring how farming practices were integrated with ritual, cosmology, and governance. It analyzes the transition from pastoralist to settled agriculture in the Indo Gangetic plains, the tools and techniques employed, the variety of crops cultivated, and the symbolic role of farmers in Vedic society. Additionally, the study highlights challenges such as climate variability and the adaptive strategies of early agrarian communities. By tracing these developments, the paper reveals how Vedic agriculture shaped not only food production but also the cultural and political identity of ancient India.

Medical libraries play a crucial role in supporting healthcare professionals, researchers, and students by providing access to reliable and up-to-date information. Understanding user information needs is essential for improving library services and ensuring effective information delivery. This paper presents a review-based analysis of user needs in medical libraries by examining existing literature. It highlights the evolving information requirements of users, the impact of digital resources, and challenges faced by medical libraries in meeting user expectations. The study also suggests strategies for enhancing user satisfaction and optimising library services. The findings emphasise the importance of user-centered approaches in modern medical library systems.

Parabolic solar cookers represent a transformative clean cooking technology with the potential to address the global energy access crisis affecting 2.3 billion people who lack access to modern cooking facilities. This comprehensive review systematically examines the current state of research on parabolic solar cooking technology, encompassing theoretical foundations, materials science, thermal performance optimization, energy storage integration, design innovations, socioeconomic adoption factors, and environmental sustainability implications. Unlike previous reviews that have focused narrowly on thermal performance metrics, this synthesis integrates technical, social, and environmental perspectives to provide a holistic understanding of the technology's potential and limitations. The review synthesizes findings from over 150 peer-reviewed studies, doctoral theses, conference proceedings, and technical reports published between 1970 and 2026, with particular emphasis on advances since 2015. Key findings reveal that modern parabolic dish cookers achieve thermal efficiencies of 35-55% and stagnation temperatures exceeding 400°C under optimal conditions, sufficient for all common cooking methods including frying, roasting, and baking. The integration of thermal energy storage has emerged as the most significant recent advancement, with phase change materials such as erythritol demonstrating heat utilization efficiencies of 13-49% and enabling cooking durations of 3-7 hours beyond sunshine hours. Material innovations, including metallized polymer films with reflectances approaching 0.95 and selective absorber coatings with solar absorptance exceeding 0.94, have substantially improved optical performance while reducing weight and cost. However, significant research gaps persist in standardized testing methodologies, long-term durability assessment under real-world conditions, techno-economic optimization of storage-integrated systems, and understanding the complex socio-cultural factors that determine adoption outcomes. This review identifies critical research priorities including the development of universal performance testing protocols, systematic investigation of storage material degradation mechanisms, field studies of adoption dynamics across diverse cultural contexts, and lifecycle assessment of emerging materials. The synthesis concludes that realizing the substantial potential of parabolic solar cooking technology requires integrated approaches that simultaneously advance technical performance, reduce costs, improve usability, and address the multifaceted adoption barriers that have historically limited the impact of solar cooking interventions.

Tinospora cordifolia (Gilo) is prominent in traditional medicine systems due to its diverse therapeutic applications and bioactive compounds. This review explores the botanical characteristics, nutritional composition, and pharmacological properties of Tinospora cordifolia. The plant is known for its immunomodulatory, anti-inflammatory, antioxidant, and adaptogenic effects, making it an invaluable resource in treating ailments like fever, diabetes, jaundice, and skin disorders. Its bioactive compounds such as alkaloids, glycosides, and diterpenoids—have shown significant potential in combating diseases and promoting overall health. By integrating insights from historical and modern perspectives, this study highlights the importance of Tinospora cordifolia as a cornerstone in developing herbal medicines and biopharmaceuticals. Further research on its safety profile and therapeutic efficacy can pave the way for its enhanced application in contemporary medicine.

Brazilian Jiu-Jitsu (BJJ) has become a global sport, including in North Macedonia. The sport promotes moral growth, encourages social awareness, and builds mental resilience in teenagers, helping them resist aggression. BJJ acts as a tool that enhances self-awareness and social skills through constant practice, with no specific goals, while teenagers improve executive function and learn to help others regulate hormones and adapt to different roles.

The necessity for effective and centralized academic administration systems has been highlighted by the quick development of digital technology in higher education. Conventional college project submission procedures frequently depend on manual documentation, physical report submissions, and unofficial feedback channels, which can result in inefficiencies including data loss, delayed evaluation, and a lack of transparency. The design and implementation of a role-based web application for submitting and reviewing academic projects are presented in this study. Administrators may control user responsibilities and assignments, faculty members can assess and offer structured comments, and students can upload project materials to the proposed system's centralized platform. PHP is used for backend processing, MySQL is used for database management, and HTML, CSS, and JavaScript are used for the front end of the system. It is installed on an Apache server environment. Real-time project status tracking, organized workflow management, and secure authentication are all guaranteed by the program. The technology increases productivity, facilitates better faculty-student collaboration, and reduces human error by digitizing the academic project lifecycle. For academic institutions looking to update project monitoring and assessment procedures, the suggested framework offers a scalable and useful alternative.

The advancement of automation and robotics has significantly influenced various sectors, including agriculture and lawn maintenance. The grass cutter robot with obstacle avoidance is an innovative solution designed to reduce human effort, improve efficiency, and enhance safety during lawn mowing operations. This project focuses on the design and development of an autonomous robot capable of cutting grass while intelligently detecting and avoiding obstacles in its path. The robot operates using a combination of sensors, microcontrollers, and motor drivers to achieve autonomous navigation. Ultrasonic sensors are commonly used for obstacle detection, enabling the robot to measure distances and change direction when an object is detected. The system is powered by rechargeable batteries and incorporates a rotating blade mechanism for cutting grass effectively. The proposed system aims to provide a cost-effective, eco-friendly, and efficient alternative to traditional manual grass cutting methods. It minimizes human intervention, reduces physical strain, and ensures consistent lawn maintenance. The project also explores improvements such as solar power integration and IoT-based monitoring for future enhancements. The importance of such systems is increasing due to the growing demand for smart and efficient solutions in agriculture a nd domestic applications. This robot not only saves time and labor but also contributes to safer and more sustainable practices.

Marine natural products (MNPs) are a valuable frontier in current medication discovery and development because of their unique chemical diversity and bioactivity. Sponge, tunicates, corals, algae, mollusks, and microorganisms are all marine creatures that produce structurally unique secondary metabolites with substantial therapeutic promise. Several MNP-derived medicines are already in clinical use, such as cytarabine and vidarabine (anticancer, antiviral), trabectedin (anticancer), eribulin (anticancer), and ziconotide (analgesic). These chemicals have broad pharmacological actions against cancer, infectious diseases, inflammation, cardio-vascular problems, neurological ailments, and chronic pain, and they frequently operate through novel mechanisms. The development pipeline includes bioprospecting, extraction, bioassay-guided screening, isolation, structural elucidation, preclinical testing, optimisation, scale-up manufacturing, and clinical evaluation. Advances in genomics, metabolomics, syn-thetic biology, and sustainable biotechnology have overcome constraints such as low yield and environmental concerns, allowing for greater access to these compounds. Thus, marine ecosystems serve as an underexplored pool of bioactive chemicals with the potential to devel-op next-generation medicines, addressing unmet medical needs and providing fresh ways for combating drug resistance and complex disorders.

The increasing use of constructed-response and graded assessment formats in Mathematics necessitates robust psychometric frameworks capable of capturing varying levels of student performance. This study investigated the application of Item Response Theory in the development, calibration, and validation of a polytomous Mathematics Achievement Test administered to senior secondary school students in the Federal Capital Territory (FCT), Abuja, Nigeria. Guided by two research questions, the study adopted a descriptive survey research design. A total of 381 SSII students were selected from a population of 54,139 using the Krejcie and Morgan sample size determination procedure. Data were collected using a polytomously scored Mathematics Achievement Test and analyzed using appropriate polytomous IRT models implemented through the mirt package in R statistical software. Results indicated that the test measured multiple latent mathematical proficiencies, with category threshold parameters demonstrating orderly progression along the ability continuum. The majority of items exhibited satisfactory slope parameters, suggesting that the graded response categories effectively differentiated students across varying ability levels. The test also demonstrated adequate reliability and provided substantial information across a broad range of student abilities. These findings confirm the suitability of polytomous IRT models for evaluating Mathematics achievement tests that incorporate partial credit scoring. It was recommended that test developers pay close attention to category functioning, ensure well-defined scoring rubrics, and conduct routine psychometric evaluations to maintain the validity, fairness, and interpretability of polytomous assessment instruments. Continuous professional development for Mathematics educators and test developers in polytomous item construction and IRT-based analysis was also advocated.

Rasashastra and Bhaishajya Kalpana constitute essential branches of Ayurveda that deal with the preparation, processing, and therapeutic application of drugs derived from metals, minerals, and herbal sources. These formulations play a pivotal role in enhancing immunity (Vyadhikshamatva), promoting tissue nourishment (Dhatu Poshana), and improving overall health. The classical pharmaceutical processes such as Shodhana (purification), Marana (incineration), and Putapaka are employed to convert raw substances into bioavailable and therapeutically potent forms. Ayurvedic principles emphasize the role of Agni (digestive fire), Ojas (vital essence), and proper nutrition in maintaining immune strength. Rasadravyas act at the cellular level by improving metabolism, microcirculation, and micronutrient availability. Various dosage forms such as Kwatha, Vati, Arishta, and Rasayana preparations contribute to immunomodulation. This review highlights the significance of Rasashastra and Bhaishajya Kalpana in immunity enhancement and disease prevention.

Because of its accessibility and perceived safety, paracetamol (acetaminophen) continues to be one of the most commonly used analgesic and antipyretic medications worldwide. Overdosing on paracetamol remains a major cause of acute liver failure globally, despite its extensive therapeutic use. According to new research, older adults are a clinically vulnerable population where toxicity outcomes may be impacted by pharmacokinetic changes, comorbidities, polypharmacy, and delayed presentation. This review examines clinical risk factors, pharmacological mechanisms, epidemiological trends, and treatment approaches for paracetamol poisoning in people 65 years of age and older. The interaction of comorbid conditions with hepatotoxic pathways, repeated supratherapeutic ingestion, and age-related metabolic changes are all given special attention. To clarify the factors influencing toxicity in this population, recent research from toxicology registries, population cohorts, and clinical pharmacology studies is critically assessed. Comprehending these trends is crucial for enhancing clinical risk assessment, refining therapeutic approaches, and directing preventative measures for senior citizens.

Rett syndrome (RTT) is a rare X-linked neurodevelopmental disorder. It is marked by a progressive loss of motor and cognitive abilities, repetitive hand movements, autonomic dysfunction, and epilepsy. This disorder mainly affects females and is often caused by mutations in the methyl-CpG binding protein 2 (MECP2) gene. Although the disorder was first described in 1966, major progress in molecular genetics over the last two decades has greatly improved understanding of its biological effects and treatment targets. Historically, treatment focused on managing symptoms with anticonvulsants, muscle relaxants, and behavioral therapies. Recently, advancements in molecular pharmacology have led to targeted therapies that aim to correct the underlying neurobiological issues. Notably, trofinetide, an insulin-like growth factor-1 analog, is now the first disease-specific therapy approved for Rett syndrome. Additionally, ongoing research is exploring gene therapy, RNA editing, neurotrophic factor modulation, and new small-molecule drugs. This review covers the genetic basis, molecular mechanisms, clinical features, current pharmacological management, and emerging therapies for Rett syndrome. It highlights recent clinical trials and research that could influence future treatment strategies for this complex disorder.

Indian Knowledge Systems (IKS) represent a vast body of traditional knowledge developed in the Indian subcontinent through centuries of intellectual inquiry, cultural evolution, and practical experience. These systems encompass diverse fields including philosophy, governance, education, economics, science, and social organization. Historical institutions in India such as the Gurukul education system, village governance structures, guild organizations, and administrative frameworks evolved within the intellectual and ethical framework of Indian Knowledge Systems.

Bulimia nervosa is a serious psychiatric eating disorder characterized by binge eating followed by compensatory behaviors. This review summarizes its causes, clinical features, diagnosis, complications, and management, emphasizing multidisciplinary care.