In light of the fast-paced growth of the transportation and logistics sector, monitoring of fleets is now crucial to any business operating more than one vehicle at once. The main problem in traditional monitoring systems is that they cannot offer a holistic view on vehicle tracking and fuel consumption. This paper will address the development of the ECV Tracker system, which incorporates vehicle tracking and monitoring of fuel consumption simultaneously. GPS sensors and fuel sensors will provide real-time data on the current status of each monitored vehicle. Data gathered will be accessible to users via web or smartphone platforms to ensure that abnormal operations are detected instantly. The ECV Tracker will help streamline processes and reduce operational expenses.

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Solar photovoltaic (PV) systems have emerged as one of the most promising renewable energy sources due to their clean and sustainable nature. However, the output power of a PV system is highly dependent on environmental conditions such as solar irradiance and temperature. To ensure maximum energy extraction under varying operating conditions, an efficient Maximum Power Point Tracking (MPPT) technique is required. Conventional MPPT methods, including Perturb and Observe (P&O) and Incremental Conductance (INC), are widely used in PV applications. Although the P&O method offers simple implementation and fast response, it suffers from steady-state oscillations around the Maximum Power Point (MPP). On the other hand, the Incremental Conductance technique provides better accuracy but requires higher computational effort and may exhibit slower response under certain conditions. This paper proposes an Adaptive Hybrid MPPT Control Algorithm that combines the advantages of both P&O and Incremental Conductance methods. The proposed controller utilizes the fast tracking capability of the P&O algorithm during transient conditions and switches to the Incremental Conductance technique near the MPP to improve tracking accuracy and minimize power oscillations. The performance of the proposed system is evaluated using MATLAB/Simulink under different irradiance conditions. Simulation results demonstrate that the hybrid MPPT controller achieves faster convergence, improved stability, reduced oscillations, and higher tracking efficiency compared to conventional MPPT techniques. The proposed approach attains a tracking efficiency of approximately 98.8%, making it an effective solution for enhancing the performance and energy yield of solar PV systems operating under dynamic environmental conditions.

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Diabetes mellitus is a chronic metabolic disorder that affects a large and growing population worldwide. Early detection and continuous monitoring are essential to prevent severe complications associated with the disease. Traditional diagnostic and monitoring methods are often periodic and lack continuous patient engagement. Recent advancements in machine learning and artificial intelligence provide effective solutions for predictive analysis and personalized healthcare support. This project presents a Diabetes Prediction System using Machine Learning techniques implemented in Python, integrated with a web-based frontend dashboard and an intelligent chatbot. The system employs supervised machine learning algorithms to analyse patient health parameters such as glucose level, insulin level, body mass index (BMI), age, and other relevant attributes to predict the risk of diabetes. The frontend interface enables users to log and visualize blood glucose and insulin levels through interactive charts. An AI-powered chatbot assists patients by providing personalized diet recommendations, reminders for glucose and insulin monitoring, and alerts during abnormal readings. The proposed system aims to improve early diagnosis, enhance self-management, and reduce diabetes-related complications. This integrated solution demonstrates the potential of intelligent healthcare systems in preventive medicine and patient-centric care.

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The advancement of smart healthcare technologies has significantly improved healthcare accessibility, patient monitoring, and emergency response systems. This paper presents a Smart Community Health Monitoring & Early Warning System designed to provide real-time health monitoring, early disease detection, and healthcare management through digital technologies. The proposed system integrates healthcare monitoring modules, patient management systems, alert generation mechanisms, and centralized healthcare databases to enhance healthcare services within communities. The system aims to reduce healthcare response time, improve patient monitoring efficiency, and support healthcare professionals with real-time patient information. The proposed platform also enables early warning notifications during abnormal health conditions, allowing timely medical intervention. Experimental analysis demonstrates that the system improves healthcare management, enhances monitoring accuracy, and provides reliable performance for smart healthcare applications.

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Heavy commercial vehicle fleets are critical to transportation, logistics, mining, and construction sectors, where vehicle reliability and maintenance efficiency significantly influence operational performance and profitability. Frequent vehicle failures, increasing maintenance expenditure, and downtime losses have created a growing need for data-driven maintenance strategies. This review article presents a comprehensive analysis of vehicle failure mechanisms, maintenance cost modeling, reliability assessment techniques, and emerging technologies used in fleet management. Reliability engineering approaches such as Weibull analysis, Markov models, Reliability Block Diagrams (RBD), and Remaining Useful Life (RUL) prediction are examined along with modern data analytics and machine learning applications. The study further reviews preventive, condition-based, predictive, and reliability-centered maintenance strategies for enhancing fleet availability and reducing operational costs. The role of Industry 4.0 technologies, including IoT, Digital Twins, Cloud Computing, and Artificial Intelligence, is also discussed. Research gaps, future trends, and opportunities for intelligent fleet maintenance systems are identified. The review provides valuable insights for researchers and practitioners seeking to improve fleet reliability, maintenance effectiveness, and long-term operational sustainability.

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This study focuses on the finite element and fatigue life evaluation of heavy vehicle leaf springs using advanced composite materials. Conventional steel leaf springs provide good load-carrying capacity but increase vehicle weight and are prone to fatigue failure under cyclic loading. In this work, steel, GFRP, CFRP, Kevlar composite, and hybrid composite materials were compared for structural and fatigue performance. A 3D leaf spring model was developed and analyzed under identical loading and boundary conditions. Total deformation, equivalent stress, equivalent strain, safety factor, fatigue life, and damage factor were evaluated. The results showed that composite materials provide significant weight reduction and improved fatigue resistance compared with steel. CFRP showed the highest fatigue life and safety factor, while hybrid composite offered balanced performance for practical applications. The study concludes that advanced composite leaf springs can improve durability, fuel efficiency, and suspension reliability in heavy vehicles.

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Vision-guided robotic systems have emerged as a key technology in Industry 4.0 by enabling robots to perceive and interact with dynamic environments. Conventional robotic manipulators typically rely on fixed coordinate programming, limiting their flexibility when object positions change. This paper presents the design and development of a low-cost vision-based robotic arm prototype capable of autonomous object detection and pick-and-place operations. The system integrates a 6-degree-of-freedom (6-DOF) robotic arm fabricated using Fused Deposition Modelling (FDM) 3D printing technology with computer vision techniques based on OpenCV and YOLOv8 object detection. A top-mounted camera captures real-time images of the workspace, and detected object positions are converted from image coordinates to real-world coordinates using homography-based calibration. Inverse kinematics algorithms are employed to compute robotic arm joint movements, while an Arduino UNO microcontroller controls servo actuation through a PCA9685 servo driver. Experimental evaluation demonstrated approximately 92% detection accuracy, coordinate mapping accuracy within ±0.5 cm, and successful autonomous pick-and-place operations under controlled laboratory conditions. The proposed system provides a cost-effective platform for intelligent automation, robotics education, and industrial material handling research.

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The present study titled aims to examine the Socio-Economic profile of tribal millet farmers, identify the major marketing constraints faced by them, and analyze the relationship between market access factors and income improvement. The study is based on a descriptive research design using both primary and secondary data. Primary data were collected from 85 tribal millet farmers in Attappady through a structured questionnaire. The collected data were analyzed using statistical tools such as frequency and percentage analysis, descriptive statistics, factor analysis, and correlation analysis with the help of SPSS software. The findings of the study reveal that most farmers belong to small and marginal categories with limited resources and depend on traditional marketing channels. Factor analysis identified three major dimensions of marketing constraints, namely Information Constraints, Infrastructure Constraints, and Market and Financial Constraints, with information-related issues emerging as the most significant factor. The correlation analysis indicates a positive and significant relationship between market access factors and farmer income improvement, highlighting the importance of effective market connectivity. The study concludes that strengthening market information systems, improving infrastructure facilities, and enhancing institutional support can significantly improve the marketing efficiency and income levels of tribal millet farmers in the study area.

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Friction Stir Processing (FSP) is a promising solid-state technique for the fabrication of surface composites with improved mechanical and tribological properties. In the present work, graphene-reinforced AA6061-T6 surface composites were fabricated using the groove filling method followed by FSP. The effects of tool rotational speed (1000 and 1200 rpm) and graphene volume fraction (15, 20, and 25 vol.%) on the microstructure and wear behavior of the composites were investigated. The distribution of graphene was found to be strongly dependent on the processing parameters. Improved material flow and more uniform reinforcement dispersion were achieved at the higher rotational speed of 1200 rpm, whereas higher graphene content resulted in localized agglomeration and defect formation in some specimens. The wear behavior showed significant dependence on both rotational speed and graphene content. Among the samples processed at 1000 rpm, the specimen containing 20 vol.% graphene exhibited lower wear compared to the other conditions. For the specimens processed at 1200 rpm, the composite reinforced with 25 vol.% graphene demonstrated the lowest wear depth and the best wear resistance. The enhanced tribological performance was attributed to the combined effects of grain refinement, improved graphene dispersion, and the formation of a protective graphene-rich tribolayer during sliding. Although variations in the coefficient of friction were observed, wear resistance was found to be more strongly influenced by microstructural integrity and reinforcement distribution.

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The design, manufacturing, and testing of vortex tubes have gained significant attention in recent years due to their potential for energy separation and cooling applications. Vortex tubes are compact devices that utilize the tangential flow of compressed gas to create hot and cold airstreams without the need for any moving parts. This abstract provides a comprehensive overview of the design principles, manufacturing techniques, and testing procedures employed in the development of vortex tubes. The first part of the abstract focuses on the design considerations for vortex tubes. It covers the geometric parameters, such as the length, diameter, and tangential inlet angle, which significantly influence the performance characteristics of the vortex tube. Various design modifications, including nozzle insertions, inner tube geometry variations, and vortex chamber configurations, are discussed to optimize the separation efficiency and temperature differentials of the vortex tube. The second part highlights the manufacturing techniques employed in producing vortex tubes. It explores the selection of materials, such as stainless steel, aluminum, and polymers, and discusses their suitability for different applications. The abstract also covers the fabrication methods, including machining, welding, and additive manufacturing, with a focus on achieving accurate dimensions, precise tolerances, and high-quality surface finishes. The third part focuses on the testing procedures used to evaluate the performance of vortex tubes. Experimental techniques, such as thermal imaging, flow visualization, and temperature measurement, are discussed to analyze the velocity profiles, temperature distributions, and pressure drops inside the vortex tubes. Performance parameters, such as the temperature separation ratio and cooling capacity, are quantitatively assessed to validate the design and manufacturing processes.

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Production scheduling plays an important role in improving productivity, machine utilization, and operational efficiency in modern manufacturing industries. This research presents a multi-objective optimization framework for production scheduling using the NSGA-II algorithm considering maintenance and energy constraints. The proposed model integrates production scheduling, preventive maintenance planning, and energy-aware optimization to minimize makespan, reduce energy consumption, and improve machine reliability simultaneously. Different scheduling parameters such as processing time, machine allocation, maintenance intervals, and energy utilization were analyzed using simulation-based experimentation. The NSGA-II optimization approach generated Pareto-optimal solutions that significantly improved production efficiency and reduced maintenance downtime and peak energy demand. Comparative analysis with traditional scheduling approaches demonstrated superior performance of the proposed model in terms of machine utilization, operational cost reduction, and sustainability improvement. Statistical analysis including ANOVA and regression analysis validated the effectiveness of the optimization framework. The proposed intelligent scheduling system provides strong industrial applicability for smart manufacturing and Industry 4.0 environments. The study also highlights future opportunities for integrating artificial intelligence, IoT, and digital twin technologies in intelligent production scheduling systems.

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Machine downtime is one of the major factors affecting productivity, equipment utilization, and operational efficiency in manufacturing industries. This study presents an experimental investigation and optimization of preventive maintenance parameters for reducing machine downtime and improving reliability. Maintenance-related factors such as maintenance interval, inspection frequency, lubrication frequency, and cleaning schedule were analyzed using statistical techniques. Downtime records, breakdown frequency, MTBF, MTTR, availability, and productivity data were collected and evaluated before and after implementing an optimized preventive maintenance schedule. The results demonstrated a significant reduction in downtime and breakdown frequency, along with improvements in machine availability, reliability, and production output. Statistical analysis identified inspection and lubrication frequency as the most influential maintenance parameters. The proposed maintenance optimization approach offers a practical and cost-effective solution for enhancing equipment performance and productivity in small and medium manufacturing industries.

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Floods are one of the leading causes of bridge failures, resulting in infrastructure damage, traffic disruption, and significant risks to human life. Conventional bridges lack intelligent monitoring systems capable of detecting flood conditions and responding automatically. This paper presents an IoT-based Smart Bridge system designed to improve bridge safety through real-time environmental monitoring and automated operation. The proposed system integrates a water level sensor, a rain sensor, an ATmega328 microcontroller, and a servo motor to continuously monitor flood conditions and initiate bridge lifting when water levels exceed a predefined threshold. The microcontroller processes sensor data and activates the lifting mechanism without requiring human intervention, thereby reducing the possibility of accidents during floods. A prototype of the system was developed and tested under simulated flood conditions, demonstrating reliable sensor performance, rapid response, and effective bridge operation. The proposed solution is economical, energy-efficient, and suitable for deployment in rural and flood-prone regions where conventional monitoring infrastructure is limited. The study highlights the potential of IoT and embedded systems to enhance bridge safety, minimize disaster-related risks, and contribute to the development of intelligent transportation infrastructure.

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