The global energy landscape is currently undergoing a transformative shift necessitated by the rapid depletion of non-renewable repositories such as coal, oil, and natural gas. Driven by the escalating demands of industrialisation and exponential population growth, particularly in developing nations, the energy gap between generation and demand continues to widen. Global efforts have subsequently pivoted toward renewable energy sources (RESs) to mitigate the environmental deterioration and carbon dioxide emissions associated with global warming, with many nations pledging to reach Net Zero by 2050. Among these technologies, solar photovoltaic (PV) systems are increasingly prioritised due to their abundance, noise-free operation, minimal maintenance requirements, and their adaptability for installation in confined urban spaces like rooftops. Power generation from RESs not only preserves billions of barrels of crude oil but is essential for establishing sustainable energy security. Despite the proliferation of PV technology, its operational efficiency remains inherently limited by its non-linear output characteristics and a heavy dependence on varying environmental conditions, specifically solar irradiance and cell temperature. While the peak solar density received at the earth's surface is approximately 1000 W/m², the actual power extracted is often much lower due to the PV cell's sensitivity to atmospheric fluctuations. A significant technical hurdle is the occurrence of Partial Shading Conditions (PSCs), which arise from moving clouds, shadows from adjacent buildings or trees, dust accumulation, and even debris such as bird droppings. These factors distract the rate of incident photons, which directly impedes the liberation of electrons within the n-type material and the subsequent flow to the load. Under PSCs, the modules within an array receive non-uniform irradiation levels, causing a mismatch in current generation. Shaded cells produce significantly less photon current than their unshaded counterparts, leading them to become reverse-biased and act as electrical loads rather than generators. This phenomenon results in substantial mismatching power losses (ML) and creates a high risk of hot-spot heating, which can cause irreversible physical damage to the module structure. To protect the array, bypass diodes are typically integrated; however, their activation introduces further complexity by transforming the standard single-peak Power-Voltage (P-V) curve into a non-linear landscape. This multi-modal curve contains several Local Maximum Power Points (LMPPs) and only one absolute Global Maximum Power Point (GMPP). Research indicates that as the number of modules receiving different insolations increases, the number of stairs in the I-V curve and the count of local optima in the P-V curve rise proportionally. The evolution of research into mitigating these losses has transitioned through several distinct technical paradigms. Initially, efforts focused on classical Maximum Power Point Tracking (MPPT) logic, such as Perturb and Observe (P&O) and Incremental Conductance (IC). While these methods are computationally simple, they are inherently flawed under PSCs as they tend to oscillate around the nearest power peak and frequently become trapped at LMPPs, leading to wasted energy. Consequently, the focus shifted toward hardware-level interventions, exploring various interconnection topologies like Series-Parallel (S-P), Total Cross-Tied (TCT), Bridge-Link (BL), and Honeycomb (HC). Comparative analyses have established that the TCT configuration generally provides the most resilient output and highest maximum power extraction across diverse shading patterns. More recently, the research domain has advanced toward computational intelligence and bio-inspired metaheuristics to navigate non-convex search spaces. Algorithms mimicking the natural behaviours of salp swarms (SSA), bats (BA), and fireflies (FA) have demonstrated superior tracking accuracy and faster convergence speeds than conventional methods. For example, the Salp Swarm Algorithm utilizes a direct GBest technique to move particles straight toward potential targets, skipping unnecessary search regions and identifying the GMPP in as little as 0.22 seconds. Similarly, the Bat Algorithm employs echolocation logic to provide reliable tracking without steady-state oscillations. Parallel to these software advancements, puzzle-based reconfigurations using Sudoku, Latin Square, and Futoshiki logic have emerged as a means to physically re-allocate modules for optimal shade dispersion. These methods, such as the Latin Square (LS-TCT) approach, aim to equalise row currents and have been validated to reduce mismatch losses by up to 1787 W in specific scenarios. This review paper provides a comprehensive and systematic synthesis of these contemporary hardware and software strategies. By critically evaluating the effectiveness of array interconnection schemes, dynamic reconfiguration algorithms, and hybrid metaheuristic trackers, this work contributes a clear benchmarking framework for enhancing energy conversion rates. This investigation is vital for selecting optimal PV schemes that can extract maximum power under dynamic operating conditions, ultimately bridging the gap between theoretical generation and practical demand in the pursuit of global energy sustainability.

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In recent years, maintaining a healthy lifestyle has become increasingly challenging due to sedentary habits, improper diet, and lack of personalized fitness guidance. Many individuals struggle to identify suitable diet plans and workout routines based on their body requirements, fitness goals, and daily schedules. Traditional fitness programs often provide generic recommendations that may not suit individual needs. This survey paper presents an overview of web-based diet and workout generator systems that provide personalized fitness and nutrition recommendations using user-specific parameters such as age, gender, weight, height, activity level, and fitness goals. The paper reviews existing approaches, technologies, and algorithms used in diet and workout recommendation systems, analyzes their limitations, and highlights the novelty of a web-based personalized diet and workout generator. The proposed approach aims to deliver customized, accessible, and user-friendly fitness guidance through a web application.

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Childhood vaccination is a key preventive measure to protect against life-threatening diseases and to reduce child mortality. In many regions, especially rural areas, parents are traditionally informed of vaccination schedules by community health workers. However, this manual approach is often inefficient, leading to missed or delayed vaccinations. The core problem lies in the absence of a centralized, automated system for managing immunization records, sending timely reminders, and coordinating appointments. To address this, a mobile-based vaccination management system is proposed. It allows parents to register multiple children, automatically generates personalized vaccination schedules based on each child's date of birth, and sends reminders via notifications and SMS. The system supports online slot booking, doctor verification, and real-time appointment tracking. It also offers transport assistance by integrating contact options for local drivers, ensuring easier access to vaccination centers.

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The increasing penetration of renewable energy resources has significantly improved the sustainability of modern power systems but has also introduced operational uncertainty and instability. Traditional control mechanisms often fail to respond effectively to rapid fluctuations in solar and wind generation. This paper proposes a deep learning–based predictive control framework that integrates Long Short-Term Memory (LSTM) forecasting with Model Predictive Control (MPC) to enhance the real-time performance of renewable-integrated smart grids. LSTM networks are utilized to forecast short-term renewable output and load profiles, while the MPC layer optimally adjusts inverter setpoints, voltage regulators, and demand response signals. Simulation results demonstrate that the proposed approach reduces voltage deviation by up to 56%, improves renewable utilization by 18%, and decreases operational cost by 22% compared to conventional control strategies. These outcomes validate the capability of deep learning models to support intelligent, adaptive, and real-time grid management.

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This project is based on an EV Battery Management System with Fire Protection using GSM. The main aim of this project is to provide safety to the battery used in electric vehicles. The system continuously checks important battery values like voltage and temperature using sensors. If the battery gets overheated or overcharged, the system automatically cuts off the battery supply and turns ON the fire protection unit. At the same time, a GSM module sends an alert message to the user's mobile phone about the problem. This helps in taking quick action and prevents accidents. This project helps to protect the battery from damage, increases battery life, and reduces the chances of fire. It is very useful for electric vehicles and other battery-operated devices.

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Thermodynamic equilibrium models (TEM) are widely used for predicting producer gas composition in biomass downdraft gasifiers due to their simplicity and computational efficiency. However, conventional TEM often overpredicts hydrogen and carbon monoxide because of the assumption of complete chemical equilibrium. In this work, a Comprehensive Thermodynamic Equilibrium Model (CTEM) incorporating modified equilibrium constants (Kwgs = 1.01 and Km = 0.65) is validated for rubber-wood biomass under varying equivalence ratio (0.274–0.420) and moisture content (0.076–0.185). Mass balance, equilibrium constant formulation, and energy balance equations are solved simultaneously using an iterative approach. Model predictions are evaluated using Mean Percentage Error (MPE) and Root Mean Square Error (RMSE). Results indicate significant reduction in hydrogen prediction error and improved nitrogen estimation compared to conventional TEM. The modified model demonstrates improved robustness across operating ranges and can be used for preparing generalized gasification performance tables.

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The QR Code Based Smart Online Student Attendance System is the application developed for educational institutes to simplify and automate student attendance taking process, so that most of the time in a class is devoted to teaching and learning. The system has been designed using Java (JSP), HTML, CSS and JavaScript front-end along with MySQL for database management. The system provides secure real-time attendance recording without the possibility of unauthorized access or proxy attendance. The two main components of the system are the staff and the students. In order to prevent unwanted access, staff members verify the comprehensive personal and academic information that students provide when they register. Students who have been approved are given a special ID card with a QR code on it, which they scan to record their attendance. To ensure verifiability, a photo of the student is taken with every attendance entry. Staff members can use the system to create thorough attendance reports for each student and the entire class, complete with verification photos. In order to keep track of their attendance status, students can monitor their attendance logs. This system is an affordable, scalable, and dependable solution for contemporary educational institutions because it integrates web technologies, database management, and secure QR code authentication.

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This paper presents the design and implementation of a Smart Car Parking System using biometric authentication and Internet of Things (IoT) technology. The proposed system aims to improve parking security, efficiency, and automation by reducing manual supervision. An ESP32 microcontroller is used as the central controller, integrated with a fingerprint sensor for user authentication, a camera module for vehicle image capture, and a real-time clock for accurate timestamping. The system automatically controls vehicle entry and exit using a servo-based gate mechanism. Parking data is logged locally or transmitted to cloud platforms, and notification messages are sent to users and administrators. The proposed solution provides a secure, scalable, and reliable parking management system suitable for smart cities, residential complexes, and commercial facilities.

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This paper presents a Smart Electrical Pole Monitoring System designed to detect the presence of electrical current in street light poles and prevent accidental electric shocks. In many public areas, leakage current in damaged or faulty poles can cause serious injuries or fatalities. The proposed system continuously monitors the current flow in the pole using appropriate sensors. If abnormal current or leakage is detected, the system immediately activates a visual or audible alert to warn nearby people. The system is cost-effective, easy to install, and suitable for urban as well as rural areas. This solution enhances public safety by providing real-time monitoring and early warning. The proposed model can be further integrated with IoT technology for remote monitoring and improved maintenance management.

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