Volume 14 | Issue 6

 Abstract

This study investigates the structural, piezoelectric, and ferroelectric properties of lead-free Barium Calcium Titanate-Cobalt Ferrite (BCT-CFO) magnetoelectric composites. The samples were synthesized using a solid-state reaction method and characterized using SEM, EDS, and X-ray diffraction techniques.

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BCT-CFO, Magnetoelectric composites, Ferroelectricity, Piezoelectricity, SEM

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Metal chalcogenide-carbon nanocomposites are gaining significant attention as advanced materials for modern energy storage and conversion technologies. Their effectiveness arises from the complementary properties of both components: metal chalcogenides provide high electrochemical activity and multiple redox states, while carbon materials enhance electrical conductivity, mechanical strength, and surface area. This review highlights recent progress in synthesis approaches, structural engineering, and applications of these nanocomposites in batteries, supercapacitors, electrocatalysis, and photocatalysis. Additionally, key limitations such as structural instability, volume expansion, and scalability challenges are critically discussed, along with future research directions for real-world applications.

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Metal Chalcogenide, Carbon Nanocomposites, Energy Storage, Energy Conversion, Chemical Vapor Deposition, Electrospinning

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In this study, the binary mixtures n-butanol with formamide were investigated, focusing on their dielectric constant, density and refractive index. The dielectric constants were measured to assess the electrostatic interactions within the mixtures, density values provided insights into the overall composition and packing of molecules. Additionally refractive index data offered information on the optical properties of the mixtures, through systematic analysis. we observed variations in these properties in n-butanol with formamide binary mixtures. These findings contribute to a deeper understanding of the physicochemical behaviour of these mixtures. From the experimental dielectric data, excess dielectric constant, Kirkwood correlation factor, Bruggeman factor are estimated and reported in the study. The results show that the dielectric constants, densities and refractive index of the binary mixtures, decreases with increasing percentage of volume of n-butanol and the study shows the presence of molecular interactions and hydrogen bonding in the binary mixtures.

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 Keywords

Binary liquid mixtures, Dielectric constant, Kirkwood correlation factor, Hydrogen bonding, Density and refractive index, Formamide-n-butanol system, Bruggeman factor

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HelioFusion Smart Solar Tracking Hybrid PCM Cryo-Refrigerator is an advanced and energy-efficient refrigeration system designed to operate using renewable solar energy. The system integrates a dual-axis solar tracking mechanism, which continuously aligns the solar panel with the sun to maximize energy absorption and improve overall efficiency. To ensure uninterrupted cooling, the system incorporates Phase Change Material (PCM) for thermal energy storage. PCM stores excess cooling energy and maintains the desired temperature even during power interruptions or low solar availability, thereby enhancing system reliability.

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 Keywords

Solar Energy, Dual-Axis Solar Tracking, IoT-Based Monitoring, Phase Change Material (PCM), Thermal Energy Storage, Heat Pipe Technology, Energy Efficiency, Smart Refrigeration, Off-Grid Applications.

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Traditional cam-follower test rigs rely on manual measurement techniques using tachometers, vernier scales, and protractors to analyze simple harmonic motion (SHM). These conventional methods are prone to human error, limited in precision, and unable to provide real-time data visualization. This research presents the development of an advanced digital cam-follower test rig integrating sensor-based data acquisition, automated measurement, and real-time computational analysis. The system employs a Royal Enfield Bullet cam-follower mechanism with a rotary encoder for RPM and angular position measurement, and a Linear Variable Differential Transformer (LVDT) for follower displacement measurement. An Arduino microcontroller transmits data to a custom Python software platform which implements signal processing algorithms to generate kinematic graphs: cam angle vs. follower displacement, velocity, and acceleration. The system also automatically reconstructs the complete cam profile. Experimental validation demonstrates measurement accuracy improvement of 95%, data acquisition time reduced by 80%, and the capability to analyze complex motion patterns previously undetectable with manual methods. This digital test rig serves as a modern pedagogical tool bridging theoretical concepts with Industry 4.0 measurement technologies.

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Cam-follower mechanism, simple harmonic motion, LVDT sensor, rotary encoder, data acquisition, Arduino, kinematic analysis, Python software.

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This paper presents the design, structural analysis, fabrication, and experimental testing of a 1-ton capacity pillar-mounted jib crane with 180-degree controlled rotation, developed to address material handling challenges in small and medium-scale industrial environments. Conventional jib cranes providing 360-degree rotation impose safety risks in confined workspaces. The proposed crane employs an ISMB 250 structural steel boom as a cantilever beam, supported by a hollow circular steel mast of 225 mm outer diameter.

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Jib Crane; ISMB 250; Structural Analysis; Material Handling; Cantilever Beam; Buckling; Fabrication

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Solar desalination has emerged as a sustainable and environmentally friendly solution to address the growing global demand for potable water, particularly in arid and remote regions where conventional water treatment technologies are economically or logistically unfeasible. With increasing freshwater scarcity driven by population growth, industrialization, and climate change, solar stills offer a simple, low-cost, and renewable energy-based desalination method. However, despite their advantages, conventional solar stills suffer from inherently low productivity due to limited heat absorption, poor evaporation rates, and significant thermal losses, which restrict their large-scale applicability.To overcome these limitations, recent research has focused on enhancing the performance of solar stills through hybridization techniques, among which the integration of solar air heaters has shown promising potential. This review paper aims to systematically analyze and synthesize existing experimental studies on hybrid solar stills coupled with solar air heaters to evaluate their effectiveness in improving distillate yield and overall thermal efficiency.The review methodology involves a comprehensive literature survey of peer-reviewed articles obtained from reputed journal databases such as Scopus, Web of Science, ScienceDirect, and Springer, covering publications primarily from 2010 to 2025. Selected studies are critically examined based on experimental configurations, operating conditions, climatic influences, and performance metrics such as daily yield, efficiency, and cost analysis.Key findings from the reviewed literature indicate that the integration of solar air heaters significantly enhances basin water temperature, accelerates evaporation rates, and improves overall system productivity compared to conventional solar stills. Additional augmentation techniques such as the use of phase change materials (PCM), nanofluids, external condensers, and optimized basin geometries further contribute to performance improvement. Among these, combined systems incorporating solar air heaters with thermal energy storage and advanced materials demonstrate the highest efficiency gains and yield enhancements.In conclusion, hybrid solar stills integrated with solar air heaters present a viable and efficient solution for improving desalination performance. Future research should focus on optimizing system design, integrating advanced materials, conducting long-term field studies, and performing economic.

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Efficiency Gains: Yield Improvement: Optimal Conditions: Condensation Recovery.

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The present study focuses on the design, development, and performance evaluation of a hemispherical solar dryer for agricultural applications. Drying is an essential process for preserving food materials by reducing their moisture content, thereby extending shelf life and minimizing post-harvest losses. Conventional open sun drying methods are associated with several limitations such as contamination, uneven drying, and dependence on climatic conditions. To overcome these challenges, a hemispherical solar dryer was developed using a dome-shaped transparent structure that enhances heat retention through the greenhouse effect. The system consists of an absorber surface, drying chamber with trays, and a controlled airflow mechanism.

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 Keywords

Hemispherical Solar Dryer, Solar Drying, Agricultural Drying, Greenhouse Effect, Moisture Removal, IoT Monitoring, Renewable Energy, Drying Efficiency

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The present study focuses on the hybrid solar thermal water purification system with metaheuristic optimization for improved efficiency aimed at addressing the growing demand for safe drinking water using renewable energy sources. The proposed system utilizes solar energy as the primary power input, which is stored in a battery and subsequently used to operate a heating unit. Water is heated to a controlled temperature below its boiling point to initiate the purification process. The generated vapor is then condensed to obtain distilled water, which undergoes an additional filtration stage using a suitable filtering medium to enhance water quality. A secondary condensation stage ensures that the purified water is delivered at ambient temperature for direct consumption. The development process followed a systematic product design approach, beginning with the identification of user requirements and proceeding through design, prototyping, and evaluation stages. Furthermore, an economic assessment was carried out to evaluate the feasibility of large-scale production. To improve system performance, metaheuristic optimization techniques were applied to key processes, including heating, cooling, and purification. The results indicate that the proposed system is an energy-efficient and cost-effective solution for decentralized water purification, particularly suitable for rural and remote areas. If implemented effectively, the system has significant potential to alleviate the scarcity of potable water.

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 Keywords

Solar Energy, Water Purification, Metaheuristic Optimization, Thermal Distillation, Hybrid System, Renewable Energy, Sustainable Water Treatment

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The rapid proliferation of high-energy-density Lithium-ion Batteries (LIBs) in electric vehicles and grid-scale storage has shifted the critical bottleneck from energy capacity to thermal stability. Conventional air and liquid cooling methods increasingly struggle to manage the localized heat flux generated during Extreme Fast Charging (XFC) and high-discharge cycles, often leading to capacity fade or catastrophic thermal runaway. This review evaluates the transition from macro-scale cooling to nanomaterial-based thermal control, focusing on the integration of nanotechnology within passive and active cooling architectures.

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 Keywords

Lithium-ion Battery (LIB), Thermal Management System (BTMS), Nanofluids, Phase Change Materials (PCM), Graphene, Thermal Runaway, Heat Transfer Enhancement.

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The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming agriculture into a data-driven, automated, and sustainable domain. By integrating intelligent analytics with sensor-rich environments, smart agriculture aims to optimize crop yields, resource utilization, and food supply chains. This review critically examines recent developments in AI-enabled IoT for agriculture, highlighting architectures, algorithms, applications, and implementation challenges. We synthesize insights from more than forty-five scholarly works (2020-2025) and provide a thematic analysis of research trends. Particular attention is given to connectivity constraints, interoperability gaps, cybersecurity risks, and socio-economic barriers that hinder adoption. Comparative evaluations of existing IoT-AI frameworks demonstrate notable improvements in precision farming, yet reveal persistent limitations in scalability, explainability, and energy efficiency. The paper identifies research opportunities in edge/fog computing, federated learning, explainable AI (XAI), and blockchain-enabled trust frameworks. By offering a comprehensive synthesis of progress and open challenges, this review positions AI-driven IoT as a cornerstone for Agriculture 5.0 and provides guidance for researchers, policymakers, and practitioners working toward resilient, inclusive, and sustainable food systems.

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Smart Agriculture, Artificial Intelligence, Internet of Things, Precision Farming, Machine Learning, Cybersecurity, Agriculture 5.0

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Urban traffic congestion significantly impacts emergency response times and public transport efficiency. Traditional fixed-time and semi-actuated signal systems fail to adapt dynamically to such priority needs, resulting in delayed emergency services and unreliable bus schedules. This project presents a Self-decisive traffic monitoring at crossroad junction system that integrates vehicle-in-urgency and public transport first preference mechanisms using IoT, sensors and vehicle-to-infrastructure (V2I) communication. The system detects approaching vehicle-in-urgency vehicles or buses via RFID, GPS, or camera-based sensing, predicts their arrival, and temporarily overrides normal signal cycles to grant a green corridor while ensuring pedestrian and cross-traffic safety. Conditional preference is also provided to delayed public transport vehicles to enhance schedule that follows. The system is assembled using Raspberry Pi 5 and the program code is executed using Thonny.

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Smart Traffic Monitor and Control, Emergency Vehicle Pre-emption, Public Transport Priority, IoT and V2I Communication, Adaptive Signal Control, Intelligent Transportation System (ITS), Crossroad junction

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 Abstract

Water scarcity is becoming a serious issue in many urban areas due to increasing population and excessive consumption of freshwater resources. Grey water, which includes wastewater generated from household activities such as washing, bathing, and cleaning, can be reused for non-potable purposes if properly monitored. This paper proposes an IoT-based smart grey water management system using Raspberry Pi 4 to monitor water quality parameters such as pH and turbidity. The system automatically decides whether the water is reusable or not. If the water quality meets predefined standards, a Solenoid Valve directs the water to a reusable storage tank; otherwise, it is diverted to a waste tank. Sensor data is transmitted to the cloud platform ThingSpeak, allowing users to monitor water quality through a mobile device or laptop. The proposed system improves water conservation and enables intelligent water management in residential environments.

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 Keywords

Grey Water, IoT, Water Quality Monitoring, Raspberry Pi, Turbidity Sensor.

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 Abstract

Groundwater accumulation in mining areas leads to operational delays, increased maintenance costs, and safety hazards. Conventional dewatering systems rely on diesel-powered pumps or grid electricity, which are costly and environmentally harmful. This paper presents the design and implementation of a solar-powered automated dewatering system using an Arduino-based control mechanism. The system utilizes an ultrasonic sensor to monitor water levels and a relay-controlled DC pump for automatic water removal. A solar panel integrated with an MPPT charge controller provides a sustainable energy source, ensuring reliable operation in remote locations. To enhance system capabilities, an IoT-based monitoring approach is proposed, enabling real-time data visualization and remote access through cloud platforms such as ThingSpeak or Blynk. The proposed system aims to reduce human intervention, improve efficiency, and promote eco-friendly mining operations. Experimental observations indicate stable operation, efficient energy utilization, and effective automation, making the system suitable for small and medium-scale mining applications.

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Solar Energy, Dewatering System, Mining Operations, IoT, Automation, Ultrasonic Sensor, Renewable Energy

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Electric fences are widely used in agricultural fields, industrial premises, and restricted areas for perimeter protection. However, unauthorized use, tampering, and accidental contact with electric fences can lead to safety hazards, property damage, and misuse of electrical energy. This paper presents an IoT-based smart hardware system designed to detect and prevent unauthorized use of electric fences through real-time monitoring, control, and alert mechanisms. The proposed system integrates a microcontroller-based unit with voltage and current sensors, motion detection modules, relay control circuits, and wireless communication technology such as Wi-Fi or GSM. The hardware continuously monitors fence status, power consumption, and intrusion attempts. If unauthorized activation, wire cutting, bypassing, or abnormal current flow is detected, the system immediately disconnects the fence power supply using an automated relay mechanism and sends instant alerts to the owner or security personnel through a mobile application or SMS.

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IOT, electric fence, unauthorized

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 Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction, communication, and behavior, making early diagnosis crucial for effective intervention. Traditional diagnostic methods rely on behavioral assessments, which are often time-consuming and subjective. In recent years, deep learning-based computer vision techniques have emerged as promising tools for automated and non-invasive autism detection using facial images. This paper presents a comprehensive review of deep learning approaches for facial image-based autism detection, focusing on convolutional neural networks, transfer learning models, and hybrid architectures. The study analyzes commonly used datasets, evaluation metrics, and performance trends, highlighting that modern approaches achieve high accuracy but remain limited by dataset size, diversity, and clinical validation. Furthermore, key challenges such as ethical concerns, generalization issues, and lack of interpretability are discussed. The paper also outlines future research directions, including multimodal learning, explainable artificial intelligence, and the adoption of advanced architectures, to enhance the reliability and applicability of automated ASD detection systems

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Autism Spectrum Disorder, Deep Learning, Facial Image Analysis, Convolutional Neural Networks, Transfer Learning, Computer Vision, Medical Image Analysis, Explainable AI.

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Memristor based systems represent an innovative approach to secure communications systems, nonlinear dynamics and hardware based efficient cryptographic systems. These systems demonstrate a unique combinatorial behavior produced by their non-linearity and ability to retain memory. As a result, they can exhibit a large variety of complex chaotic behaviors. When combined with field programmable gate arrays (FPGAs), memristor based chaotic systems may have the ability to be implemented in real-time, scalable and reconfigurable manner that makes them excellent candidates for current and future engineering applications. In this article, we review the literature pertaining to the generation of memristor based chaos on FPGA platforms through three major categories of discussion including theoretical foundations of memristors, implementation techniques for memristors and performance metrics of memristors from various study's performed within the literature. Performance metrics will be evaluated using comparison metrics including: hardware utilization, power consumption rate, throughput rate and system complexity. Finally, this research paper will identify the key gaps within existing research on memristor based chaos, outline limitations and discuss possibilities for future research. The combination of FPGA reconfigurability and memristor non-linear dynamics will create a new paradigm for the generation of high-speed, efficient and secure chaotic systems that can potentially be used in the future.

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FPGA, Memristor, Chaos, Cryptography, Throughput, Reconfigurability

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This project presents an IoT-based Smart Medication Management and Pill Dispenser System designed to promote medication adherence through automation, tracking, and remote supervision. The system ensures that patients receive the right dose at the right time, minimizing human errors and noncompliance. It integrates IoT technology, sensors, and cloud connectivity to automatically dispense medication, monitor intake, and alert both patients and caregivers. A companion mobile application provides real-time updates, reminders, and visualization. By bridging healthcare with smart automation, the system improves patient safety, enhances treatment outcomes, and supports the growing demand for digital healthcare management Introduction

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 Keywords

Internet of Things (IoT), Smart Medication System, Pill Dispenser, Medication Adherence, Automated Drug Delivery, Health Monitoring, Embedded Systems, ESP32 .

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 Abstract

Waste management has become a major global challenge due to rapid urbanization and increasing population. Improper segregation of waste at the source leads to environmental pollution, inefficient recycling, and health hazards. This paper proposes a Smart Waste Segregation and Recycling System using Internet of Things (IoT) technologies. The system utilizes multiple sensors such as moisture sensors, metal sensors, and IR proximity sensors to automatically classify waste into biodegradable, recyclable, and non-biodegradable categories. A motor-driven mechanism ensures proper segregation into designated compartments. Additionally, ultrasonic sensors monitor bin fill levels and send real-time alerts using IoT modules. The system also incorporates a data-driven reward mechanism to encourage public participation. The proposed solution enhances waste management efficiency, reduces manual labor, and promotes sustainable environmental practices.

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 Keywords

IoT, Waste Segregation, Smart Bin, Sensors, Recycling, Automation.

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 Abstract

Proper medication intake is a major concern, particularly for elderly individuals and patients suffering from long-term illnesses. Missing doses or consuming incorrect medication can lead to severe health complications and increased medical risks. This paper proposes an Integrated Drug Dispenser system that automates the process of medicine distribution while improving patient safety and adherence.

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 Keywords

Smart Medication System, Automated Drug Dispenser, Raspberry Pi, GSM Aler, Healthcare IoT, Elderly Assitance

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