Job satisfaction has become a critical factor influencing employee retention in modern organizations. This study examines the relationship between job satisfaction and employee retention by analysing how factors such as salary, recognition, and work environment affect employees’ intention to stay. Primary data was collected through a structured questionnaire from 50 respondents.
The findings indicate that job satisfaction significantly impacts employee retention, with higher satisfaction levels leading to a stronger intention to remain in the organization. Key factors such as compensation and career growth opportunities were identified as major drivers of retention. The study highlights the importance of effective HR practices in improving employee satisfaction and reducing turnover, thereby contributing to organizational stability and performance.

Women tourists face heightened safety risks in unfa- miliar environments due to the lack of real-time, location-aware safety intelligence. SafeRoute is a web-based platform designed to address this gap by integrating machine learning, geospatial visualization, and emergency response mechanisms to enhance the safety of women travelers. The system enables citizens to report safety incidents anonymously or with identification and employs an XGBoost-based classifier to predict crime-prone areas using historical incident data obtained from the National Crime Records Bureau (NCRB) [1]. A pre-trained AI model detects and filters fake or misleading reports to ensure data integrity. Crime hotspots are dynamically visualized through an intensity- weighted heatmap rendered using the Leaflet.js mapping library and its Leaflet.heat plugin, where each district’s geographical coordinates are mapped against total crime volume to produce color-coded spatial overlays enabling tourists to identify high-risk zones and plan safer routes. A role-based dashboard architecture serves tourists, safety officers, and administrators with tailored interfaces for reporting, monitoring, and response. An integrated SOS module allows tourists to trigger real-time emergency alerts with automatic location sharing to nearby safety officers. Evaluation of the prediction model yielded an accuracy of 87.46% and an AUC of 0.9572, with macro-averaged precision, recall, and F1-score all at 0.87, demonstrating reliable performance across balanced class distributions. SafeRoute offers a scalable, data- driven solution to improve situational awareness and emergency responsiveness for women tourists.

Agriculture plays a vital role in the economy, and maintaining optimal environmental conditions inside a greenhouse is essential for improving crop yield and quality. Manual monitoring of temperature, humidity, soil moisture, and light intensity is inefficient and may lead to improper plant growth.This project presents the design and implementation of a Greenhouse Monitoring and Control System using Internet of Things (IoT) technology. The system continuously monitors key environmental parameters such as temperature, humidity, soil moisture, and light intensity using sensors like DHT11/DHT22, soil moisture sensor, and LDR. The collected data is processed using a microcontroller such as NodeMCU and transmitted to a cloud platform via Wi-Fi.Based on predefined threshold values, the system automatically controls actuators such as water pumps, exhaust fans, and artificial lighting through relay modules. Real-time data is displayed on a mobile application or web dashboard, allowing remote monitoring and control from anywhere.The proposed system helps in maintaining optimal greenhouse conditions, reducing water consumption, minimizing human intervention, and increasing crop productivity. The project is low-cost, energy-efficient, and suitable for smart agriculture applications. Future enhancements may include integration of advanced analytics and automation using Artificial Intelligence for predictive crop management.

Early prediction of chronic diseases plays a very crucial role in preventive healthcare and reducing long-term medical complications. Our research paper presents an AI – based multi-disease prediction system alongwith the health risk calculator that leverages machine learning techniques to analyze human health data and estimates the likelihood of developing various diseases. The proposed system focuses on multiple coditions, including Diabetes, Hyper Tension, Fatty Lever Diseases , Thyroid Disease, Kidney Disease, Anemia, Stroke and Stress Sickness. We have started with these diseases as we can control and cure these disease at earlier stages with just adjusting our daily lifestyle and diets. Our belief is “ Prevention is Better than cure” and a global healthylifestyle of people to build a disease free nation.

The Smart Complaint Management System (SCMS) is a web-based application designed to enhance the efficiency and transparency of public grievance redressal processes. Traditional complaint handling systems rely heavily on manual documentation and decentralized record maintenance, leading to delays, lack of accountability, and inefficient tracking mechanisms. The proposed system provides a centralized digital platform where users can register, submit complaints related to civic issues, upload supporting images, and monitor complaint status in real time. The system incorporates secure authentication, structured database management, and role-based access for administrators to review, update, and resolve complaints effectively. Developed using HTML, CSS, and JavaScript for the frontend, PHP for backend processing, and MySQL as the relational database, the system ensures secure data handling and streamlined communication between citizens and authorities. The implementation of SCMS significantly reduces paperwork, enhances transparency, improves response time, and promotes efficient public service delivery. This system contributes to the advancement of e-governance initiatives by providing a scalable and user-friendly digital grievance management solution.

Automated detection and counting of blood cells in thin blood smear images have become essential in modern medical diagnostics due to the limitations of manual microscopic analysis. Traditional methods are labor-intensive, time-consuming, and prone to human error, especially when processing large volumes of samples. This paper presents a comprehensive study on automated techniques for identifying and counting red blood cells (RBCs), white blood cells (WBCs), and platelets using image processing and deep learning approaches. The proposed methodology integrates preprocessing, segmentation, feature extraction, and classification using convolutional neural networks (CNNs) and object detection models such as YOLO and RetinaNet. Advanced segmentation techniques like U-Net and watershed algorithms are employed to handle overlapping cells and noisy backgrounds. The system is evaluated using publicly available datasets such as BCCD and LISC, with performance metrics including accuracy, precision, recall, and F1-score. Experimental results demonstrate that deep learning-based approaches significantly outperform traditional methods, achieving high accuracy and robustness in varying imaging conditions. The study highlights the potential of automated systems to assist hematologists in rapid and reliable diagnosis of blood-related disorders, including anemia, leukemia, and infections. Future work focuses on improving real-time deployment and handling complex pathological cases.

The rapid growth of digital education platforms has significantly improved access to learning resources; however, accessibility for visually impaired learners remains a critical challenge due to the predominance of visually driven interfaces. This paper presents QuizTalk, an AI-powered voice-based quiz system designed to enable visually impaired users to participate in assessments independently and efficiently.
The proposed system integrates Text-to-Speech (TTS) to de-liver quiz questions audibly and Speech-to-Text (STT) to capture user responses through voice commands, thereby eliminating the need for visual interaction. Additionally, the system incorporates artificial intelligence using the ChatGPT API to generate dynamic quiz questions and provide real-time explanations for incorrect answers, enhancing conceptual understanding and engagement. Experimental evaluation and system testing demonstrate im-proved accessibility, usability, and interaction efficiency com-pared to traditional quiz systems. The proposed solution pro-motes inclusive education by enabling hands-free interaction and reducing dependency on external assistance. Furthermore, QuizTalk lays the foundation for future enhancement, includ-ings multilingual support, offline voice processing, and adaptive learning systems.

Pharmaceutical logistics storage is a critical component of pharmaceutical management at community health centers (Puskesmas) because it affects the quality, safety, and availability of medications. Issues such as expired medications, substandard storage arrangements, and limited storage facilities are still encountered at several Puskesmas in Kendari City. This study aims to identify factors related to pharmaceutical logistics storage at Community Health Centers in Kendari City in 2025, including spatial arrangement, medication stock organization, medication stock record-keeping, and medication quality monitoring. This study employs a quantitative method with a cross-sectional design. The research was conducted at Community Health Centers in Kendari City in 2025. The study population consisted of all pharmacy staff at community health centers in Kendari City, with total sampling used as the sampling technique. Data were collected using a questionnaire that had been tested for validity and reliability, and analyzed using SPSS with the chi-square test. The results showed a significant relationship between medication inventory recording (p-value = 0.044) and pharmaceutical logistics storage, whereas spatial arrangement (p-value = 0.086), medication stock recording (p-value = 0.065), and medication quality monitoring (p-value = 0.054) did not show a relationship with pharmaceutical logistics storage. This study emphasizes that continuous improvement in storage management is essential in accordance with pharmaceutical service standards.

Agriculture serves as the backbone of the global economy, and crop yield is significantly hampered by various diseases and pest infections. Traditional methods of detection rely on manual observation by an expert, which is often time-consuming, labor-intensive, and prone to human error, particularly in remote areas. To address these challenges, this paper proposes an automated system for the detection of crop diseases and pests using Deep Learning techniques. The proposed model achieved a validation accuracy of 100% and demonstrated excellent performance across precision, recall, and F1-score metrics.

Phishing is one of the most common cyber threats that targets users by creating fake websites, emails, or links to steal sensitive information such as usernames, passwords, banking credentials, and personal data. As phishing attacks are becoming more sophisticated, there is a growing need for an intelligent system that can detect and prevent such malicious activities in real time.The Phishing Detection and Prevention System is designed to identify phishing websites and suspicious URLs using machine learning techniques and URL-based feature analysis. The system analyzes various characteristics of a given URL, such as URL length, presence of special characters, domain age, HTTPS usage, and suspicious patterns, to classify whether the website is legitimate or phishing. A trained machine learning model processes these features and provides accurate predictions to alert users before they access harmful websites.The system also includes a user-friendly interface where users can enter a URL and instantly receive security feedback. By combining detection algorithms with preventive alert mechanisms, the project helps users avoid cyber fraud and enhances online safety.This project demonstrates the practical application of cybersecurity and machine learning concepts to build an effective, scalable, and efficient phishing detection solution. It can be further extended as a browser extension or integrated with email security systems for broader protection against phishing attacks.

There is much inefficiency that has become part and parcel of the Indian rental industry, including dependence on brokers, unclear rental rates, and fragmented property listings. This leads to the challenge faced by tenants in identifying appropriate rental accommodation. This paper proposes TrustNest, an end-to-end AI-powered rental discovery platform that seeks to overcome these challenges by connecting tenants and landlords in a streamlined way through a single website. The proposed solution makes use of a three-tier architecture comprising a React.js front-end, a Python Flask REST API back-end, and a MySQL database. One of the novel aspects about this study is the utilization of machine learning algorithms utilizing a Random Forest Regressor to calculate fair rental prices based on several variables like location, size of the property, type of property, proximity to metro stations, and IT parks. Listings will be supplemented by a Livability Score of zero to one hundred and a price declaration that denotes whether it is a Great Deal, Fair Price, or Overpricing.

The development of adaptive and responsive artificial intelligence in 3D interactive environments often requires extensive training time and massive datasets. This study introduces a live training imitation learning system designed to rapidly train autonomous agents using real-time human demonstrations. Utilizing the Godot 4 engine integrated with a Python-based backend, the system combines Behavioral Cloning (BC) and Proximal Policy Optimization (PPO). The methodology focuses on eliminating frame-of-reference mismatches by utilizing agent-local coordinates and basis vectors, alongside frame-stacking for enhanced temporal awareness. Human gameplay trajectories are recorded through intent-based actions and used to pre-train the agent via the imitation library, followed by PPO refinement driven by a custom reward function shaping approach velocity, jump-matching, and alignment. The results demonstrate that agents trained through this live pipeline achieve stable pursuit behaviors significantly faster than traditional RL methods, while avoiding degenerate strategies such as environment exploitation. This paper details the architecture, training pipeline, and synchronization mechanisms that enable seamless transitions between data collection and reinforcement learning, providing a robust framework for real-time AI development in applied technology sciences.

Water quality is a critical factor for environmental health, human consumption, and aquatic life. Monitoring water quality in remote, hazardous, or physically inaccessible water bodies is a challenging task when performed manually. This paper presents the design, development, and testing of a Bluetooth-controlled RC Boat equipped with a pH sensor and turbidity sensor for real-time water quality monitoring. The boat is built on a lightweight base frame powered by DC motors driven through a motor driver circuit, and is remotely controlled via a smartphone Bluetooth application. The system uses an Arduino UNO microcontroller as the central processing unit to collect sensor data and control motor operations. The boat can be navigated to areas where manual testing is risky or physically impractical, collect water quality data, and transmit it wirelessly to the user's smartphone. The proposed system offers a low-cost, portable, and effective solution for water quality assessment in industrial zones, lakes, rivers, and water treatment facilities. Key results show pH sensor accuracy within ±0.2 units, turbidity variation below 5%, Bluetooth range up to 15 m, and battery life of 45–60 minutes per charge.

Cerebral Palsy (CP) is a neurological condition that mainly affects how a person moves, controls muscles, and maintains posture. It happens because of damage to the developing brain, which usually occurs before birth, during delivery, or shortly after birth.
It is important to understand that Cerebral Palsy is not contagious, meaning it cannot spread from one person to another. Also, it is a non-progressive condition, so the brain damage does not get worse over time. However, the symptoms may change as the child grows.
Cerebral Palsy is one of the most common physical disabilities that begin in childhood. It affects around 1 in 500 newborns, and globally, about 17 million people are living with this condition.
Rather than being a single disease, Cerebral Palsy is a clinical condition where children show similar symptoms due to early brain injury. These symptoms can vary a lot depending on the type of movement problem, the severity, and which part of the body is affected.
Currently, there is no complete cure for Cerebral Palsy, but medical science is making progress in both prevention and treatment. For example, proper medical care during premature labor and special treatments for newborns can help reduce the severity of the condition.
The main goal of treatment is to:
• Improve movement and daily functioning
• Reduce complications like seizures, feeding issues, and bone problems
• Help individuals live a better and more independent life
These treatments include early therapy, medical care, rehabilitation technologies, and prevention of secondary complications.

In developing countries where concrete is widely used, the high and steadily increasing cost of concrete has made construction very expensive. This coupled with deleterious effect of concrete production on the environment has led to studies on various materials which could be used as partial replacement for coarse aggregate. This project is experimented to reduce the cost of concrete. The only way to reduce and tackle these problems is reuse and recycles. In this project work, experiments have been conducted with the collection of materials required and data required for mix design are obtained. The M20 grade concrete is designed as per Indian standard code for conventional concrete. The water cement ratio is maintained for this mix design is 0.45. The granite wastes were properly cut down to the size of coarse aggregate and then they were mixed with the concrete in 10%, 20%, 30%, 40%. Cubes were casted with these concrete mixes and subjected to curing of 7 days, 14 days, 28 days and their strength is determined. The determined compressive strength was compared with the conventional concrete cube’s strength. Of the above percentage mixes, the perfect percentage mix of granite with coarse aggregate is found and can be brought to use.

Traffic congestion has emerged as one of the most pressing urban challenges in metropolitan cities across India. Chennai, being a major economic, educational, and cultural hub, experiences severe traffic congestion due to rapid urbanization, population growth, increased vehicle ownership, and inadequate road infrastructure. This study examines the nature of traffic congestion in Chennai and its impact on the daily lives of citizens. The research focuses on the social, economic, health, and environmental consequences of traffic congestion, including time loss, stress, reduced productivity, air pollution, and road safety concerns. Both primary and secondary data are used to analyze commuting patterns, public perception, and the effectiveness of existing traffic management measures. The findings reveal that traffic congestion significantly affects work-life balance, physical and mental health, and overall quality of life. The study emphasizes the need for improved public transport systems, better urban planning, strict traffic regulation enforcement, and public awareness to reduce congestion in Chennai.

One of the most common problems people face when they visit a new place is that they cannot find basic nearby services like photocopy shops, stationery stores, or printing centres. Small local vendors are usually not listed on any digital platform, which creates problems for both the user and the vendor. In this paper, we have built a location-based system that helps users find nearby vendors and compare their service prices. The system uses the browser's GPS to get the user's location, a Node.js backend to find nearby vendors from a MongoDB database, and Leaflet.js to show them on an interactive map. The frontend is built using React.js and is designed to be simple and easy to use. The main features of the system include distance-based vendor filtering using the Haversine formula, display of per-service prices, and a price comparison view. During testing, the system was able to find vendors accurately and returned results in under 2 seconds.

Transportation management practices have become increasingly important in explaining logistics management outcomes in a global trade environment shaped by border delays, infrastructure gaps, service inefficiencies, and visibility challenges. Despite growing research on logistics performance, fewer studies have examined transportation management practices as direct predictors of logistics management outcomes using a recent public dataset. This study therefore examined the relationship between transportation management practices and logistics management using the 2023 World Bank Logistics Performance Index dataset. The study adopted a quantitative correlational design based on secondary data for 139 countries. Transportation management practices were operationalized through customs efficiency, infrastructure quality, ease of arranging international shipments, logistics competence, and tracking and tracing capability, while logistics management was measured through timeliness. Data were analyzed using descriptive statistics, diagnostic tests, Pearson correlation, and multiple linear regression. The descriptive results showed that timeliness had the highest mean score (M = 3.242, SD = 0.565), while customs efficiency had the lowest (M = 2.800, SD = 0.625). Correlation analysis indicated that all transportation management variables had strong positive and statistically significant relationships with timeliness: customs (r = .863, p < .001), infrastructure (r = .863, p < .001), international shipments (r = .831, p < .001), logistics competence (r = .898, p < .001), and tracking and tracing (r = .911, p < .001). Regression results showed that the model was significant, F(5, 133) = 152.000, p < .001, explaining 85.1% of the variance in timeliness (R² = .851). However, only logistics competence (B = 0.273, p = .012) and tracking and tracing (B = 0.427, p < .001) remained significant predictors. The study concluded that transportation management practices are significantly related to logistics management. It recommends greater investment in logistics competence, digital tracking systems, and coordinated transport reforms to improve shipment timeliness.

This project, titled “Operating a Bell using Arduino Board,” is designed to automate the control of a bell using an Arduino microcontroller. The main objective is to develop a simple and efficient system that can ring a bell at predefined times or based on user input. In this system, the Arduino board acts as the central controller, processing programming instructions and activating the bell through a relay module or buzzer. This system can be effectively used in schools, colleges, and offices to automate bell operations, thereby reducing manual effort and improving time accuracy. The proposed system is cost-effective, reliable, and easy to implement. It also helps in understanding the fundamental concepts of embedded systems, programming, and automation.

Problem: In quantum chemistry, calculating molecular energies is an extremely complex process. Classical computers cannot solve these problems at scale because more electrons mean exponentially more memory and time — 10 electrons require 2¹⁰ = 1,024 states, while 100 electrons require 2¹⁰⁰ states, which is practically impossible to solve.
Our Approach: We use the VQE (Variational Quantum Eigensolver) algorithm to solve this issue. It is a hybrid process where both quantum and classical computers work together. The quantum computer generates quantum states, and the classical computer optimizes those states. We implemented this on the real hardware of IBM Quantum.
What We Got: Using the VQE algorithm, we obtained the ground state energy of −0.469 Hartree for the hydrogen atom, compared to the theoretical value of −0.471 Hartree — an error of only 0.002 Ha. For the H₂ molecule we obtained −1.143 Ha (exact: −1.146 Ha). The reaction energy (when two H atoms combine to form H₂) was −0.204 Ha, differing from the theoretical value.
What We Prove: This demonstrates that VQE algorithms accurately work on quantum hardware.

Today’s workplaces need more than basic attendance logs. Big offices especially struggle to know who’s actually present and working at their stations. Swipe cards and biometric devices are fine for clocking in and out, but they can’t say whether an employee really sat at their desk all day. This project steps in with a real-time employee tracking and activity monitoring system, built with OpenCV and the lightweight YOLOv4-tiny deep learning model. All you need is a regular camera pointed at the workspace. The system constantly scans the video feed, quickly and accurately looking for people.
Each employee’s desk is mapped as a digital Region of Interest (ROI). If the system spots someone in that area, they’re counted as “Present”; if not, “Absent.”

The present study successfully developed and validated a simple, accurate, and cost-effective UV–Visible spectrophotometric method for the simultaneous estimation of plant-based anti-inflammatory agents, berbaloin and plumbagin. The method was based on their absorption maxima at 291.0 nm and 267.0 nm, respectively, with an isobestic point at 280.0 nm, confirming its suitability for simultaneous analysis. Both drugs obeyed Beer’s law within the concentration ranges of 4–12 µg/mL for berbaloin and 10–50 µg/mL for plumbagin, exhibiting excellent linearity with correlation coefficients (R²) of 0.9985 and 0.9866, respectively. The method demonstrated high precision, as indicated by low %RSD values for repeatability, intraday, and interday studies. Ruggedness and robustness studies further confirmed the reliability of the method, with %RSD values remaining below acceptable limits under varying conditions. The limits of detection (LOD) and quantification (LOQ) indicated good sensitivity of the method for both analytes. Overall, the proposed method is rapid, precise, accurate, sensitive, and robust, making it highly suitable for routine quality control and simultaneous estimation of berbaloin and plumbagin in pharmaceutical and herbal formulations.

A novel and efficient UV spectrophotometric method has been developed for the simultaneous quantification of Erythromycin and Niacinamide in topical formulations. Characterization revealed Niacinamide as a white crystalline powder and Erythromycin as white to off-white crystals, both confirming purity. Solubility tests indicated Niacinamide's high hydrophilicity, while Erythromycin preferred organic solvents. The method demonstrated linearity in concentrations of 2–10 µg/ml, showing excellent correlation (R² values of 0.998 for erythromycin and 0.992 for niacinamide) and precision (RSD values < 2%). The FTIR analysis confirmed the chemical identities of both compounds. Additionally, limits of detection were established at 1.0837 µg/ml for Niacinamide and 2.1165 µg/ml for Erythromycin. This validated method is deemed suitable for routine quality control in pharmaceutical applications due to its accuracy, sensitivity, and reliability.

The rise of social media platforms has fundamentally transformed the way consumers express opinions about brands, products, and services. In today's hyper-connected world, a single post on Instagram, Twitter, or Google Reviews has the potential to shape the perception of an entire brand overnight. This research paper empirically investigates the relationship between social media sentiment — both positive and negative
— and its tangible impact on the brand image of businesses operating in Navi Mumbai. Using a mixed approach of primary data collected through structured questionnaires from 120 respondents (consumers and business owners), and secondary data drawn from academic literature, the study identifies how businesses respond to online reviews, how customers form or revise their brand perceptions, and what sentiment patterns emerge most frequently across platforms. The study also draws on behavioural insights: how herd mentality, emotional amplification, and platform trust levels all play a role in shaping brand narratives. Key findings suggest that negative reviews have a disproportionately strong impact on brand image compared to positive ones — a phenomenon consistent with loss aversion theory. The paper concludes with actionable recommendations for brand managers, digital marketers, and small business owners in Navi Mumbai to leverage sentiment analysis tools to proactively protect and strengthen their brand identity.

In many African society, the belief in witchcraft is real, the craft is usually acquired through initiation by older members of the movement of witchcraft through supernatural means. Membership of this movements are said to be nefarious, and notorious for witch hunting and for other evil happenings, including death or other misfortunes. Practitioners or close allies believed that the practice of witchcrafts is for high wisdom, divinity and protection. It is pertinent to note that, the cases of suspected witchcraft activities are largely within the rural settings and among the poor. The elderly and weak persons are most vulnerable, and risk mob attacks and banishment from their communities, or sometimes they could be put to death. The aftermath of suspected witchcraft practices on victims cannot be overemphasized in many African societies, including Nigeria. Several Non-Governmental Organization (NGOs) and other human right organizations since 2017 have championed the campaign against stigmatization of witchcraft yet to no avail. This paper adopt the doctrinal method and it discovers that despite the criminization of the practice of witchcraft on section 210 of the Nigeria criminal code but it failed to defined the real meaning of witchcraft. It is the submission of this paper that the Nigerian criminal code, a colonial era document is bereft of proper understanding of witchcraft practices to which it proscribed. The paper further submit that witchcraft is a movement of divinity and wisdom with which an individual could be endowed, and protected. Resolution 47/8 of the human right council of July 12th 2021 hitherto bars the unfair accusation of witchcraft and harmful attack on victims. Conclusively, the practice and belief in witchcraft is human and it is a right for all Nigerians at home and diaspora to be initiated and enjoy the practice whereof. This is consistent with equity and equality. It is also the recommendation of this work that witchcraft are to be legalized in the Nigerian constitution for a clearer meaning and demystification.

In today’s highly competitive and dynamic business environment, organizations are increasingly focusing on human capital as a key driver of success. Among various human resource practices, employee engagement has emerged as a critical factor influencing organizational performance. Employee engagement refers to the emotional, cognitive, and behavioral connection that employees have with their work and organization.
Engaged employees are more productive, committed, and motivated to contribute to organizational goals. On the other hand, disengaged employees may lead to low productivity, absenteeism, and high turnover. Therefore, understanding the concept of employee engagement and its impact on employee motivation has become essential for modern organizations.
This study aims to explore the relationship between employee engagement and motivation, identify key factors affecting engagement, and suggest strategies to enhance employee motivation through engagement practices.

In the modern digital era, the exponential growth of data has created a significant demand for efficient, scalable, and secure storage systems. Cloud computing has emerged as a dominant paradigm that provides on-demand access to computing resources and storage infrastructure. However, most commercial cloud storage platforms such as Google Drive and Dropbox require paid subscriptions and complex configurations, making them less suitable for academic and small-scale applications.
This research presents Mini Drive, a lightweight cloud-based file storage and sharing system developed using Node.js and Supabase. The system enables users to upload, store, retrieve, and download files securely through a web-based interface integrated with cloud infrastructure. The backend is implemented using RESTful APIs, while Supabase provides storage and database services with built-in security mechanisms such as Row Level Security (RLS).
The system follows a modular architecture to ensure scalability, flexibility, and efficient data handling. The primary objective is to demonstrate that a fully functional cloud storage system can be developed using free-tier technologies without compromising performance and security. The implementation focuses on core functionalities including file upload, file listing, and file download, along with a user-friendly interface.

With the growing demand for intelligent security infrastructure, conventional access control systems such as mechanical locks, RFID cards, and PIN-based authentication mechanisms have proven vulnerable to duplication, theft, and misuse. This research presents IntelliGate, a real-time Artificial Intelligence (AI)-driven facial recognition-based smart access control system integrated with IoT-enabled hardware components. The system utilizes computer vision algorithms implemented through OpenCV and facial encoding techniques powered by Dlib to authenticate individuals. Upon successful identification, a Raspberry Pi Foundation-developed Raspberry Pi 4 activates a relay module connected to an electromagnetic lock. Unauthorized access attempts trigger automated logging, image capture, buzzer alerts, and real-time email notifications.
Experimental analysis demonstrates an average recognition accuracy of approximately 94% with low response latency, making IntelliGate a scalable, cost-effective, and intelligent security solution for residential, institutional, and industrial applications.

This research paper presents a comprehensive analysis of e-commerce sales data using Python-based data analytics and visualization techniques. With the rapid growth of online shopping platforms, large volumes of transactional data are generated daily, making it essential to analyze and extract useful insights for business growth. The primary objective of this study is to clean raw sales data, perform exploratory data analysis (EDA), and visualize important trends such as monthly sales patterns, product performance, and customer purchasing behavior.
The study utilizes powerful Python libraries including Pandas, NumPy, Matplotlib, and Seaborn to process and analyze the dataset. Data cleaning techniques such as handling missing values, removing duplicates, and correcting inconsistencies are applied to ensure accuracy. After preprocessing, various visualization techniques are used to represent the data in graphical form, making it easier to interpret patterns and trends.
The results of this research help in identifying high-performing products, seasonal demand variations, and customer buying habits. These insights can assist businesses in making data-driven decisions, improving marketing strategies, and increasing overall profitability. Unlike traditional research, this study focuses on practical implementation and visualization-driven insights, making it more useful for real-world applications.

MediCare is a comprehensive family healthcare management system designed to address the growing challenges in managing medical routines such as medicine schedules, doctor appointments, and emergency situations. In many households, healthcare data is scattered across multiple applications, making it difficult to track and manage effectively. MediCare integrates all essential healthcare features into a single web-based platform.
The system uses Supabase for cloud-based data storage and real-time synchronization, ensuring reliable and secure access to health information. A major feature of the system is the voice-assisted alarm, which uses Text-to-Speech technology to notify users about medicine timings and doctor appointments. This feature significantly improves accessibility for elderly users and visually impaired individuals.
Additionally, MediCare includes an emergency SOS system and a basic image-based pill identification feature. The system is lightweight, scalable, and easy to use, making it suitable for both academic and real-world applications.

The current lifestyle has made children more susceptible to carious lesions all around the world. One of the major contributing microorganisms is Streptococcus mutans, which causes initiation of enamel caries and is a major etiological pathogen. For the prevention of caries and therapeutic purposes, mouthwashes or mouth rinses are promoted a lot. The first mentioned mouth rinse was seen around 2700 BC in Chinese medicine. Among antimicrobial agents, chlorhexidine is considered gold standard but it has side effects such as extrinsic stain formation and bitter taste. Glycyrrhiza (licorice), a herb, which has medicinal value and was used in ancient times as a remedy for a great diversity of ailments and sweetener. Roots of Glycyrrhiza contain a high concentration of saponin and glycyrrhizin, which are supposed to be sweetening agents. Green tea, which is very rich in fluoride, with its catechin content attribute to its benefits. Polyphenols and various flavonoids such as catechin, catechin gallate, and catechin proanthocyanidin are its main constituents. Hence, the aim of the study was to evaluate and compare the antimicrobial activity of ethanolic licorice extract with and without green tea extract and sodium fluoride mouthwash against S. mutans.

The quick rise in elastic unused generation positions a severe eco-friendly task universal. This project discovers an innovative and workable explanation by using recycled plastic unused as a partial replacement for coarse aggregate in M30 grade concrete suitable for rigid concrete structure. Using Ordinary Portland Cement (OPC), 20 mm coarse aggregate, and the given parameters (workability 75 mm slump, water-cement ratio 0.45, 0.5% admixture), the concrete mix is designed as per IS 10262:2019 guidelines. Plastic waste replaces coarse aggregate by weight at four levels: 7%, 11%, 15%, and 20%. The primary objectives are to reduce natural aggregate consumption, promote circular economy principles, minimize landfill burden, and develop eco-friendly rigid concretes with comparable or improved properties such as reduced self-weight and enhanced flexibility. The step-by-step mix design yields practical proportions for laboratory and field trials. This approach not only addresses plastic pollution but also supports workable infrastructure development in road construction, offering a cost-effective and environmentally responsible alternative without compromising the structural requirements of rigid pavements.

Natural Language Processing (NLP) has witnessed a revolutionary transformation with the advent of deep learning techniques, particularly transformer-based architectures. This research paper presents a comprehensive comparative analysis of state-of-the-art transformer models, including BERT, RoBERTa, GPT, T5, and XLNet, focusing on their performance in text classification and sentiment analysis tasks. We evaluate these models across multiple benchmark datasets, including GLUE and SuperGLUE, and analyze their strengths, limitations, and computational requirements. Our experimental results demonstrate that while larger models generally achieve superior performance, the trade-offs between accuracy, computational cost, and inference speed vary significantly across different applications. The study also investigates the effectiveness of various fine-tuning strategies and word embedding techniques, providing practical insights for researchers and practitioners working on NLP applications. Our findings suggest that RoBERTa-large achieves the best overall performance with 89.3% accuracy on the GLUE benchmark, while BERT-base offers a favorable balance between performance and computational efficiency for resource-constrained environments. This research contributes to the growing body of knowledge on deep learning approaches in NLP and provides actionable recommendations for model selection based on specific use cases and resource availability.

Introduction:
Shilajit is a classical Ayurvedic rasayana described as a mineral–organic exudate from mountainous regions. Ancient texts describe its types, origin, and medicinal importance, with Lauha Shilajatu considered the most potent.
Materials and Methods: Key Ayurvedic texts were reviewed to compile information on the origin, types, purification (Shodhana), therapeutic uses, dosage, and safety of Shilajit.
Results: Classical sources describe specific Shodhana procedures, purity tests, and indications in prameha, sthaulya, pandu, kushtha, rajyakshma, urinary diseases, and general debility. Shilajit contains humic substances, fulvic acid, dibenzo-α-pyrones, minerals, and diverse bioactive compounds.
Discussion: Traditional evidence highlights Shilajit’s rejuvenative and therapeutic potential, emphasizing proper purification and dosage. While widely used in Ayurveda, further scientific research is required to validate its pharmacological actions and standardize quality.

Now a days increasing population in the world, then the constructions for living humans could be more like houses, bridges, appartments etc. Rapidly wastage of concrete aggregates is increasing. To minimize the wastage of concretes by utilizing properly with hot mix asphalt mixtures for construction of road pavements. The objective of this study is to understand the vitality of using RCA for the construction of bituminous pavements. Recycled concrete aggregates (RCA) materials are resulted from milling process. In this study samples of Recycled concrete aggregates (RCA) materials were collected and analysed for suitability of their usage in flexible pavements. Their characteristics including gradation, Aggregate Impact value, Aggregate Crushing value, Specific gravity, Flakiness Elongation Index, Loss Angles Abrasion value, and Water absorption were determined and compared to the standard specifications. The present study deals with evaluation of Marshall Stability, de-formation and the moisture damage resistance values of HMA which is mixed with RCA in varying proportions.. The Marshall mix design method was adopted in this study to determine the optimum binder content (OBC) for the asphalt mixes containing five aggregate combination with RCA contents of 0, 10, 20, 30 percent was found to be 5, 5.5, 6 percent which is optimum respectively.
Large quantities of natural aggregates (NA) are required in the manufacturing of asphalt mixtures for pavements. The extraction of NA generates high environmental impacts that negatively affect environmental preservation and conservation. Diverse alternative materials obtained in construction and demolition processes in civil construction worked have been studies as possible replacements for NA, with the purpose of reducing these environmental impacts. The foregoing study presents a state of knowledge review referring to the use of recycled concrete aggregates (RCA) when these are used in the manufacturing of asphalt mixtures (mainly hot-mix asphalt HMA type). Environmental aspects are presented, as well as possible benefits and limitations of using RCA as replacements for NA in asphalt mixtures. However, it is not possible to establish a behavior pattern, since the characteristics of the RCA are very heterogeneous and vary from the original source of the concrete. It is highlighted from the literature that RCA has a wide potential for use in the manufacture of asphalt mixtures, since in most studies, the mixtures with these materials comply the quality requirements contemplated by the construction specifications, mainly in low-volume roads. Additionally, based on the literature review, some recommendations and suggestions are presented for future research. Effective management and handling of construction and demolition waste (CDW) can yield significant technical and environmental benefits for road pavement construction.

In Human Resource Management (HRM), AI has significantly changed the way we recruit and select employees. Companies have to deal with increasing demands to recruit talented individuals for their organisation as quickly, accurately and cheaply as possible while handling large amounts of applicants and improving how candidates experience the hiring process. Many of the old methods used for recruiting are slow, costly, present a high degree of unconscious bias and inconsistencies in the decision-making process. AI-based recruiting tools such as applicant tracking systems (ATS), recruitment chatbots, predictive analytics, resume screening and automated interview scheduling are now showing great potential to improve hiring efficiency.
This study will review how AI-based recruitment tools impact on the hiring efficiency of organisations. The outcome of this study will look at how AI affects: recruitment speed, cost reduction, quality of hiring decisions, employee perceptions and the balance between technology and human judgment. The research design was based on a quantitative approach using a structured questionnaire administered to 115 individuals from various industries, including employees, HR professionals, managers and consultants. Descriptive statistics and Chi-square tests were used to analyse the data and test the hypotheses..
Data shows that AI can significantly improve recruiting efficiency, lower the time it takes to hire someone, increase recruiter productivity, and create higher-quality hiring decisions. Furthermore, many of the respondents think AI is a tool to assist human recruiters with making decisions, instead of replacing human recruiters' role in that process. Other major issues that emerged include data privacy and algorithmic bias, while costs associated with implementing AI and employee pushback were found to be less of an issue. This research demonstrates that the best way to use AI in recruiting is through the collaborative Human + AI recruitment model, where AI completes repetitive operational tasks while the human recruiter retains all responsibilities related to strategy and ethical decision making.

Metaverse showcasing is developing as a effective device for improving brand encounter through immersive and intelligently advanced situations. This think about analyzes buyer mindfulness, engagement, and components impacting the adoption of metaverse promoting in Pune. It analyzes client behavior and preferences, centering on components such as interactivity, personalization, and advancement. Information collected through a organized survey demonstrates developing intrigued in metaverse stages, in spite of the fact that appropriation remains at an early organize. Concerns related to openness, mechanical complexity, and information security still exist. The study concludes that metaverse showcasing has solid potential to construct more profound brand associations if these challenges are addressed.

Because of their medicinal advantages and historical relevance in traditional medical systems, herbal medications are utilized extensively worldwide. However, maintaining consistent quality, safety, and efficacy is extremely difficult due to their complex chemical composition and variability. In order to solve these issues, regulatory frameworks created by the International Council for Harmonization (ICH) and the European Union (EU) are essential. While ICH gives generic pharmaceutical quality standards that apply to herbal formulations, the EU offers particular recommendations designed for herbal medical goods.
The EU and ICH guidelines for quality control, such as raw material standardization, Good Agricultural and Collection Practices (GACP), analytical validation, impurity control, and stability testing, are thoroughly covered in this paper. By incorporating these criteria, harmonization is ensured, global acceptance is increased, and the creation of superior herbal therapeutic products is supported.

Bālagraha is a distinctive concept described in Ayurvedic pediatrics (Kaumarbhritya), referring to a group of disorders affecting children that are traditionally attributed to subtle or supernatural influences. Classical Ayurvedic texts such as Kashyapa Samhita, Charaka Samhita, and Sushruta Samhita provide detailed descriptions of their etiology, classification, symptomatology, and management. Due to the physiological immaturity of children—characterized by Aparipakva Dhatus, Manda Agni, and Alpa Bala—they are considered highly susceptible to such afflictions. This review critically analyzes the concept of Bālagraha, its diagnostic framework, and therapeutic approaches including Daivavyapashraya, Yuktivyapashraya, and Satvavajaya Chikitsa. It also explores possible correlations with modern pediatric conditions such as infections, neurological disorders, and psychosomatic illnesses. The study highlights the clinical relevance and integrative potential of Bālagraha in contemporary pediatric healthcare.

In the pharmaceutical business, basic testing of medications and pharmaceutical substances is an essential part of quality assurance and control. Before pharmaceutical items are delivered to customers, these tests guarantee their identification, purity, potency, and safety. Physical, chemical, and instrumental techniques of basic analytical testing for drugs are all covered in detail in this work. There includes a thorough discussion of procedures including identification tests, purity tests, limit tests, assay techniques, and dissolution tests.
Additionally emphasized are the functions of pharmacopoeial standards and regulatory compliance. Maintaining medication efficacy, reducing hazards, and guaranteeing public health safety all depend on an understanding of these fundamental tests.

India is emerging as a vibrant hub where traditional Indian knowledge systems and Artificial Intelligence are creating a power convergence. India is a knowledge civilization where oral tradition, the gurukul system and dharma-related knowledge have been preserved for hundreds of centuries. Where IKS gives deep ethical knowledge through Dharma, consciousness and with wisdom. Meanwhile, AI provides modern tools to preserve, interpret, and circulate ancient heritage on the global scale.
Here I am using Mahabharat as a philosophical roadmap. The Mahabharata keep a close eye on Dharma which means the idea that right action must be based on context, empathy and responsibility but Modern AI technology often prioritize efficiency. This paper also maps AI concepts on to the iconic characters from epic that capture different AI ethic. For example, Krishna symbolizes the ultimate strategic wisdom and ethical guidance that AI lack meanwhile Sanjay’s ability to see everything in real time serves as an excellent analogy for modern surveillance and data transmission.
The Mahabharat’s episodes on JioHotstar, like dice game of autonomy vs responsibility, divine weapons. All of these can be connected with AI tools like natural language processing, generating models and knowledge graphs, that help to analysis and digitalize the epic like the Mahabharat.
This study discusses the dharma-based AI ethics that suggest principles like anima (non-violence) and karma that provide necessary human values for AI development. This paper argues that without Dharmic core AI can become risks like Duryodhana’s power without a moral compass and Bhishma, who depicts rigid knowledge that fails to accept to human suffering. Ultimately, the paper argues that AI can give breath to the life of the IKS in the ‘Dharma to Data’ journey that provides through ethical negotiation that prioritises human values. This study embraces classical literature, cultural narrative and cognitive science to offer high impact on future AI research.

The growing demand for safe, skin-friendly, and environmentally friendly beauty products is driving a shift from traditional alcohol-based aftershave lotions to botanical formulations. Although traditional aftershave has antiseptic properties, it often contains high concentrations of alcohol and synthetic additives, which can cause irritation, dryness, erythema, and micro-abrasions of the skin, especially in people with sensitive skin. In response to these concerns, this study focused on the development and evaluation of herbal aftershave lotions that incorporate natural ingredients with proven dermatological benefits.

The main objective of this study was to develop a stable, effective and skin-compatible herbal aftershave lotion using herbal ingredients. Particular attention was paid to the combination of menthol and aloe vera due to their well-documented pharmacological properties. Menthol, derived from peppermint oil, was used for its immediate cooling, soothing, and mild analgesic effects, while aloe vera (Aloe barbadensis) was chosen for its moisturizing, anti-inflammatory, and healing properties. Other optional herbs such as neem extract, witch hazel, chamomile, and tea tree oil are thought to enhance antibacterial and therapeutic effects.

Millions of people die each year from tuberculosis (TB), one of the worst infectious diseases. We provide a broad overview of tuberculosis (TB) in this paper, covering its pathogenesis, diagnosis, and recommended course of therapy. We looked through PubMed for pertinent TB publications in order to prepare this article. We also looked for relevant publications and clinical guidelines on the websites of global organizations including the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). The purpose of this article is to provide general information to patients, policy makers, health professionals, and the general public. Mycobacterium tuberculosis is the causative agent of tuberculosis (TB), an infectious disease that mainly affects the lungs but can potentially affect other organs. Due to issues including poverty, overcrowding, and restricted access to healthcare, it continues to be a significant global health concern, particularly in developing nations. When an infected individual coughs, sneezes, or speaks, airborne droplets of tuberculosis are released. Active disease is more likely to occur in people with compromised immune systems, such as those with HIV, diabetes, or malnutrition.

Secure file sharing has become a critical requirement in today’s digital world, where sensitive information is constantly transmitted across networks that may not be fully secure. Traditional security approaches, which rely on perimeter-based protection, are increasingly ineffective due to the rise of sophisticated cyber threats and the growing adoption of cloud-based systems. This paper proposes a secure file-sharing framework that integrates the Diffie–Hellman key exchange mechanism within a Zero Trust architecture. The Diffie–Hellman method allows two communicating parties to generate a shared secret key over an insecure channel, which can then be used for encrypting files. Meanwhile, the Zero Trust model ensures that every access request is verified, authenticated, and continuously monitored. By combining these two approaches, the proposed system enhances confidentiality, integrity, and access control. The study further evaluates system performance, highlighting improvements in security while maintaining acceptable computational efficiency.

Pharmacogenomics is the area of pharmacology that examines how genetic variation affects a patient's reaction to a medication by linking gene expression or single-nucleotide polymorphisms to a drug's toxicity or effectiveness. It seeks to create logical ways to optimize medication therapy in relation to the patient's genotype in order to guarantee maximal effectiveness with few side effects. The development of personalized medicine, where medications and drug combinations are tailored to each person's distinct genetic composition, is promised by these methods. The whole genome application of pharmacogenetics, which studies how medications interact with individual genes, is called pharmacogenomics.

The thyroid hormones (THs), comprising predominantly triiodothyronine(T3) and thyroxine(T4), are the main controllers of metabolic processes and energy homeostasis. Their action permeates the body and controls primitive metabolic rate, thermogenesis, lipid and carbohydrate metabolism, and mitochondrial function. This review outlines the physiological processes by which THs control metabolism, the molecular mechanisms involved, and the clinical applicability of thyroid dysfunction to metabolic health. In addition, we bandy recent developments in thyroid hormone analogues and their implicit remedial operations in metabolic diseases.

Thermal transport in tetragonal tungsten bronze (TTB) materials has attracted significant attention due to their structurally complex frameworks and tunable functional properties. These materials, which include single crystals, thin films, and especially ceramics, exhibit low
thermal conductivity primarily governed by phonon-mediated transport. In ceramic forms, microstructural features such as grain boundaries, defects, porosity, and compositional disorder further enhance phonon scattering. Among intrinsic phonon–phonon interactions, Normal (N-Process) and Umklapp (U-Process) scattering processes play a critical role in determining the total and lattice thermal conductivities. While N-processes conserve crystal momentum and redistribute phonon populations without directly impeding heat flow, U-processes involve momentum transfer to the lattice via reciprocal lattice vectors, thereby introducing thermal resistance. In TTB materials, strong lattice anharmonicity, multi-site occupancy, crystallographic symmetry, CS planes and natural chemical disorder significantly increase the probability of Umklapp scattering, particularly at elevated temperatures. This paper provides a detailed theoretical and materials-oriented analysis of phonon transport in TTB systems, with emphasis on ceramics. The influence of crystal structure, defect chemistry, and processing routes on thermal transport is examined. The experimental thermal behaviour of Sm-doped Sr5LaTi3Nb7O30 TTB ceramic system is discussed to illustrate how compositional, doping and microstructural engineering can tailor phonon scattering and thermal properties. The findings highlight the importance of Umklapp processes in suppressing thermal conductivity and underscore the potential of TTB ceramics for thermoelectric and thermal (energy) management applications.

Sign language is the only medium of communication for the speech-impaired community while the rest of the population communicate verbally. This project aims to bridge this communication gap by proposing a novel approach to interpret the static and dynamic signs in the Indian Sign Language and convert them to speech. A sensor glove, with flex sensors to detect the bending of each finger and an IMU to read the orientation of the hand, is used to collect data about the actions. This data is then wirelessly transmitted and classified into corresponding speech outputs. LSTM networks were studied and implemented for classification of gesture data because of their ability to learn long-term dependencies. The designed model could classify 26 gestures with an accuracy of 98%, showing the feasibility of using LSTM based neural networks for the purpose of sign language translation. To live in a society, it is very important to communicate with each other. But this poses a grave problem for people with hearing disabilities. As they can converse using only sign language, it becomes very difficult for others who don’t know the sign language to understand them. So, the purpose of this paper is to create an interpreter which can convert american sign language into the Engilsh language. Through convolutional neural network we were able to create such an interpreter which can interpret the american sign language.

Among the first line antitubercular agents globally, Rifampicin is considered to be one of the most important agents. Rifampicin was discovered in the 1960s and is a derivative of Streptomyces mediterranei, which was found to revolutionize the treatment of TB, shortening the treatment period considerably and lowering the chances of relapse problems. It is a bactericidal antibiotic that works by blocking Dna-dependent RNA polymerase in the susceptible organisms thus inhibiting the production of RNA and finally causing death of bacterial cells. The main application of rifampicin is combination therapy in treating tuberculosis to avoid development of resistance. It is also used in the treatment of leprosy, brucellosis and prophylaxis of meningococcal and Haemophilus influenzae infections. Rifampicin is phosphorously incorporated when taken orally, is extensively diffused in the body, and is processed in the liver. It is a strong stimulator of the cytochrome P450 enzymes, which lead to multiple drug interactions. Side effects are hepatotoxicity, gastrointestinal, flu-like syndrome and orange body fluid discolouration.
Because of the high-worldwide prevalence of tuberculosis, especially in such nations as India, rifampicin still finds that group of drugs as a part of national TB control programs. Nevertheless, with the increasing resistance to rifampicin, multidrug-resistant tuberculosis (MDR-TB) is being introduced, which is highly difficult to treat. This review article is a discussion of the chemistry, mechanism of action, pharmacokinetics, pharmacodynamics, clinical uses, dosage, adverse effects, drug interactions, resistance mechanisms, monitoring parameters and the perspectives of rifampicin in the future.

Tuberculosis (TB) remains one of the most formidable challenges to global public health, persisting despite centuries of medical advancement. Caused by the bacterium Mycobacterium tuberculosis, it primarily affects the lungs but can disseminate to almost any organ system. This paper provides an extensive review of TB, covering its etiology, pathophysiology, diagnostic modalities, and current treatment paradigms. Furthermore, we explore the socio-economic factors that perpetuate the epidemic and the rising threat of multi-drug-resistant strains. In 2024, approximately 10.6 million individuals were infected globally, with India accounting for nearly 25% of the total burden. By synthesizing current clinical guidelines and epidemiological data, this overview aims to provide a holistic understanding of the disease for students and healthcare professionals at Mewar University. Furthermore, contemporary evidence demonstrates that tuberculosis manifestations exist on a dynamic spectrum from infection to disease, rather than a binary state as previously understood. It is important to understand what causes people to move between these states in order to reduce the burden of tuberculosis and meet the goals of the END-TB Strategy set by the WHO. Vaccination, infection detection, and preventive treatment are essential components of tuberculosis prevention. But the recent rapid growth of Internet technology has made telemedicine a much more important part of treating tuberculosis. This proposal seeks to furnish a thorough examination of the diverse facets of telemedicine in the treatment of tuberculosis. It talks about functional positioning, medical qualifications, the range of applications, the management team, the operational model, the medical standards, the evaluation of medications, precautions, and risk management. It is meant to be a guide for healthcare professionals on how to use telemedicine to help people with tuberculosis.

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'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 shift towards electromobility worldwide is the key approach to counter the 25% of Aglobal carbon dioxide emissions produced by the transportation sector. But the massive adoption of electric vehicles is obstructed by "range anxiety," long waiting times, and the absence of real-time infrastructure information. This paper describes a complete Internet of Things (IoT)-based framework for smart electric vehicle charging management, combining diverse sensing hardware with cloud-based reservation algorithms. We test the performance of the SCT013 current sensor and ZMPT101B voltage sensor, observing an average error of only 0.036A and 1.66V, respectively. Moreover, we examine AI-powered scheduling models, namely Long Short-Term Memory (LSTM) and Random Forest networks, which provide an accuracy of 87.4% in availability forecasting and minimize urban waiting times to an average of 7.8 minutes. The research also measures the sustainability value of solar-integrated stations, proving that a 10-panel solar photovoltaic system can completely compensate for the standard user's daily 37-mile commute, ensuring a 100% carbon-neutral footprint. The results prove that smart coordination can decrease the mean travel time per trip by 9.8% and minimize station peak loads by 25%.

The rapid advancement of artificial intelligence and web technologies has led to the development of intelligent chatbot systems capable of simulating human-like conversations. This paper presents the design and implementation of a web-based chatbot system developed using Next.js and React. The system integrates the Anthropic SDK to process user queries and generate context-aware responses.
The proposed chatbot leverages API-based communication to interact with the AI model and provides real- time responses to user inputs. The system is designed with scalability, modularity, and security in mind, ensuring efficient handling of multiple users simultaneously. The chatbot can be integrated into various domains such as education, customer service, and digital platforms.
This research highlights the architecture, methodology, applications, and challenges of chatbot systems while emphasizing the importance of AI-driven automation in modern systems.

Metal additive manufacturing (AM), commonly referred to as metal 3D printing, has emerged as a transformative manufacturing technology enabling the fabrication of complex metallic components directly from digital models. In the last decade, advancements in laser systems, powder metallurgy, and process monitoring technologies have significantly improved the reliability and industrial adoption of metal additive manufacturing. This review paper discusses recent developments in major metal 3D printing processes including powder bed fusion, directed energy deposition, binder jetting, and sheet lamination techniques. The study also highlights commonly used metallic materials such as titanium alloys, stainless steel, aluminium alloys, and nickel-based super alloys. Furthermore, industrial applications in aerospace, automotive, biomedical, and tooling industries are examined. The review also addresses technical challenges such as residual stresses, surface finish limitations, and high production costs. Finally, future research directions and technological developments are discussed. The study indicates that metal additive manufacturing has strong potential to revolutionize modern manufacturing systems through design flexibility, material efficiency, and rapid product development.

Balrog Chikitsa, the Ayurvedic branch of pediatrics, emphasizes preventive and curative healthcare tailored to the delicate physiology of children. Panchakarma, the five-fold bio-purificatory therapy, is traditionally considered intensive; however, classical Ayurvedic literature advocates its modified and judicious use in pediatric populations. This review aims to critically analyze the role of Panchakarma in Balrog Chikitsa with respect to its indications, adaptations, therapeutic benefits, and safety considerations. A thorough review of classical texts and contemporary studies reveals that therapies such as mrudu Basti and Virachan are particularly beneficial in managing pediatric disorders including respiratory diseases, gastrointestinal disturbances, skin diseases and neurodevelopmental conditions. Properly administered Panchakarma enhances immunity, improves metabolic functions, and supports overall growth and development. However, strict adherence to age-appropriate modifications and clinical judgment is essential to ensure safety.

India has an abundance of therapeutic plants and herbs. Many medicinal and fragrant plants found in India's forests are primarily harvested as raw materials for the manufacture of pharmaceuticals and fragrance products. A medicinal herb garden is one that is grown to support people's health maintenance needs as well as potential acute health problems. It is simple to cultivate and has applications in home cooking, health care, and cosmetics. There are 30 important medicinal plans are can be planted in the home gardens. Medicinal plants can be grown through their seeds, rhizomes, and stem cutting to grow in the home gardens. Plant parts such as bulbs, leaves, rhizomes, root, whole plant, fruits, seeds, flowers, twigs and stems are used for the medicine preparation. Similar to yoga or meditation, gardening is an ancient activity that keeps the body healthy, stimulates the mind, and uplifts the spirit. In addition to improving the family's nutritional condition and overall health, cultivating and keeping medicinal plant species in home gardens is an affordable alternative to using expensive medications and pills to treat a variety of illnesses. In one of the Sanskrit shloka, it is said that there is no plant on the earth which cannot be used as a medicine. What is needed it’s know how to use.

Ayurveda, the traditional system of Indian medicine, encompasses a well-structured and logical framework for the acquisition and validation of knowledge. Classical texts (Samhitas) describe comprehensive methodologies for inquiry, learning, and scientific discussion. Among these, Vadamarga represents a systematic approach to scholarly debate and validation of knowledge, reflecting an advanced form of research methodology. The present review aims to explore the principles of Ayurvedic research methodology with special reference to Vadamarga, and to correlate them with modern research practices. Concepts such as Pramana, Tadvidya Sambhasha, and Panchavayavi Vakya are critically analysed to understand their relevance in contemporary research. The study highlights that Ayurvedic methodologies emphasize rational thinking, observation, inference, and structured discussion, which align closely with modern scientific approaches. Integrating these classical principles into present-day research may enhance the authenticity, depth, and holistic understanding of scientific inquiry.

The travel and tourism industry is one of the fastest growing sectors in the world It contributes large part of global economy and provides millions of jobs inspite of this growth, many travel agencies still depend on traditional methods. Traditional systems require customers to physically visit agencies, compare packages through brochures, and conduct cash transactions, time comparing packages. The system provides distinct interfaces for customers and administrators, enabling efficient package management, real-time booking, and secure financial transactions. The platform incorporates smart filtering, payment gateway integration, and automated invoice generation. Evaluation results demonstrate that TripGo reduces booking time by approximately 75%, eliminates manual data entry errors, and provides a transparent and secure environment for both travelers and travel agencies.

Employee well-being and mental health have emerged as central concerns in modern organizational management due to increasing workplace complexities, technological disruptions, and evolving employee expectations. This research study examines the multidimensional concept of employee well-being and its direct and indirect impact on organizational productivity, engagement, and sustainability. The study identifies key determinants of mental health such as workload, organizational culture, leadership style, and work-life balance. It also evaluates corporate initiatives undertaken by leading organizations to enhance employee well-being.
Through a qualitative and descriptive research methodology based on secondary data, the study highlights that poor mental health results in decreased productivity, absenteeism, burnout, and high employee turnover. Research findings indicate that workplace mental health interventions significantly reduce stress and improve work effectiveness. The study further includes a comparative analysis of companies such as Google, Microsoft, and Tata Consultancy Services (TCS) to understand practical implementation.
The research concludes that employee well-being is not only a social responsibility but also a strategic investment that enhances organizational performance.

Khadi is one of the most important traditional products in India and represents the country’s cultural heritage, self-reliance, and sustainability. The present study focuses on customer preference towards Khadi products in Tirupur, a city well known for its textile industry. The objective of this study is to understand the level of awareness, buying behavior, and preference of customers towards Khadi products such as clothing, skincare items, and household products. It also examines the factors that influence customers to choose Khadi products, including quality, price, eco-friendliness, and cultural value.

Diphenhydramine is a first-generation antihistamine widely utilized for managing allergic reactions, common cold symptoms, motion sickness, and short-term sleep disturbances. It acts by blocking histamine H1 receptors, thereby reducing symptoms such as itching, sneezing, and swelling.
This study explores the availability, demand, and usage trends of diphenhydramine in retail pharmacies and hospital environments. The findings suggest that despite the emergence of newer antihistamines with fewer sedative effects, diphenhydramine continues to maintain a strong presence due to its cost-effectiveness, accessibility, and multiple therapeutic benefits. However, its sedative nature and associated side effects influence its preference among users.

Maize is one of the most important crops in India, but fungal diseases like Northern Leaf Keywords: Maize Disease Detection, CNN, MERN Stack, Agriculture 5.0, Plant Pathology, Deep Learning, Treatment Recommendation. Blight, Common Rust, and Gray Leaf Spot causemajor yield losses every year. The problem is that most farmers do not have access to plant pathologists. They end up guessing which pesticide to use, which wastes money and harms the environment. In this project, we built a web-based application called "Agri Doctor" that helps farmers detect maize diseases using their smartphones. The system uses a Convolutional Neural Network (CNN) trained on leaf images to identify diseases with reasonable accuracy. Once a disease is detected, the system immediately suggests a treatment plan – including chemical, organic, and cultural methods – from a database.
The frontend is built with React, the backend uses Node.js and Express, and the database is MongoDB. The AI model runs as a separate Python service using FastAPI. We tested the system with real field images collected from farms in and around Ghaziabad. The model achieved about 96% accuracy on field images, which is good enough for practical use.
This paper describes what we built, how we built it, what problems we faced, and what we learned. The goal is not to claim perfection but to show that a practical, low-cost, AI-powered tool can actually help farmers make better decisions.

In recent years, growing concerns regarding children's physical health and increasing sedentary lifestyles have encouraged parents and educators to focus more heavily on sports and physical activities. With the rapid growth of technology and screen-based entertainment, sports activities like basketball have become an important way to promote physical fitness and healthy habits among children. This study empirically investigates the behavioural and environmental factors influencing children's participation in basketball. The research focuses on identifying the impact of parental encouragement, children’s personal interest, awareness of fitness benefits, and the structural availability of sports facilities. By understanding these dynamics, the research provides actionable insights that may help educators, parents, and community planners improve sports participation and combat sedentary habits in youth.

Hypertension remains one of the most prevalent non-communicable diseases worldwide and a major contributor to cardiovascular morbidity and mortality. Among the available therapeutic options, Amlodipine, a long-acting dihydropyridine calcium channel blocker, has emerged as a cornerstone in antihypertensive therapy due to its efficacy, safety, and patient-friendly dosing regimen.
This study aims to critically analyze the market dynamics, utilization patterns, and prescribing behavior associated with Amlodipine in both retail pharmacy and hospital settings. The study is based on a comprehensive review of secondary data sources including pharmacological literature, research publications, and pharmaceutical market reports, supplemented with observational insights.
The analysis indicates that Amlodipine holds a dominant position in the cardiovascular drug segment due to its affordability, widespread availability, and strong clinical performance. Retail pharmacies contribute significantly to overall sales due to chronic therapy requirements, while hospitals play a pivotal role in therapy initiation and long-term prescription trends.
The study concludes that the dual-channel distribution system of retail and hospital sectors strengthens the market sustainability of Amlodipine. Future demand is expected to increase in parallel with the rising global burden of hypertension and lifestyle-related disorders.

Business valuation is the process of estimating the economic value of a business or an owner’s interest in a company using a systematic set of procedures and financial techniques. It aims to determine the theoretical fair value of a firm, particularly in situations where assets or earnings may be overvalued or undervalued. Business valuation is widely used to determine the price that buyers and sellers are willing to pay or receive during transactions involving business sales, mergers, or restructuring.
Business valuation plays a crucial role in financial management by providing an objective and analytical assessment of a firm’s worth. It assists managers, investors, and other stakeholders in making informed decisions related to investment planning, capital structuring, mergers and acquisitions, performance evaluation, risk management, and long-term strategic planning. Valuation is also essential for purposes such as mergers and acquisitions, share transfer and insurance, insolvency and bankruptcy proceedings, income tax assessments, financial reporting, and strategic decision-making. In addition to estimating the selling price of a business, valuation techniques are frequently used by professional appraisers to resolve disputes related to estate and gift taxation, divorce litigation, and allocation of purchase price among business assets.
This study aims to analyze the concept, importance, and application of business valuation techniques with a focus on the three commonly used approaches: the Income Approach, the Market Approach, and the Cost (Asset-based) Approach. The Income Approach evaluates value based on the present value of expected future benefits, the Market Approach compares the firm with similar companies operating in the market, and the Cost Approach determines value based on the fair value of net assets. Special emphasis is placed on the Discounted Cash Flow (DCF) method, which estimates business value by discounting projected future cash flows using an appropriate discount rate that reflects the firm’s risk profile.

The return of Ghanaian professionals from the diaspora has increased substantially over the past decade, yet the workplace integration of these returnees and their perceived work ethic by Ghana-based colleagues remains largely unexamined. This qualitative exploratory study investigates how returnee professionals reflect on their own work ethic and how they are perceived by their Ghanaian-born colleagues in Accra's corporate and professional services sector. Using an exploratory qualitative design grounded in Social Identity Theory and Acculturation Theory, the study recruited 24 participants through purposive and snowball sampling: 12 returnee professionals who had worked abroad for at least three years before returning to Ghana, and 12 Ghanaian-born colleagues who work alongside returnees. Participants completed in-depth semi-structured interviews exploring perceptions of work ethic, workplace behaviours, cultural differences, and team dynamics. Data were analysed using thematic analysis, yielding seven superordinate themes: (1) The Pace Paradox: Speed Versus Relationship; (2) The Question of Respect: Hierarchical Expectations; (3) Work-Life Boundaries: Returnee Rigidity Versus Local Fluidity; (4) The Competence Presumption: Returnees as Outsiders Within; (5) The Reverse Culture Shock of Workplace Norms; (6) Communication Styles: Directness as Disrespect; and (7) The Middle Ground: Successful Integration Strategies. Findings reveal that returnees perceive themselves as efficient, task-oriented, and boundary-conscious, while colleagues perceive them as arrogant, impatient, and culturally dislocated. Conversely, colleagues perceive themselves as relationship-oriented, respectful of hierarchy, and flexible, while returnees perceive them as inefficient, deferential to a fault, and lacking boundaries. Successful integration was associated with cultural humility, deliberate adaptation, and mentorship from experienced local colleagues. These findings inform expatriate reintegration programmes, human resource policies, and diversity and inclusion initiatives for Ghanaian organisations employing returnee professionals.