VW Applied Sciences is a peer-reviewed, Open Access, and Double-Blind refereed international journal published quaterly by VALLWAY RESEARCH. The journal provides a global platform for researchers, academicians, industry professionals, and practitioners to disseminate original research, comprehensive reviews, and applied studies in a wide range of science and engineering disciplines. Established to bridge the gap between scientific innovation and industrial application, VW Applied Sciences focuses on publishing high-quality contributions that demonstrate practical implementation and applied research value. Title: VW Applied Sciences Editor-in-Chief: Dr. Md Irfanul Haque Siddiqui Online ISSN: 2582-5615 Frequency: Quaterly Published Since: 2019 Publication Format: Online Language: English Copyright: © VALLWAY RESEARCH Publisher: VALLWAY RESEARCH PVT. LTD. INDIA The journal welcomes submissions in the following categories: · Original Research Articles · Review Papers and Short Reviews · Technical Notes · Case Studies · Short Communications Indexing and Abstracting VW Applied Sciences is currently under evaluation for inclusion in Scopus. The journal adheres strictly to publication ethics, peer-review transparency, and editorial independence in line with SCOPUS and international indexing body guidelines. Open Access and Licensing All articles published in VW Applied Sciences are made freely available under an Open Access model. Authors retain copyright under a [Creative Commons license, e.g., CC BY 4.0], allowing for free distribution and reuse with proper attribution.
Assessment of UAV-Based Remote Sensing for Environmental Surveillance
Unmanned Aerial Vehicles (UAVs) have become indispensable tools for modern environmental surveillance due to their agility, cost-effectiveness, and ability to acquire high-resolution spatiotemporal data. This review evaluates the major technological, operational, and analytical components of UAV-based remote sensing and its applications across environmental monitoring domains. UAVs equipped with multispectral, hyperspectral, thermal, and LiDAR sensors enable detailed observations of vegetation health, water quality, soil conditions, wildlife distribution, and disaster-affected regions. The integration of geospatial analytics, machine learning algorithms, and cloud data platforms further enhances the interpretability and accuracy of UAV-derived information. Despite their advantages, UAV systems face challenges related to regulatory restrictions, limited battery endurance, weather sensitivity, data processing complexity, and the need for standardized methodologies. The review synthesizes current research findings to highlight the strengths and limitations of UAV-based environmental surveillance and identifies emerging trends such as swarm UAV systems, AI-driven image interpretation, and real-time mapping through edge computing. The study concludes that UAVs will continue to expand their role in environmental studies, making monitoring systems faster, more adaptive, and more data-rich.
Copyright (c) 2023 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Development of Smart Wearable Systems for Physiological Signal Monitoring
Smart wearable systems have transformed modern healthcare by providing real-time, continuous monitoring of physiological signals. Advances in flexible electronics, low-power sensor technology, and wireless communication have enabled wearables to track parameters such as ECG, heart rate, blood oxygen saturation, skin temperature, and motion analysis with increasing accuracy. This review explores the sensing mechanisms, system architecture, and analytical frameworks used in wearable devices. The role of photoplethysmography, ECG electrodes, thermistors, and inertial measurement units is analyzed in detail. Additionally, the paper discusses wireless protocols such as BLE, Wi-Fi, and NFC that facilitate data transmission to cloud and edge platforms. Wearables also enhance chronic disease management, elderly monitoring, and fitness assessment. However, they face challenges related to sensor noise, motion artifacts, battery limitations, and cyber-security vulnerabilities. Emerging trends such as AI-enabled predictive diagnostics, energy harvesting, and integration with Digital Twins are expected to significantly enhance the functionality of wearable systems. This review concludes that smart wearables will continue to expand their role in clinical and personal health management.
Implementation of Digital Twin Frameworks for Industrial Process Optimization
Digital Twin (DT) frameworks have become central to Industry 4.0 due to their ability to create synchronized virtual models of physical industrial systems. These models support real-time analytics, predictive maintenance, and performance optimization. This review discusses the evolution, architecture, and applications of Digital Twins in industrial settings, demonstrating how IoT sensors, simulation engines, and machine learning algorithms collectively enable a dynamic bidirectional relationship between virtual and physical environments. The study further examines operational benefits such as reduced downtime, improved resource utilization, and enhanced process visibility. It also evaluates existing challenges, including high computational demand, lack of interoperability, cyber-security risks, modelling complexity, and integration issues with legacy machines. Industrial case studies highlight measurable gains achieved through Digital Twin adoption in manufacturing, power systems, and process industries. The review concludes that Digital Twins are indispensable for intelligent automation and industrial optimization, with future advancements expected through AI-driven simulation, cloud-edge integration, and autonomous decision systems.
Optimization of Renewable Energy Microgrids Using Smart Control Algorithms
Renewable energy microgrids have emerged as a promising solution for decentralized power generation, enabling greater energy independence, resilience, and sustainability. With the increasing penetration of intermittent renewable sources such as solar and wind, smart control algorithms are essential for ensuring stable operation, optimal power flow, and reliability. This review provides a comprehensive examination of optimization techniques used in microgrid energy management systems. It explores classical control methods, heuristic optimization, model predictive control, and AI driven algorithms that address uncertainties in generation and demand. The paper assesses strategies for real-time scheduling, load balancing, energy storage optimization, and demand-side management. Challenges such as variability of renewable generation, communication constraints, cybersecurity risks, and integration with legacy systems are discussed in detail. The review also highlights recent advancements in multi-agent systems, reinforcement learning, and adaptive controllers that enhance microgrid flexibility and resilience. Practical case studies demonstrate how smart optimization improves energy efficiency, reduces operational cost, and supports carbon-neutral energy ecosystems. The paper concludes by outlining future research opportunities in digital twin modeling, transactive energy systems, and hybrid control architectures that will shape next- generation renewable microgrids.
Application of Machine Vision Techniques for Automated Quality Inspection
Machine vision has become an indispensable component of modern industrial automation, enabling accurate, efficient, and consistent quality inspection. Unlike conventional manual inspection, which is prone to fatigue and inconsistency, machine vision offers non-invasive, objective, and high-speed evaluation of products across diverse manufacturing domains. This review examines the evolution, architecture, and contemporary implementation of machine vision techniques for automated inspection tasks. Emphasis is placed on image acquisition methods, illumination strategies, feature extraction, and classification algorithms, including the transition from classical image processing to advanced deep learning–based models. Key challenges such as variable lighting, occlusion, surface reflectivity, and computational cost are analyzed alongside the emergence of real-time inspection enabled by edge AI and optimized convolutional neural networks. The paper also reviews case studies demonstrating applications in electronics, automotive systems, food processing, textile inspection, and pharmaceutical packaging. The integration of robotics, IoT environments, and cloud analytics further expands the capabilities of machine vision-based inspection systems. The review concludes by considering future innovations such as self- learning inspection pipelines, multimodal imaging, explainable AI, and 3D vision technologies poised to reshape industrial quality assurance.
Design and Evaluation of Wireless Sensor Networks for Precision Agriculture
Precision agriculture aims to optimize crop yield, resource use, and environmental sustainability through data-driven decision-making. Wireless Sensor Networks (WSNs) have emerged as a cornerstone technology for achieving these goals by enabling real-time monitoring of soil moisture, temperature, nutrient levels, microclimate, pest activity, and crop health. This paper reviews the design principles, architectures, communication protocols, and evaluation frameworks that support the effective deployment of WSNs in agricultural settings. The study examines node placement strategies, energy-efficient routing mechanisms, fault tolerance methods, and sensor calibration techniques that enhance measurement reliability. Challenges such as connectivity limitations, power constraints, interference, and environmental variability are examined alongside emerging innovations including LPWAN technologies, energy harvesting, and AI-assisted sensing. Case studies from smart farms illustrate improvements in irrigation scheduling, fertilizer optimization, and disease prediction enabled by WSNinfrastructures. This review concludes that wireless sensor networks are indispensable for sustainable agriculture and will continue to evolve through multi-sensor integration, long-range communication standards, and intelligent edge based analytics.
Integration of Edge Computing Architectures for Real-Time Healthcare Monitoring
The demand for continuous and intelligent healthcare monitoring has intensified with the rise of chronic illnesses, aging populations, and the increasing need for real-time physiological assessment. Edge computing has emerged as a critical enabler of next-generation healthcare systems by bringing computational power closer to patient-side devices and reducing latency inherent in cloud-based processing. This review explores the architectures, models, and implementation strategies that enable edge-driven healthcare monitoring. The paper evaluates sensor integration methods, distributed analytics, adaptive learning algorithms, and communication topologies that support low-latency and energy efficient monitoring. It also discusses hybrid models in which cloud and edge systems collaborate to enhance inference accuracy, optimize network traffic, and provide scalable health analytics. Major challenges including data heterogeneity, interoperability, patient privacy, and cybersecurity are examined alongside ongoing advancements such as federated learning, intelligent gateways, and AI-powered edge nodes. Case studies from telemedicine, cardiac monitoring, and emergency response systems illustrate the advantages of edge computing in delivering timely decision support. The review concludes by identifying future directions related to predictive diagnostics, autonomous care systems, and edge-integrated biosensor networks that will shape the future of personalized digital healthcare.
Development of AI-Driven Predictive Maintenance Models for Industrial Machinery
Artificial intelligence has transformed industrial operations by enabling the prediction of machine failures before they occur, substantially reducing downtime and maintenance costs. AI-driven predictive maintenance integrates machine learning algorithms, sensor networks, and real time analytics to assess machinery performance and identify early signs of degradation. This review examines the evolution, architecture, and functioning of predictive maintenance systems that rely on artificial intelligence. Emphasis is placed on the roles of machine learning, deep learning, analytics platforms, and industrial Internet of Things (IIoT) infrastructures that support large-scale condition monitoring. The paper discusses data acquisition challenges, algorithmic performance issues, and the critical need for interpretability in industrial environments. Case studies from manufacturing, power systems, and transportation demonstrate how AI- enabled solutions outperform traditional maintenance strategies in accuracy and operational efficiency. The review also analyzes emerging trends, including digital twins, federated learning, and edge-AI implementations designed to enhance resilience and scalability in smart factories. The findings highlight that AI-driven predictive maintenance is central to the modernization of Industry 4.0 ecosystems and will continue to evolve as industries transition toward fully autonomous and self-optimizing systems.
Evaluation of Biocompatible 3D Printed Prosthetics with Enhanced Functionality for Medical Applications
Thermal Advances in additive manufacturing have transformed the development of prosthetic devices, offering rapid customization, lower production costs, and superior adaptability to patient needs. This research evaluates the performance, biocompatibility, and functional enhancement of 3D printed prosthetics produced using medical-grade polymers and composite materials. Experimental assessments were conducted to analyze mechanical strength, surface finish, microbial resistance, and user comfort, while biocompatibility tests examined cytotoxicity, allergenic responses, and tissue interaction behavior. Functionality enhancements—such as embedded sensors, lightweight structural lattices, and adjustable joint mechanisms—were also studied in detail. The results demonstrate that 3D printed prosthetics fabricated from PLA-based composites and thermoplastic polyurethane exhibit strong mechanical resilience and excellent wearer compatibility, while sensor-integrated versions significantly improve adaptability and monitoring. The findings confirm that modern additive manufacturing technologies can produce clinically reliable prosthetics comparable to traditional counterparts, ensuring improved patient mobility and comfort. This study provides important insights into material selection, design optimization, and performance evaluation for next-generation prosthetic development, enabling pathways for medical institutions and manufacturers to adopt advanced 3D printed solutions.
Copyright (c) 2022 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Study of Novel Photocatalysts for Efficient Degradation of Environmental Pollutants under Visible Light
Growing industrialization and urban activities have increased the release of harmful organic pollutants into aquatic and terrestrial ecosystems, necessitating advanced remediation technologies that operate efficiently under sustainable conditions. Visible-light-driven photocatalysis has emerged as a promising environmental treatment method due to its ability to utilize abundant solar radiation while achieving high degradation rates for persistent contaminants. This study evaluates novel photocatalysts including doped metal oxides, polymeric carbon nitride, and plasmonic nanostructures and investigates their physicochemical behavior, photocatalytic efficiency, charge-carrier dynamics, and structural stability under continuous visible-light exposure. Experimental work involved pollutant degradation trials using dyes, pesticides, and pharmaceutical residues as model contaminants, while monitoring reaction kinetics and mineralization potential. Results demonstrated that metal–nonmetal co-doped TiO₂ and Ag–g-C₃ N₄ hybrids exhibited significantly enhanced visible-light absorption and reduced electron–hole recombination, leading to degradation efficiencies exceeding 90% for selected pollutants. Stability tests confirmed minimal structural decay after repeated cycles. The findings underscore the potential of engineered photocatalysts as scalable solutions for environmental rehabilitation. The work contributes important guidelines for material selection, photocatalyst engineering, and practical deployment in wastewater purification systems.
Experimental Assessment of Thermal Energy Storage Systems for Renewable Energy Integration
Thermal Energy Storage (TES) systems have become an essential component in modern renewable energy technologies, offering potential solutions to intermittency challenges and enabling stable grid integration. This research experimentally evaluates the performance, efficiency, and operational behavior of latent and sensible heat storage configurations under varying renewable energy input conditions. A comprehensive assessment was conducted using phase change materials, molten salts, and water-based heat storage units, focusing on charging–discharging cycles, heat retention duration, and thermal stability. The study further analyzes transient thermal response, energy density, and round-trip efficiency, correlating results with typical solar and wind generation profiles to determine their compatibility with real-time renewable operations. The findings highlight that latent heat–based TES systems outperform conventional sensible heat units in energy density and stability, while hybrid combinations provide optimal balance between cost and performance. Experimental results demonstrate that TES paired with solar photovoltaic and concentrated solar power units significantly enhances output reliability and peak-shaving capabilities. This work contributes new insights into TES behavior under dynamic renewable energy fluctuations and provides a strong foundation for designing efficient, scalable storage infrastructures for future sustainable energy networks.
Development and Characterization of Antimicrobial Coatings for Use in Public Health and Medical Devices
The rise of healthcare-associated infections (HAIs) and contamination incidents in public spaces has intensified the need for durable antimicrobial coatings capable of preventing microbial adhesion and proliferation. This study investigates the development, material formulation, and performance evaluation of antimicrobial coatings specifically designed for public health infrastructures and medical devices. Silver nanoparticles (AgNPs), copper oxide (CuO), and zinc oxide (ZnO) were incorporated into polymeric and sol-gel matrices to enhance antimicrobial activity, mechanical durability, and biocompatibility. Laboratory analysis focused on microbial inhibition efficiency, surface morphology, coating adhesion, and long-term stability under simulated environmental and clinical conditions. Results reveal that nanocomposite coatings significantly reduce the viability of E. coli, S. aureus, and fungal species, demonstrating bactericidal rates exceeding 95% under optimized concentrations. The findings emphasize the potential of engineered antimicrobial surfaces in reducing infection risks, enhancing medical-device safety, and improving public hygiene standards.
Implementation and Assessment of Remote Sensing Technologies for Environmental Protection and Disaster Management
Remote sensing technologies have transformed environmental protection and disaster management through their ability to provide timely, accurate, and large-scale observations. These technologies enable rapid detection of environmental degradation, early warnings for natural disasters, and continuous monitoring of vulnerable regions. This study investigates the implementation, performance, and comparative assessment of modern remote sensing systems—including satellite imagery, UAV-based sensing, multispectral and hyperspectral monitoring, and thermal observations—for effective environmental and disaster-response applications. A multi method analytical framework evaluates spatial resolution, temporal coverage, sensor sensitivity, and data-processing efficiency across different platforms. Case-study simulations demonstrate that integrated Earth-observation systems significantly improve prediction accuracy for floods, landslides, forest fires, and pollution dispersion. Results show that remote-sensing-enhanced response strategies reduce assessment delays by up to 45% and improve environmental-damage estimation by over 30% when combined with GIS-based analysis. The findings highlight the critical role of integrated remote-sensing systems in strengthening modern environmental management and disaster-risk-reduction mechanisms.
Inavestigation of Innovative Recycling Methods for Electronic Waste to Recover Valuable Metals Efficiently
Electronic waste (e-waste) has become one of the fastest-growing solid-waste streams globally, driven by rapid technological turnover and consumer demand for electronic devices. Traditional recycling approaches struggle with low recovery rates, high energy consumption, and environmental hazards associated with toxic components. This study investigates innovative and sustainable methods for recovering valuable metals—such as gold, silver, palladium, copper, and rare earth elements—from e-waste through advanced mechanical, chemical, and biological routes. A multi-method assessment is conducted using controlled laboratory simulations and comparative analysis of leaching efficiencies, energy requirements, and environmental impacts. Experimental results demonstrate that bioleaching using selective microorganisms and green solvent-assisted hydrometallurgy provide significant improvements in metal recovery efficiency while reducing the use of hazardous reagents. The findings highlight the potential of integrating hybrid techniques to optimize metal extraction from complex electronic scrap materials.
Modeling, Simulation, and Energy Efficiency of Hybrid Electric Vehicles under Urban Driving Conditions
Hybrid Electric Vehicles (HEVs) have emerged as a critical technological solution for improving fuel savings and reducing environmental impacts in congested urban environments. Urban driving conditions, characterized by frequent stop-and-go movement, variable speeds, and high idling time, impose unique energy demands on hybrid powertrains. This study presents comprehensive modeling and simulation of HEV performance under realistic city-traffic conditions. Using a validated multi-domain simulation environment, the research investigates component-level behavior, control-strategy efficiency, and overall energy consumption patterns. The study further evaluates regenerative braking potential, engine on/off scheduling, and battery State-of-Charge (SOC) stability. Results demonstrate that optimized energy- management algorithms can reduce fuel consumption by up to 28% while maintaining stable battery operation. Sensitivity analyses reveal that drive-cycle variability and traffic congestion levels significantly influence vehicle efficiency. The findings contribute to better prediction models and improved energy- management frameworks for next-generation HEVs.
Development and Testing of Advanced Filtration Membranes for Industrial Wastewater Treatment Applications
Industrial effluents generated by sectors such as textiles, pharmaceuticals, petrochemicals, and metallurgy contain hazardous pollutants including dyes, heavy metals, surfactants, and microplastics, making their treatment a major environmental challenge. Conventional techniques often fail to remove persistent contaminants or operate efficiently at large scales. This research explores the fabrication and testing of advanced PVDF based nanocomposite membranes enhanced with titanium dioxide (TiO₂ ) and graphene oxide (GO) to improve hydrophilicity, antifouling resistance, and filtration performance. Experimental results indicate that hybrid membranes demonstrate superior flux, higher rejection efficiency for dyes and metals, and improved mechanical durability in comparison to conventional polymeric membranes. The study uses simulated industrial wastewater and evaluates parameters such as flux recovery ratio, fouling resistance, and structural stability under different pressure and pH conditions. Findings highlight the strong potential of nanocomposite membranes for industrial wastewater treatment and their ability to reduce operational costs and enhance long-term sustainability. The study also examines utility, industrial relevance, implementation constraints, and future expansion possibilities for broader industrial adoption.
Simulation and Performance Analysis of Automated Systems in Precision Agriculture for Crop Yield Optimization
Automated systems have emerged as a transformative force in precision agriculture by integrating sensing technologies, robotics, and advanced modelling frameworks for yield optimization. This study conducts a comprehensive simulation-based performance analysis of automated agricultural systems using APSIM, AquaCrop, and agent-based modelling integrated within a dynamic digital twin environment. High-resolution data derived from soil-moisture sensors, multispectral aerial imagery, canopy-temperature indices, and location-specific climate measurements form the primary inputs for the simulation ecosystem. Several automated management scenarios, including dynamic irrigation scheduling, adaptive nitrogen delivery, and variable-rate seeding, are tested to evaluate their impact on agricultural productivity and resource-use efficiency. Simulation results show that automated irrigation systems reduce water consumption while maintaining yield stability, automated nitrogen delivery enhances nutrient-use efficiency, and variable-rate seeding improves plant population uniformity. Digital twin synchronization further strengthens predictive accuracy and operational adaptability. The findings highlight the utility of simulation-first evaluation as an essential step toward the safe, sustainable, and economically viable deployment of autonomous farm technologies. By demonstrating an average simulated yield increase exceeding twenty percent compared with uniform manual management, this study affirms the critical role of modelling-supported automation in shaping data-driven, resilient, and high-performance agricultural systems.
Fabrication and Electrochemical Analysis of Novel Energy Storage Devices Based on Nanostructured Materials
Nanostructured materials offer transformative potential for energy storage because they combine large surface area, controlled morphology, and enhanced electron/ion transport pathways, which together improve both power and energy density. In this work, we synthesize a range of nanostructured electrode materials including metal-oxide nanostructures (e.g., Mo-based oxides), carbon–metal oxide composites, and hybrid architectures using sol-gel, electrochemical deposition, and hydrothermal techniques. These materials are integrated into prototype supercapacitor and battery devices, where their electrochemical behavior is characterized via cyclic voltammetry, galvanostatic charge–discharge, and electrochemical impedance spectroscopy. Our results demonstrate that nanoscale structuring significantly reduces charge- transfer resistance, accelerates ion diffusion, and yields high capacitance retention over extended cycling. In particular, Mo-based nanostructures and Mn-oxide nanocomposites show exceptional pseudocapacitive behaviour, while carbon–metal oxide composites deliver synergistic benefits in stability and rate performance. This study validates scalable fabrication strategies for nanostructured electrodes and underscores their viability for next-generation high-performance energy storage systems
Field Evaluation of Smart Irrigation Systems Designed to Optimize Water Use Efficiency in Agricultural Practices
Smart irrigation systems have emerged as key technologies for addressing global water scarcity and enhancing agricultural productivity. These systems utilize sensors, communication networks, and intelligent algorithms to optimize irrigation schedules based on real-time soil moisture, climatic variables, and crop water stress indicators. Despite extensive laboratory and controlled environment testing, there remains a critical need for comprehensive field evaluation to understand system performance under diverse agricultural conditions. This study presents an in-depth field assessment of smart irrigation systems deployed across multiple crop fields varying in soil type, climate, and cropping patterns. System effectiveness was analyzed through water-use efficiency measurements, crop yield response, sensor reliability, and operational stability. Results demonstrate that smart irrigation systems significantly reduce water consumption while maintaining or increasing crop yield, particularly in regions experiencing erratic rainfall or limited water availability. The findings also highlight environmental and infrastructural challenges affecting system performance, such as sensor drift, soil heterogeneity, and network connectivity. This research contributes important insights for farmers, agronomists, and policymakers seeking to adopt water-efficient and technologically advanced irrigation solutions that support sustainable agricultural development.
Fabrication and Mechanical Characterization of Biopolymer Composites for Use in Biomedical Implant Devices
Biopolymer composites have gained significant attention in biomedical engineering due to their biocompatibility, biodegradability, and mechanical tunability, making them promising candidates for next-generation implantable devices. However, the gap between laboratory-scale fabrication and clinically reliable performance limits their widespread adoption. This study explores the systematic fabrication, structural optimization, and mechanical characterization of biopolymer composites intended for orthopedic, dental, and soft-tissue implants. Fabrication involved blending natural and synthetic biopolymers with bioactive reinforcements to improve strength, stiffness, and long-term stability. Mechanical behavior was assessed under tensile, compressive, flexural, fatigue, and impact conditions to evaluate suitability for load-bearing and semi-load-bearing applications. Microstructural imaging and degradation experiments further revealed how reinforcement dispersion and polymer crystallinity influence performance. Results show that tailored composite formulations can achieve mechanical properties comparable to conventional implant materials while offering superior biological integration and reduced risk of inflammatory response. This work strengthens the scientific foundation for developing safe, durable, and clinically viable biopolymer- based implants that align with future biomedical advancements and personalized medicine.
Application of Advanced Computer Vision Techniques for Automated Quality Control in Manufacturing Processes
Automated quality control has become essential for modern manufacturing systems as industries transition toward intelligent, autonomous, and data-driven production. Traditional manual inspection suffers from human error, fatigue, and limited repeatability, making it inadequate for high-precision or large- volume manufacturing. Advanced computer vision technologies, particularly those enabled by deep learning and multi-sensor integration, provide a scalable alternative capable of detecting micro-defects, dimensional inaccuracies, assembly faults, and surface anomalies in real time. This study presents an extensive investigation into the deployment of convolutional neural networks, transformer-based visual models, multispectral imaging, and industrial edge-AI systems for automated inspection across diverse manufacturing settings. Field implementation across multiple production lines demonstrates substantial improvements in accuracy, processing speed, and defect classification consistency. The results reinforce the role of machine vision as a cornerstone of Industry 4.0 and highlight the technological, environmental, and infrastructural factors influencing adoption. The study further evaluates operational challenges such as lighting variability, domain shift, dataset imbalance, and hardware constraints, offering strategies for building robust and scalable inspection pipelines. The findings provide a strong foundation for manufacturers seeking cost-effective and high-reliability automated quality control solutions.
Durability and Environmental Performance Testing of Eco-Friendly Construction Materials under Field Conditions
Eco-friendly construction materials are central to sustainable infrastructure, yet their long-term performance under real environmental exposure is still insufficiently validated. Laboratory experiments, while controlled and precise, fail to reproduce the complex interplay of variables rainfall, UV radiation, humidity, temperature cycling, freeze–thaw effects, and biological degradation that influence material stability in the field. This research investigates the durability and environmental behavior of four major sustainable materials: compressed stabilized earth blocks (CSEB), fly ash–based geopolymer concrete, recycled aggregate concrete, and natural fiber composites. Field exposure trials were conducted in humid tropical, arid, and temperate regions for 18–24 months. Measurements included structural degradation patterns, strength retention, moisture absorption, microstructural evolution through SEM, and leachate chemistry based on ASTM and ISO standards. Results indicate that geopolymer concrete demonstrates the highest resilience, while natural fiber composites and CSEB show climate-dependent degradation. Recycled aggregate concrete provides stable performance with moderate porosity-linked deterioration. Leachate analyses confirm environmental safety across all materials. The study contributes comprehensive, field- based evidence essential for sustainable construction codes, policy decisions, and industrial adoption.
Integration Strategies and Performance Analysis of Renewable Energy Sources in Microgrid Systems for Rural Electrification
Rural electrification remains a persistent challenge in many developing regions, where grid extension is often economically unfeasible due to difficult terrain, dispersed populations, and high infrastructure costs. Microgrid systems powered by renewable energy sources offer a sustainable and decentralized alternative capable of delivering reliable electricity to off-grid communities. This research examines the technical, economic, and operational dimensions of integrating solar, wind, biomass, and small-hydro systems into rural microgrids using optimized control architectures. High-resolution load data, resource assessments, and system simulations were employed to evaluate energy balance, reliability, stability, and cost performance. Results indicate that hybrid renewable microgrids, supported by advanced energy management systems, can achieve significant improvements in power availability, voltage stability, and lifecycle cost efficiency. Innovative integration strategies such as adaptive droop control, multi-agent coordination, and predictive storage optimization enhance operational resilience under fluctuating demand and variable renewable output. The study concludes that renewable-powered microgrids present a scalable, environmentally beneficial, and economically competitive solution for rural electrification. A comprehensive assessment of performance metrics demonstrates their potential to transform rural development by enabling productive use of energy, improving socio economic conditions, and minimizing dependence on fossil fuels.
Assessment of Urbanization Impacts on Air Quality Using High-Resolution Sensor Networks and Data Analytics
Urbanization continues to alter environmental systems, producing significant deterioration in urban air quality across developing and developed regions. Conventional monitoring stations, although technically sophisticated, are too sparse to reflect the micro-level variations produced by traffic flows, construction intensity, land-use transitions, and demographic pressures. This study assesses the impacts of urbanization on atmospheric conditions by deploying a high resolution network of low-cost sensors that measure PM2.5, PM10, NO2, CO, and O3. These measurements were analysed alongside urban growth indicators such as road density, population concentration, and expansion of built-up surfaces. Machine learning and spatial analytics were employed to understand pollution dynamics and predict future trends. Results reveal pronounced pollution hotspots, strong correlations between urban activity and pollutant increments, and substantial improvements in predictive performance when sensor data are integrated with land-use attributes. The study provides a detailed evaluation of how contemporary urban growth reshapes air quality and highlights the transformative value of sensor-driven environmental intelligence for sustainable planning.
Synthesis and Catalytic Performance Evaluation of Metal Nanoparticles in Industrial Chemical Reactions
Metal nanoparticles have emerged as highly efficient catalysts in a wide range of industrial chemical reactions due to their large surface-area to-volume ratio, tunable electronic structures and ability to facilitate rapid reaction kinetics. This study investigates the synthesis, characterization and catalytic performance of selected metal nanoparticles, including gold, silver, palladium and platinum, synthesized through chemical reduction, green synthesis and thermal decomposition methods. Precise control over nanoparticle morphology, size distribution and surface functionalization was achieved through optimization of synthesis conditions and stabilizing agents. The nanoparticles were characterized using ultraviolet–visible spectroscopy, X-ray diffraction, electron microscopy and surface area analysis to establish their structural and physicochemical properties. Catalytic performance was evaluated in representative industrial reactions such as hydrogenation, oxidation, carbon–carbon coupling and pollutant degradation. Experimental results indicate that smaller nanoparticles with well defined morphologies exhibit significantly enhanced catalytic efficiency, higher turnover frequencies and improved stability compared to conventional catalysts. Palladium nanoparticles demonstrated exceptional activity in cross-coupling reactions, while gold nanoparticles exhibited superior catalytic oxidation behavior. This study concludes that metal nanoparticles, when synthesized with controlled morphology and stabilized against agglomeration, can significantly improve reaction yields, reduce energy requirements and enhance the overall sustainability of industrial catalytic processes.
Novel Approaches for Soil Remediation Using Bioremediation Techniques to Restore Agricultural Productivity
Soil degradation caused by industrial pollution, agrochemical overuse and heavy metal accumulation presents a major challenge to global agricultural productivity. Conventional remediation strategies such as excavation, chemical amendments and soil washing are often expensive, disruptive and environmentally hazardous, necessitating the development of sustainable alternatives. Bioremediation has emerged as an effective, ecological and cost-efficient strategy to restore soil quality using microorganisms, plants and enzymatic pathways capable of transforming or immobilizing contaminants. This study investigates novel approaches in bioremediation, including microbial consortia engineering, rhizoremediation optimization, biochar-supported microbial systems and genetically enhanced strains designed to withstand high toxicity levels. The research integrates laboratory experiments, field trials and computational modelling to assess contaminant degradation kinetics, soil health recovery and crop productivity outcomes. Significant improvements were observed in heavy metal immobilization, hydrocarbon degradation and nutrient profile restoration across multiple soil types. Results confirm that advanced bioremediation provides long-term ecological restoration with minimal soil disturbance compared to conventional methods. The study highlights the need for scalable frameworks, continuous microbial-soil interaction monitoring and localized strategies that account for soil composition and climatic influence. Ultimately, the findings demonstrate that novel bioremediation techniques can significantly enhance agricultural sustainability and enable productive reuse of contaminated lands.
Deployment and Validation of Artificial Intelligence-Based Diagnostic Tools for Early Detection of Chronic Diseases
The dramatic rise of chronic diseases worldwide has intensified the need for accurate, rapid and cost-effective diagnostic systems capable of detecting early pathological patterns before the onset of severe symptoms. Artificial Intelligence (AI), particularly machine learning and deep learning methods, has emerged as a transformative approach for analyzing multimodal medical data and providing clinically reliable predictions. This study presents the development and validation of an AI-based diagnostic framework designed to identify early indicators of chronic conditions such as cardiovascular disease, diabetes, chronic obstructive pulmonary disease and early-stage kidney dysfunction. The system integrates heterogeneous data sources including imaging modalities, electronic health records and biochemical markers, and employs hybrid neural architectures and ensemble algorithms for robust prediction. Model training was conducted using publicly available datasets and validated through cross-validation and independent clinical test sets. Experimental results demonstrate high accuracy, sensitivity and specificity across all disease categories, confirming the potential of AI-driven diagnostics as effective decision-support tools. The work highlights major implementation challenges related to data imbalance, model transparency and ethical considerations in clinical adoption. The study concludes that AI-based diagnostics can significantly enhance early detection strategies and can be integrated into healthcare workflows to improve long-term patient outcomes.
Deployment and Testing of Wireless Sensor Networks for Real-Time Monitoring of Environmental Parameters
The rapid increase in environmental variability and climate-related challenges has intensified the need for efficient, scalable, and autonomous monitoring systems. Wireless Sensor Networks (WSNs) provide a promising solution for real-time environmental surveillance due to their distributed architecture, low power consumption, and ability to operate in remote or inaccessible regions. This study presents the design, deployment, and comprehensive performance evaluation of a multi-node WSN used to monitor temperature, humidity, air quality, and soil moisture. The system architecture integrates low-power IEEE 802.15.4 communication, mesh-based routing, and calibrated environmental sensors. Field experiments were conducted over twenty days under varying atmospheric and terrain conditions to analyze packet delivery ratio, latency, throughput, and energy consumption. Results indicate that while the WSN demonstrated stable performance with a packet delivery ratio above 90 percent during optimal conditions, environmental obstructions and rainfall caused noticeable degradation in signal strength and network reliability. The findings align with established studies on the vulnerabilities of WSN communication to environmental interference. Overall, the deployed system exhibited strong potential for long-term environmental monitoring applications, including precision agriculture, pollution analysis, and smart city infrastructures. Future improvements should integrate energy harvesting modules, adaptive routing algorithms, and enhanced self-healing capabilities for extended autonomous operation.
Performance Analysis and Thermal Insulation Characteristics of Innovative Eco-Friendly Building Materials
The urgent demand for sustainable construction practices has encouraged the adoption of eco- friendly building materials with enhanced thermal insulation capabilities. This study investigates the performance characteristics, thermal resistance, energy efficiency benefits, and environmental impact of three emerging green materials: hempcrete, recycled cellulose fiber, and aerogel enhanced composites. The objective is to examine how these materials perform under differential temperature conditions, their conductivity behavior, long-term structural stability, and potential applicability in residential and commercial infrastructures. Experimental evaluations included thermal conductivity measurements, moisture diffusion analysis, and thermal cycling tests to study material degradation and resilience. Results indicate that hempcrete exhibits low thermal conductivity due to its porous structure, recycled cellulose provides stable insulation across varied humidity ranges, and aerogel-based composites outperform conventional insulation due to nanoporous architectures that minimize heat transfer. The findings confirm that eco-friendly insulating materials significantly reduce operational energy consumption, support passive cooling, and contribute to climate-conscious architecture. By comparing functional properties and environmental benefits, this research highlights their potential as competitive alternatives to conventional materials like mineral wool and polystyrene. The conclusions emphasize the importance of integrating these materials into future green building codes to support sustainable urban development.
Investigation of Conductive Inks Based on Silver Nanoparticles for Flexible and Wearable Electronic Devices
The Flexible and wearable electronic devices require conductive materials that combine high electrical performance with mechanical flexibility, stability, and compatibility with diverse substrates. Silver nanoparticle–based conductive inks have emerged as leading candidates due to their exceptional conductivity, tunable particle size, and suitability for low-temperature processing. This study investigates the synthesis, formulation, printing behavior, and performance characteristics of silver nanoparticle conductive inks intended for flexible and wearable applications. The research evaluates the influence of nanoparticle morphology, capping agents, dispersant concentration, and solvent composition on rheology, film formation, and conductivity. Multiple printing techniques, including inkjet printing and screen printing, are analyzed to determine their effect on pattern fidelity and electrical performance. Post-printing sintering processes—thermal, photonic, and chemical—are examined to understand their impact on nanoparticle coalescence and substrate compatibility. Mechanical durability is assessed through bending and cyclic deformation tests, while environmental stability is evaluated under temperature and humidity variations. Results demonstrate that properly engineered silver nanoparticle inks, combined with optimized sintering techniques, achieve low resistivity, strong adhesion, and long-term flexibility suited for wearable sensors, smart textiles, and stretchable circuits. The study provides comprehensive insights into processing– structure–property relationships essential for advancing next generation flexible electronics.