VOLUME 14, ISSUE 1, JANUARY 2026

Abstract: Lithium-ion batteries find wide applications in electric vehicles, energy storage systems, and portable electronic devices due to their high energy density and long cycle life. In a series-connected battery pack, divergence in individual cell characteristics can lead to SOC imbalance, which severely degrades performance, safety, and cycle life. Thus, cell balancing emerges as one of the essential tasks of the BMS. This paper deals with the modeling and simulation of an efficient passive cell balancing method using MATLAB Simulink. The proposed scheme is applied on a ten-cell series-connected lithium-ion battery pack, and balancing is triggered by a threshold-based switching action. The simulation results show that the proposed method causes significant reduction in SOC imbalance and attains equilibration within some finite balancing time. Because of the simplicity and low cost of the method, it is suitable for low-to-medium-power applications in batteries..

Keywords: Lithium-ion Battery, Passive Balancing, Battery Management System, MATLAB Simulink, Cell Equalization, State of Charge (SoC)

Abstract: This paper presents the development of an IoT-based Battery Management System for real-time monitoring of battery parameters. The proposed system continuously measures battery temperature and voltage using appropriate sensors interfaced with an Arduino Uno microcontroller. The sensed data is uploaded to the Thing Speak cloud platform for real-time visualization and remote monitoring. A threshold-based buzzer alert mechanism is implemented to provide immediate warning during abnormal temperature rise, ensuring battery safety. The temperature sensor used in the system provides an accuracy of ±0.5 °C. The cloud update interval is set to 15 seconds, with an average communication latency of approximately 2–3 seconds. Experimental results demonstrate reliable data acquisition, stable cloud communication, and effective alert generation, making the system suitable for low-cost battery monitoring applications.

Keywords: IoT, battery management, electric vehicle, monitoring, sensors, cloud, analytics, efficiency, sustainability, performance.

Abstract: This project presents a Zigbee-based Nearby Vehicle Accident Alert System designed to enhance road safety by providing real-time accident notifications to nearby vehicles and emergency services. The system employs Zigbee technology, a low-power, cost-effective wireless communication protocol, to establish a network between vehicles and roadside units.

When an accident is detected using onboard sensors like tilt sensor, the system triggers an automatic alert, transmitting critical information such as the location, time, and severity of the incident to nearby vehicles and emergency responds within the Zigbee network range. This immediate communication helps quicker emergency response, and improves the overall efficiency of post-accident management. The system's low power consumption and reliable communication capabilities make safer road environments.

Abstract: This project presents the design and implementation of a solar–wind hybrid power generation system on highways aimed at utilizing renewable energy sources efficiently. Solar panels capture energy from sunlight, while wind turbines generate power from airflow created by moving vehicles and natural wind. The combination of solar and wind energy ensures reliable and continuous power generation throughout the day and night.

The generated electrical energy can be used for highway lighting, traffic signals, toll booths, and nearby public utilities, thereby reducing dependency on conventional fossil-fuel-based power sources. This system helps in lowering carbon emissions, minimizing energy losses, and promoting sustainable infrastructure development. The proposed hybrid power generation model is cost-effective, eco-friendly, and contributes to the effective utilization of unused highway spaces, supporting the goal of clean and renewable energy generation.

Keywords: Solar Energy, Wind Energy, Hybrid Power Generation, Highway Energy System, Renewable Energy, Battery Storage, Inverter, Sustainable Infrastructure

Abstract: Wander Wish is an intelligent travel planning and destination discovery platform designed to simplify and personalize the travel experience for users. The project aims to bridge the gap between travelers and the overwhelming amount of travel information available across multiple online sources by providing a unified, user-centric solution. Wander Wish leverages modern web technologies and intelligent data processing to assist users in discovering destinations, planning itineraries, searching accommodations, and making informed travel decisions based on preferences, budget, and time constraints.The system integrates real-time data from travel service providers such as hotel booking platforms, location databases, and travel information sources to deliver accurate and up-to-date results. By utilizing intelligent recommendation logic and Large Language Model (LLM) capabilities, Wander Wish analyzes user inputs such as travel dates, interests, group size, and budget to generate personalized travel suggestions, optimized itineraries, and accommodation recommendations. This approach reduces manual effort and enhances decision-making efficiency for travelers.Wander Wish follows a modular client-server architecture, ensuring scalability, maintainability, and seamless integration with external APIs. The backend handles data aggregation, search logic, and AI-driven recommendations, while the frontend offers an intuitive and responsive user interface for effortless interaction. Security and performance considerations are incorporated to ensure safe handling of user data and fast response times.Overall, the Wander Wish project demonstrates how intelligent automation and AI-assisted decision support can transform traditional travel planning into a smarter, more personalized experience. The platform not only saves time and effort for users but also enhances travel satisfaction by aligning recommendations with individual preferences, making Wander Wish a practical and innovative solution in the modern travel technology ecosystem.

Abstract: Medication non-adherence and unauthorized access to prescription drugs continue to be two of the critical challenges that plague modern healthcare, often resulting in inefficient treatment, adverse drug events, and avoidable hospitalizations. This work proposes an IoT-enabled Smart Pill Bottle for controlled and timely medication intake in a monitored environment. This system includes embedded sensors, an ESP32-based control unit, and cloud connectivity, enabling real-time authentication of medication access and dose consumption verification. Sensor-based event detection, combined with automated reminders and notifications to caregivers, will permit continuous adherence monitoring and immediate response to missed or delayed doses. Tamper-detection mechanisms further recognize unauthorized access attempts and secure safety for controlled and high-risk medications. Cloud-synchronized logs and adherence analytics will present valid, timestamped data to caregivers and healthcare professionals to facilitate proactive intervention and evidence-based clinical decisions. The proposed solution provides significant improvement in medication safety while improving accountability and enhancing remote healthcare monitoring for elderly and chronically ill patients.

Keywords: IoT Healthcare, Medication Adherence, Cloud-Based Monitoring, Medicine Dispenser.

Abstract: The rapid growth of embedded systems and automation has increased the demand for intelligent monitoring and control solutions in low-power electrical applications. This project presents the design and implementation of an Arduino Uno–based monitoring and control system for a copper winding charging station. The system integrates voltage and current sensors to continuously monitor electrical parameters, ensuring safe and efficient operation. An IR module is employed for object or position detection, while a relay and servomotor provide controlled switching and mechanical actuation, respectively. Wireless communication capability is enabled through the ESP8266 module, allowing Internet of Things (IoT)–based data transmission and remote monitoring. A 16×2 LCD is used to display real-time system parameters such as voltage, current, and operational status. The Arduino Uno serves as the central controller, processing sensor data and executing control logic programmed using the Arduino IDE. The proposed system offers a low-cost, reliable, and scalable solution for automated charging station applications, with potential extension to smart energy management and industrial automation systems.

Keywords: Arduino UNO, LCD Display, Voltage Sensor, Current Sensor, IR Sensor, ESP8266, Servomotor, Copper Coil, Connecting Wires, Arduino IDE – C Programming

Abstract: Gesture recognition provides a natural and intuitive method for human–machine interaction. This paper presents the design and experimental implementation of a low‑cost hand gesture controlled smart robot using an Arduino microcontroller, MEMS accelerometer sensor, RF communication, Bluetooth interface, and ultrasonic obstacle detection. The system allows a mobile robot to be controlled by hand movements, Android application touch commands, and voice recognition. The accelerometer mounted on the user’s hand senses tilt directions, which are transmitted wirelessly to the robot for motion control. An additional obstacle detection mechanism improves operational safety. The proposed system is developed as a laboratory‑scale prototype and demonstrates reliable performance with affordable hardware. The results indicate that gesture‑based control combined with wireless communication offers an efficient alternative to conventional remote‑controlled robotic systems for automation and assistive applications.

Keywords: Gesture recognition, Arduino, MEMS accelerometer, RF communication, Bluetooth, mobile robot, obstacle detection.

Abstract: Water management has become a critical concern due to rapid urbanization and increasing water demand. Conventional water tank systems rely on manual motor operation, which often results in water overflow, wastage of electricity, and increased human effort. This paper presents the design and implementation of an IoT based Smart Water Tank Control and Monitoring System that automatically controls the water pump and inlet valve based on real-time water level data. The system employs an Arduino ATmega328 microcontroller for local control and a NodeMCU ESP8266 module for cloud connectivity. Water level sensing is achieved using level sensors, and the system status is transmitted to a Firebase cloud platform, enabling remote monitoring and control through a mobile application. Manual override functionality is also incorporated to ensure reliability during network or system failures. The proposed system improves water conservation, reduces power consumption, and offers a cost-effective solution suitable for domestic, institutional, and agricultural applications.

Keywords: IoT, Smart Water Tank, Water Level Monitoring, NodeMCU, Arduino, Firebase

Abstract: The rapid development of virtual and augmented reality technologies has led to the formation of a new digital ecosystem known as the Metaverse. It is an interconnected network of three-dimensional, immersive and interactive environments, in which users act through avatars, communicate, learn and create. The metaverse, as a technological and social evolution of the internet, opens up unprecedented possibilities for education, as it allows the development of experiential, collaborative and personalized learning experiences. The present study explores the cognitive, affective, metacognitive and technical dimensions of learning in virtual environments of the metaverse, with a theoretical background of the Cognitive Theory of Multimedia Learning (CTML), Cognitive-Affective Theory of Learning with Media (CATLM) and Cognitive Load Theory (CLT). A questionnaire was created to evaluate the learning experience and, through simulated data of 200 students, statistical techniques (descriptive analysis, reliability, PLS-SEM) were applied to verify the theoretical model. The results showed that emotional engagement (CATLM) and cognitive dimension (CTML) are the strongest predictors of perceived effectiveness, while metacognition and usability function as reinforcers. The model presented high reliability (α =.91), explaining 71% of the variation in effectiveness (R² = 0.71). The study concludes with practical directions for designing educational virtual worlds in the metaverse and lays the foundations for future empirical research in real conditions.

Keywords: Metaverse, Virtual Worlds, Cognitive Load Theory, Cognitive-Affective Theory

Abstract: As high-speed data streams from financial transaction systems, cloud infrastructures, Internet of Things devices, and huge communication networks undergo rapid change, real-time analytics are becoming increasingly important. The incapacity of traditional batch-oriented and rule-based anomaly detection systems to handle a range of data distributions, low latency requirements, and a constant stream of new data is rendering them obsolete. This study provides a thorough AI-driven analytics pipeline aimed at discovering anomalies in real time in quickly flowing data streams in order to address these problems.

Real-time data reprocessing, adaptive feature engineering, complex artificial intelligence models, and techniques for handling growing streams are all included in the proposed pipeline, which is a complete system. Deep learning techniques like autoencoder- based models and recurrent neural networks, along with statistical detectors, can effectively identify changes in streaming data across both brief and extended durations. This project's main goal is to improve online learning by teaching individuals how to combat notion drift. These enable the pipeline to quickly adjust to new data patterns without affecting the day-to-day operations of the company.

The architecture relies on contemporary distributed stream processing platforms and containerized deployment techniques to guarantee scalability, fault tolerance, and speed of operation. The suggested methodology beats current anomaly detection systems in terms of detection accuracy, robustness, and sub-second reaction times, according to thorough experimental evaluation on both synthetic and real-world streaming datasets. Overall, our work gives a valuable and adaptable foundation for using AI-driven anomaly detection approaches in real-time data stream applications that are vital to the mission.

Keywords: AI-driven pipelines, stream processing, online learning, idea drift, deep learning, autoencoders, distributed systems, real- time analytics, fast data streams, and abnormality detection are some of the most important terms.

Abstract: Despite the importance of speed in sports performance, limited studies have compared speed abilities between football players and non-football players using standardized field tests such as the 50 Yard Dash Run. Hence, the present study was undertaken to compare the speed performance of football players and non-football players The purpose of the present study was to compare the speed performance of football players and non-football players and to examine the effect of resistance training on speed among physical education students. A total of 40 football players and non-football players were selected as subjects and randomly divided into two equal group. The results revealed a statistically significant difference in speed performance between football players and non-football players, with football players demonstrating superior speed.