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.
Application of Bioinformatics Tools for Genetic Variant Analysis
The rapid advancement of next-generation sequencing technologies has transformed biological research by enabling large-scale identification of genetic variants associated with diseases, traits, and evolutionary processes. However, the massive volume and complexity of genomic data generated by high- throughput sequencing platforms necessitate sophisticated bioinformatics tools for accurate variant detection, annotation, and interpretation. This paper presents a comprehensive study on the application of bioinformatics tools for genetic variant analysis, focusing on computational pipelines, algorithmic approaches, and practical challenges in genomic data analysis. The study examines widely used tools for sequence alignment, variant calling, functional annotation, and pathogenicity prediction, emphasizing their performance, accuracy, and limitations. Comparative evaluation is conducted using simulated and real- world genomic datasets to assess sensitivity, specificity, computational efficiency, and reproducibility. Results indicate that integrated bioinformatics workflows significantly improve variant detection accuracy and interpretability compared to isolated tool usage. Nevertheless, challenges such as sequencing bias, reference genome limitations, data storage demands, and ethical concerns related to genomic privacy persist. The paper concludes that bioinformatics tools are indispensable for modern genetic variant analysis and highlights future directions for improving scalability, interpretability, and clinical translation.
Copyright (c) 2024 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Optimization of Electric Vehicle Charging Infrastructure Using AI
The rapid global adoption of electric vehicles has introduced significant challenges for existing power distribution networks and urban infrastructure, particularly with respect to the availability, efficiency, and reliability of charging stations. Conventional planning and operation of electric vehicle charging infrastructure rely on static demand estimation and rule-based scheduling, which are inadequate for handling highly dynamic charging behaviors and spatial–temporal demand variations. This paper presents a comprehensive study on the optimization of electric vehicle charging infrastructure using artificial intelligence techniques. The proposed framework integrates machine learning-based demand forecasting, intelligent charging scheduling, and adaptive resource allocation to enhance infrastructure utilization and grid stability. Performance evaluation is conducted through simulation-based urban scenarios focusing on charging latency, station utilization, energy cost, and grid load balancing. Results indicate that AI-driven optimization significantly reduces peak load stress, minimizes charging wait times, and improves overall system efficiency compared to conventional approaches. However, challenges related to data availability, algorithm transparency, cybersecurity, and regulatory integration persist. The study concludes that artificial intelligence is a critical enabler for scalable and sustainable electric vehicle charging infrastructure, provided that technological innovation is aligned with policy support and grid modernization efforts.
Development of Smart Waste Management Frameworks Using Sensor Networks
Rapid urbanization and population growth have placed unprecedented pressure on municipal solid waste management systems, leading to environmental degradation, public health risks, and inefficient resource utilization. Traditional waste management practices rely on fixed collection schedules and manual monitoring, resulting in overflowing bins, unnecessary fuel consumption, and increased operational costs. This paper presents a comprehensive study on the development and evaluation of smart waste management frameworks using sensor networks. The proposed framework integrates heterogeneous sensors, wireless communication technologies, cloud-based data analytics, and intelligent decision-support mechanisms to enable real-time monitoring and optimized waste collection. System performance is evaluated in terms of data accuracy, communication reliability, energy efficiency, scalability, and economic feasibility. Comparative analysis with conventional waste management approaches demonstrates that sensor-enabled systems significantly improve collection efficiency, reduce operational costs, and enhance urban cleanliness. However, challenges related to sensor durability, network reliability, data security, and institutional adoption remain significant barriers to large-scale implementation. The study concludes that smart waste management frameworks represent a critical component of sustainable smart cities, provided that technological innovation is complemented by policy support and stakeholder engagement.
Design of Autonomous Drone Swarms for Search and Rescue Operations
Search and rescue operations in disaster-stricken and hazardous environments demand rapid situational awareness, adaptability, and resilience under uncertain conditions. Conventional ground-based rescue methods and single-drone systems often suffer from limited coverage, communication constraints, and delayed response times. This paper presents an extensive design and evaluation of autonomous drone swarms tailored for search and rescue operations. The proposed framework integrates distributed swarm intelligence, cooperative perception, decentralized communication, and autonomous decision-making to enable scalable and fault-tolerant aerial rescue missions. The study analyzes swarm architectures, control strategies, sensing modalities, and coordination mechanisms suitable for complex environments such as collapsed structures, dense forests, and flood-affected regions. Performance evaluation is conducted through simulation-based scenarios focusing on coverage efficiency, target detection accuracy, communication latency, energy consumption, and system robustness. Results demonstrate that drone swarms significantly outperform single-drone deployments by achieving faster area coverage, higher redundancy, and improved mission reliability. However, challenges related to inter-drone communication, collision avoidance, regulatory compliance, and ethical deployment persist. The paper concludes that autonomous drone swarms represent a transformative advancement in emergency response systems, provided that technical, regulatory, and operational barriers are systematically addressed.
Evaluation of Intelligent Aquaculture Monitoring Systems Using IoT
The accelerating expansion of aquaculture as a primary source of animal protein has intensified the need for intelligent monitoring solutions capable of ensuring productivity, sustainability, and environmental resilience. Conventional aquaculture management relies on periodic manual sampling and visual inspection, which fail to capture rapid fluctuations in water quality and often result in delayed corrective actions. This study presents an extensive evaluation of intelligent aquaculture monitoring systems based on Internet of Things technologies, focusing on architectural design, performance efficiency, operational reliability, scalability, and deployment feasibility. The proposed evaluation framework integrates multi-parameter water quality sensors, low-power wireless communication, cloud-based analytics, and automated alert mechanisms. Comparative experimental analysis with traditional monitoring practices is conducted to assess data accuracy, latency, energy consumption, fault tolerance, and economic viability. Results demonstrate that IoT-enabled systems substantially enhance early anomaly detection, reduce resource wastage, and support predictive aquaculture management. However, challenges such as sensor degradation, connectivity limitations, cybersecurity vulnerabilities, and maintenance costs persist, particularly in resource-constrained regions. The study concludes that intelligent IoT-based aquaculture monitoring systems offer a transformative pathway toward precision aquaculture, provided that technological, economic, and policy-level barriers are systematically addressed.
ASSESSMENT OF MACHINE LEARNING ALGORITHMS FOR FINANCIAL FRAUD DETECTION
The rapid growth of digital financial services has significantly increased the complexity and frequency of financial fraud, posing serious challenges to financial institutions and regulatory bodies. Traditional rule-based fraud detection systems are increasingly ineffective against evolving fraud patterns due to their rigidity and inability to adapt to large-scale, high-dimensional transaction data. This paper presents a comprehensive assessment of machine learning algorithms for financial fraud detection, focusing on their effectiveness, scalability, and practical applicability. Supervised, unsupervised, and ensemble-based machine learning techniques are evaluated using transactional datasets characterized by class imbalance and non-linear patterns. The study analyzes algorithmic performance in terms of accuracy, precision, recall, F1- score, and computational efficiency. Experimental results indicate that ensemble and hybrid models outperform individual classifiers in detecting fraudulent activities while maintaining acceptable false- positive rates. The paper further discusses data preprocessing challenges, feature engineering strategies, and ethical considerations associated with automated fraud detection systems. The findings highlight the importance of adaptive, data-driven models in combating financial fraud and provide insights into the selection of appropriate machine learning techniques for real world deployment.
IMPLEMENTATION OF CLOUD INTEGRATED SCADA SYSTEMS FOR INDUSTRIAL AUTOMATION
Industrial automation systems are undergoing a profound transformation driven by digitalization, large-scale data generation, and the demand for intelligent decision-making. Conventional Supervisory Control and Data Acquisition (SCADA) systems, while highly reliable for localized control, are increasingly limited in their ability to support advanced analytics, remote accessibility, and scalable data management. This paper presents a comprehensive investigation into the implementation of cloud-integrated SCADA systems for industrial automation. A hybrid cloud–edge architecture is proposed in which real-time control functions remain localized, while monitoring, historical data storage, and analytics are migrated to cloud platforms. The system is implemented using industry-standard communication protocols and secure data transmission mechanisms. Performance is evaluated in a simulated industrial environment with emphasis on latency, reliability, scalability, and cybersecurity. Results demonstrate that cloud integration significantly enhances operational visibility and predictive maintenance capability without violating supervisory control constraints when designed appropriately. Security challenges, fault tolerance mechanisms, and practical deployment considerations are examined in detail. The study concludes that cloud-integrated SCADA architectures represent a viable and future-oriented solution for modern industrial automation within the Industry 4.0 paradigm.
DEVELOPMENT OF MEMS-BASED SENSORS FOR BIOMEDICAL APPLICATIONS
Microelectromechanical systems (MEMS) have become a foundational technology in biomedical engineering due to their capability to integrate mechanical structures, sensing elements, and electronic circuitry on a microscale platform. The growing emphasis on continuous health monitoring, early diagnosis, and personalized medical care has significantly increased demand for compact, low-power, and highly sensitive biomedical sensors. This research presents a comprehensive study on the development of MEMS- based sensors for biomedical applications, focusing on both physiological and biochemical sensing. The paper details sensor design principles, microfabrication techniques, material considerations, signal conditioning strategies, and performance evaluation under simulated physiological conditions. A silicon- based piezoresistive MEMS pressure sensor and a microcantilever-based biosensor are developed as representative case studies. Experimental results demonstrate high sensitivity, stable linear response, and acceptable repeatability within clinically relevant operating ranges. Challenges related to biocompatibility, biofouling, packaging, and long-term reliability are critically examined. The study concludes that MEMS- based biomedical sensors offer substantial advantages over conventional sensing technologies and represent a key enabling technology for next-generation wearable, implantable, and point-of-care medical devices.
Application of Additive Manufacturing for Rapid Prototyping in Engineering
Additive manufacturing has emerged as a transformative technology in modern engineering design and production, particularly in the domain of rapid prototyping. Unlike conventional subtractive manufacturing processes, additive manufacturing enables the fabrication of complex geometries directly from digital models with minimal material waste and reduced lead times. This paper presents a comprehensive, journal-ready analysis of the application of additive manufacturing for rapid prototyping in engineering. It examines the fundamental principles of additive manufacturing technologies, their evolution, and their growing role in accelerating product development cycles. Various additive manufacturing techniques, including fused deposition modeling, stereolithography, selective laser sintering, and metal- based processes, are discussed with respect to material compatibility, accuracy, surface finish, and mechanical performance. The paper further explores design freedom, cost efficiency, and iterative testing as key advantages of rapid prototyping through additive manufacturing. Challenges related to material limitations, dimensional accuracy, and scalability are critically analyzed. Drawing on recent research findings, the study highlights the increasing integration of additive manufacturing with digital design tools and simulation frameworks. The paper concludes by emphasizing the strategic importance of additive manufacturing in engineering innovation and its future potential in bridging the gap between conceptual design and full-scale production.
Development of AI-Based Anomaly Detection Systems for Network Security
The exponential growth of digital networks and cloud-based infrastructures has significantly increased the complexity and frequency of cyber threats. Traditional signature-based intrusion detection systems are increasingly inadequate in identifying novel, sophisticated, and zero-day attacks that deviate from known patterns. Artificial intelligence-based anomaly detection systems have emerged as a powerful alternative, capable of learning normal network behavior and identifying deviations that may indicate malicious activity. This paper presents a comprehensive, journal-ready study on the development of AI- based anomaly detection systems for network security. It examines the evolution of network threats, the limitations of conventional detection mechanisms, and the role of machine learning and deep learning models in detecting anomalous traffic patterns. Supervised, unsupervised, and hybrid learning approaches are analyzed with respect to accuracy, scalability, and adaptability to dynamic network environments. The paper also discusses feature engineering, dataset challenges, evaluation metrics, and deployment considerations in real-world networks. Experimental findings reported in recent literature demonstrate that AI-driven anomaly detection significantly improves detection rates while reducing false positives. The study concludes by outlining future research directions focused on explainable AI, federated learning, and real- time adaptive security frameworks to enhance trust and robustness in intelligent network defense systems.
Evaluation of Intelligent Fleet Management Using Telematics Data
The rapid digitization of transportation systems has accelerated the adoption of intelligent fleet management solutions driven by vehicle telematics data. Telematics platforms integrate sensors, global positioning systems, onboard diagnostics, and wireless communication to generate continuous streams of data related to vehicle location, driver behavior, fuel consumption, and mechanical health. This paper presents a journal-ready, long-form evaluation of intelligent fleet management systems that leverage telematics data to improve operational efficiency, safety, and sustainability. The study examines the architecture of modern telematics-based fleet management systems, data acquisition mechanisms, analytics techniques, and decision-support applications. Emphasis is placed on performance metrics such as fuel efficiency, maintenance optimization, route planning, and driver. behavior assessment. Drawing on empirical findings reported in recent literature and industry deployments, the paper evaluates the impact of telematics-driven intelligence on cost reduction and environmental performance. Challenges related to data quality, scalability, cybersecurity, and privacy are critically discussed. The paper concludes by identifying future research directions, including predictive analytics, integration with intelligent transportation systems, and the use of artificial intelligence to enable autonomous and adaptive fleet operations.
Design of Smart Irrigation Systems Using Real Time Soil Analytics
Agriculture accounts for a major share of global freshwater consumption, and inefficient irrigation practices significantly contribute to water scarcity and reduced crop productivity. Smart irrigation systems based on real-time soil analytics offer a sustainable solution by aligning water application with actual crop and soil requirements. This paper presents a comprehensive, journal-ready study on the design and implementation of smart irrigation systems that integrate soil sensing technologies, wireless communication, data analytics, and automated control mechanisms. The study examines the role of soil moisture, temperature, electrical conductivity, and nutrient sensing in determining irrigation schedules, and evaluates how real-time data processing improves decision-making compared to conventional time-based irrigation. A layered system architecture is proposed, comprising in-field sensor nodes, edge-level processing units, cloud-based analytics, and actuator-driven irrigation control. Findings from recent field experiments and simulation studies reported in the literature indicate water savings of up to 40 percent while maintaining or improving crop yields. Challenges such as sensor calibration, data reliability, energy efficiency, and adoption barriers for small-scale farmers are critically discussed. The paper concludes that real-time soil analytics, when combined with intelligent control strategies, can play a pivotal role in sustainable agriculture and climate-resilient food production.
Application of Robotics in Hazardous Environment Inspection
Inspection of hazardous environments such as nuclear facilities, chemical plants, underground mines, disaster-struck buildings, and offshore infrastructure poses severe risks to human operators. Robotics has emerged as a reliable and increasingly indispensable solution for minimizing human exposure while enhancing inspection accuracy, repeatability, and operational efficiency. This paper presents a comprehensive, journal-ready analysis of the application of robotic systems in hazardous environment inspection. It examines the evolution of inspection robotics, including ground robots, aerial drones, underwater vehicles, and hybrid platforms, with a focus on sensing technologies, autonomy levels, communication architectures, and human–robot interaction. The study integrates findings from recent experimental deployments and simulation-based research to evaluate system performance under adverse conditions such as low visibility, radiation exposure, toxic gases, and unstable terrain. Particular emphasis is placed on shared autonomy frameworks that balance human decision-making with machine intelligence to ensure safety and reliability. Challenges related to perception degradation, power limitations, communication failures, and regulatory certification are critically discussed. The paper concludes by identifying future research directions, including resilient multi-robot systems, AI-driven anomaly detection, and standardized benchmarking protocols, which are essential for transitioning inspection robotics from experimental deployments to routine industrial use.
Development of High-Efficiency Photovoltaic Materials Using Nanostructures
The continuous growth in global energy demand and the urgency to reduce carbon emissions have intensified research into high-efficiency photovoltaic materials. Conventional photovoltaic technologies, while commercially mature, face intrinsic efficiency limits due to optical losses, carrier recombination, and material constraints. Nanostructured photovoltaic materials have emerged as a promising pathway to overcome these limitations by enabling precise control over light–matter interactions, charge transport, and interface engineering at the nanoscale. This paper presents a comprehensive, journal- ready investigation into the development of high-efficiency photovoltaic materials using nanostructures, focusing on silicon-based, perovskite, and hybrid photovoltaic systems. The study integrates theoretical insights, experimental advances reported in recent literature, and conceptual modeling to examine how nanostructures such as quantum dots, nanowires, plasmonic nanoparticles, and dielectric nanophotonic architectures enhance light absorption, reduce recombination losses, and improve charge extraction. Particular emphasis is placed on nanostructured perovskite–silicon tandem solar cells, where nanoscale interface engineering and light-trapping strategies have demonstrated significant efficiency gains. Challenges related to material stability, large-area fabrication, and cost-effective scalability are critically discussed. The paper concludes by outlining future research directions necessary for translating nanostructured photovoltaic concepts from laboratory-scale demonstrations to industrially viable solar technologies.
Development of Green Hydrogen Production Technologies Using Advanced Catalysts
The transition to sustainable energy sources has intensified research on green hydrogen production as a clean and renewable fuel alternative. Green hydrogen, generated from water electrolysis powered by renewable energy, offers zero carbon emissions and supports decarbonization across industrial, transportation, and power sectors. This paper investigates the development of advanced catalysts for enhancing efficiency, selectivity, and durability in hydrogen evolution reactions. Various catalytic materials, including transition metal alloys, perovskites, nanostructured platinum-group metals, and earth-abundant metal oxides, were evaluated for performance in proton-exchange membrane and alkaline electrolysis systems. Experimental studies coupled with computational modeling examined reaction kinetics, overpotential reduction, and stability under operational conditions. Results demonstrate that optimized catalyst structures can significantly reduce energy consumption, increase hydrogen production rates, and maintain long-term operational stability. The paper further discusses the integration of catalyst technologies with solar and wind-driven electrolysis, exploring techno-economic feasibility, scalability, and environmental impact. Challenges related to cost, material availability, and electrode degradation are addressed, highlighting research pathways for industrial adoption. The findings underscore the critical role of advanced catalysts in advancing green hydrogen technologies, offering a pathway toward sustainable energy solutions and carbon-neutral industrial processes.
Integration of Quantum-Safe Cryptographic Techniques in IoT Networks
The rapid proliferation of Internet of Things (IoT) devices has revolutionized digital communication, yet it simultaneously poses unprecedented security challenges due to constrained device capabilities and increasing cyber threats. Conventional cryptographic techniques, while currently effective, are vulnerable to emerging quantum computing technologies that threaten to render classical encryption obsolete. This paper explores the integration of quantum-safe cryptographic techniques within IoT networks to ensure long-term confidentiality, integrity, and authentication. It examines post-quantum cryptographic primitives, including lattice-based, hash-based, multivariate, and code-based schemes, and evaluates their suitability for resource-constrained IoT environments. The study focuses on lightweight implementation strategies, balancing computational efficiency with robust security requirements. Simulation analyses demonstrate that properly optimized quantum safe protocols can provide resilient encryption, key exchange, and digital signature functionalities without overwhelming device resources. Furthermore, the integration of these techniques with IoT communication standards and secure network protocols ensures end-to-end protection across heterogeneous networks. Challenges such as latency, energy consumption, and scalability are addressed, highlighting the necessity of tailored cryptographic solutions for diverse IoT applications. The findings confirm that quantum safe cryptography is essential for future-proofing IoT networks, offering sustainable security measures that remain resilient against both classical and quantum adversaries, thereby supporting the continued expansion of secure and intelligent interconnected systems.
Evaluation of Intelligent Water Treatment Systems Using AI
The rising complexity of water treatment infrastructure, driven by increasing population, industrial effluents, and stringent water quality standards, necessitates intelligent monitoring and operational systems. Traditional water treatment facilities rely on static, rule-based control systems that often fail to adapt to dynamic water quality variations and energy efficiency requirements. This paper evaluates the role of artificial intelligence (AI) in designing intelligent water treatment systems capable of adaptive control, predictive maintenance, and real-time optimization. The study examines AI methodologies such as machine learning, deep neural networks, and reinforcement learning in modeling water treatment processes, including coagulation, sedimentation, filtration, and disinfection. Through simulation-based assessments, AI-driven systems demonstrated improved removal efficiencies, reduced chemical usage, optimized energy consumption, and early anomaly detection. Furthermore, the integration of Internet of Things (IoT) sensors with AI algorithms enabled continuous monitoring and feedback, facilitating proactive interventions and minimizing operational downtime. The findings suggest that AI not only enhances operational efficiency but also supports sustainable water management practices. Challenges related to data quality, algorithm interpretability, and system integration are also discussed, providing a roadmap for implementing intelligent water treatment solutions that meet the growing demands of urban and industrial water systems.
Application of Smart Materials in Structural Health Monitoring
The increasing demand for reliable and long lasting infrastructure has amplified global interest in structural health monitoring (SHM) systems powered by smart materials. These materials, which exhibit dynamic responses to environmental and mechanical stimuli, have introduced a transformative shift from reactive maintenance to continuous, real-time structural assessment. This paper provides a detailed examination of smart material integration in SHM, focusing on piezoelectric ceramics, shape-memory alloys, fiber Bragg gratings, and magnetostrictive components. The study evaluates their sensing mechanisms, signal interpretation characteristics, and compatibility with reinforced concrete, steel, and composite structures. Through simulation and experimental insights, the paper demonstrates how smart materials significantly enhance early damage detection, enabling engineers to identify micro-cracks, delamination, corrosion onset, and strain anomalies before they escalate into critical failures. Emerging trends such as self- sensing composites and embedded multifunctional smart layers are also discussed to illustrate the evolution of intelligent infrastructure strategies. The findings emphasize that smart materials, when combined with data-driven analytics and distributed sensing frameworks, provide an indispensable foundation for next- generation civil engineering practices by ensuring operational safety, extending structural service life, and reducing maintenance uncertainties.
Analysis of Renewable Energy Forecasting Using Machine Learning Techniques
The accelerating shift toward renewable energy systems has heightened the necessity for accurate forecasting models capable of managing the inherent variability in wind, solar and hybrid energy sources. Machine learning (ML) techniques have emerged as powerful tools for predicting renewable energy generation due to their ability to model nonlinear dynamics, adapt to evolving patterns and exploit large datasets. This study provides a comprehensive analytical assessment of ML-based forecasting approaches for renewable energy systems, covering linear regression, support vector regression, random forests, long short-term memory networks and hybrid optimization frameworks. Emphasis is placed on model performance, data preprocessing strategies, feature engineering requirements and temporal resolution considerations. The paper examines how uncertainty in meteorological variables, such as irradiance, temperature and wind speed, affects prediction accuracy and demonstrates how ML algorithms can alleviate these fluctuations through robust training and error correction schemes. Additionally, the study discusses the integration of ML forecasting tools into smart grids, illustrating their contribution to demand–supply balancing, storage scheduling and economic dispatch. The findings suggest that ML driven forecasting significantly enhances grid reliability and operational efficiency, although challenges persist concerning data scarcity, model generalization, explainability and real-time deployment. Future prospects include federated learning, physics-informed networks and multimodal forecasting platforms.
Application of Biosensors for Real-Time Disease Detection
The rapid global spread of infectious and chronic diseases has highlighted the urgent need for diagnostic technologies capable of delivering accurate, real-time data at the point of care. Biosensors, which integrate biological recognition systems with physicochemical transducers, have therefore emerged as indispensable tools in modern biomedicine. This paper offers a comprehensive examination of biosensor platforms used for real-time disease detection, emphasizing their operational mechanisms, technological evolution and clinical applications. Recent advances in nanomaterials, microfluidic engineering and synthetic biology have substantially enhanced biosensor sensitivity, selectivity and miniaturization, allowing detection of biomarkers at femtomolar concentrations. Wearable and implantable biosensors further extend diagnostic capabilities by enabling continuous physiological monitoring. The increasing integration of biosensors with wireless communication technologies and AI-driven analytics has opened new possibilities for personalized medicine and remote healthcare. Despite these advances, challenges persist regarding stability, biocompatibility, data security and regulatory approval. This study synthesizes state-of-the-art literature while identifying future directions such as multiplexed biosensing, bioelectronic medicine and digital-twin-assisted diagnostics. The analysis concludes that biosensors will continue to redefine medical diagnostics by enabling proactive, data-driven and precision-oriented healthcare systems.
Development of Hybrid Solar-Thermal Energy Harvesting Technologies
Hybrid solar-thermal energy harvesting technologies combine photovoltaic (PV) and solar thermal systems to optimize the utilization of solar energy for electricity generation and heat applications. Traditional photovoltaic modules convert a portion of incident solar radiation into electricity, with substantial energy lost as heat. Integrating thermal collectors with PV systems allows simultaneous electrical and thermal energy harvesting, improving overall system efficiency. This paper examines the design, development, and performance evaluation of hybrid solar thermal energy systems. The study explores material selection, thermal management techniques, and energy conversion optimization strategies. Simulation and experimental prototypes were developed to assess electrical output, thermal efficiency, and energy yield under varying irradiance and temperature conditions. Results demonstrate that hybrid systems can achieve up to 45–60% overall energy utilization, significantly surpassing standalone PV modules. The integration of phase-change materials (PCMs) and heat exchangers enhances thermal regulation and maintains optimal PV efficiency. The paper further discusses challenges such as cost effectiveness, scalability, system integration, and long-term durability. Hybrid solar-thermal systems represent a viable pathway toward sustainable energy generation, offering both electricity and heat for industrial, residential, and agricultural applications. Their deployment contributes to national renewable energy targets and reduces reliance on fossil fuels, advancing global energy sustainability objectives.
Copyright (c) 2023 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Evaluation of AR/VR Platforms for ImmersiveSkill-Based Training
Immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) have transformed contemporary training systems by enabling simulated, interactive, and controlled learning environments. Their application in skill based training has expanded across fields including healthcare, industrial safety, engineering, aviation, military operations, and vocational education. This study evaluates the pedagogical, technological, and usability dimensions of AR/VR platforms to determine their effectiveness in enhancing skill acquisition, learner engagement, and performance accuracy. The research synthesizes recent empirical findings and theoretical frameworks to analyze immersion, presence, cognitive load, haptic integration, motion tracking, and adaptability across leading AR/VR systems. Results from existing studies indicate that VR-based simulations significantly improve complex motor skills, while AR environments enhance contextual understanding and real-time decision-making. Despite these advantages, challenges such as cybersickness, high hardware costs, limited content scalability, and technological constraints persist. The study concludes that AR/VR platforms offer substantial benefits for experiential learning, yet require advancements in ergonomics, accessibility, and multimodal sensory integration to achieve widespread adoption. Comprehensive evaluation criteria are proposed to guide educators, developers, and institutions in selecting suitable immersive training systems aligned with specific skill requirements and learner profiles.
Design of Smart Building Energy Management Systems Using IoT
The global transition toward sustainable and energy-efficient built environments has intensified research in smart building energy management systems (BEMS) that leverage Internet of Things (IoT) technologies for real-time monitoring and optimized consumption. Traditional building energy systems rely on static schedules and manual operations, which are insufficient for modern dynamic occupancy patterns, variable environmental conditions, and the growing integration of distributed renewable sources. This study presents the design, modeling, and evaluation of an IoT-enabled smart BEMS incorporating distributed sensor networks, intelligent control algorithms, and cloud-based data analytics. A modular architecture is developed consisting of environmental sensors, smart meters, actuators, energy optimization algorithms, and a user interface accessed through edge-cloud integration. Experimental evaluation is conducted on a simulated commercial building using realistic occupancy models and appliance level energy profiles. Results indicate that the proposed IoT-BEMS achieves a reduction of 22 to 35 percent in overall energy consumption compared to legacy systems through optimized HVAC operation, adaptive lighting control, and predictive load scheduling. Furthermore, the architecture demonstrates significant improvements in user comfort, system responsiveness, and energy forecasting accuracy. The study highlights IoT-enabled BEMS as a crucial pathway for implementing national and global sustainability goals and provides a foundation for scalable smart infrastructure in urban environments.
Application of Reinforcement Learning in Adaptive Traffic Management
Urban mobility systems are increasingly strained by rising population densities, unpredictable traffic fluctuations, and limitations in traditional fixed time signal control methods. Reinforcement Learning (RL) has emerged as a promising paradigm capable of enabling traffic signals to learn adaptive strategies based on real-time feedback. This study investigates the design, implementation, and evaluation of an RL- based adaptive traffic management system integrating a Deep Q-Network (DQN) agent trained to optimize signal timings under dynamic vehicular loads. A simulated environment was constructed using SUMO (Simulation of Urban Mobility) to replicate multi-lane intersections, stochastic vehicle arrivals, lane constraints, and peak-hour surges. The agent’s reward structure was formulated around minimizing queue lengths, reducing average waiting time, and enhancing traffic throughput. Comparative evaluation with fixed-time and actuated control systems revealed substantial improvements: average waiting time decreased by 34 percent, queue lengths reduced by 28 percent, and intersection utilization increased by 22 percent. Real-world data from Bengaluru and Pune traffic profiles were integrated to enhance environmental realism. Findings indicate that RL based adaptive systems can outperform conventional controllers and provide scalable, city wide traffic-management optimization when integrated with sensor networks and vehicular communication infrastructures.
Development of Autonomous NavigationAlgorithms for Service Robots
The increasing deployment of service robots in healthcare, hospitality, logistics, and domestic environments requires robust autonomous navigation capabilities. Traditional navigation approaches often fail to deliver sufficient adaptability to dynamic and unstructured spaces, creating the need for new algorithmic frameworks capable of real-time decision-making. This study presents a comprehensive development and evaluation of a hybrid navigation model combining probabilistic mapping, deep-learning- based perception, and behavior-based motion planning. The proposed system integrates an enhanced Simultaneous Localization and Mapping (SLAM) module with a convolutional neural network for obstacle detection and a hierarchical reinforcement learning structure for path optimization. Experiments were conducted using a TurtleBot3 platform, simulated environments in Gazebo, and real-world corridor and room layouts. Results demonstrate improved localization accuracy, reduced collision rates, and higher trajectory smoothness compared to conventional and basic SLAM frameworks. Findings indicate that hybrid navigation systems offer significant reliability in environments characterized by moving humans, changing object positions, and variable lighting conditions. This research contributes to scalable algorithmic designs for next-generation service robots intended for human-centric tasks.
Application of Nanomaterials for HighPerformance Energy Storage Devices
The increasing demand for high-performance energy storage devices necessitates the development of advanced materials that can provide superior capacity, stability, and charge discharge efficiency. Nanomaterials have emerged as a key enabler for next-generation batteries, supercapacitors, and hybrid energy storage systems due to their high surface area, tunable physicochemical properties, and enhanced electrochemical performance. This paper explores the application of nanomaterials in energy storage devices, focusing on electrode design, nanostructure engineering, and material synthesis techniques. A comprehensive methodology integrating nanomaterial fabrication, characterization, and electrochemical testing is presented. Results indicate that nanostructured electrodes exhibit improved charge storage, faster ion diffusion, and prolonged cycle life compared to conventional materials. The study also highlights challenges including scalability, cost, and environmental impact, while discussing strategies for commercial implementation. The integration of nanomaterials into energy storage devices is shown to significantly enhance device performance, efficiency, and durability, supporting sustainable energy technologies and applications in portable electronics, electric vehicles, and renewable energy systems.
Optimization of Waste-to-Energy ConversionUsing Advanced Bioprocessing
The growing global demand for sustainable energy solutions has highlighted the potential of waste-to-energy (WtE) conversion technologies as a means of reducing environmental pollution while generating renewable energy. Advanced bioprocessing approaches, including anaerobic digestion, microbial fermentation, and enzymatic hydrolysis, offer opportunities to optimize the conversion efficiency of municipal, agricultural, and industrial waste streams. This paper presents an assessment of various bioprocessing techniques for WtE conversion, focusing on process optimization, microbial community engineering, and system design. A systematic methodology integrating waste characterization, reactor design, process parameter optimization, and energy yield assessment is employed. Results demonstrate that process enhancements, such as co digestion, bioaugmentation, and reactor optimization, significantly improve methane production, energy recovery, and operational stability. The study highlights the utility of advanced bioprocessing in sustainable energy production, identifies key challenges, and outlines future directions for scalable, economically viable WtE systems.
Assessment of 5G-Enabled CommunicationSystems for Industrial IoT
The advent of 5G communication technology is poised to revolutionize Industrial Internet of Things (IIoT) applications by providing ultra-reliable, low-latency, and high-bandwidth connectivity. This paper assesses the integration of 5G-enabled communication systems within industrial IoT networks, focusing on performance metrics, system architecture, and practical deployment considerations. The study presents a comprehensive methodology that includes network simulation, latency and throughput analysis, reliability testing, and security assessment. Results indicate significant improvements in real-time data transfer, device synchronization, and operational efficiency in smart factories and automated industrial environments. Additionally, the paper evaluates potential challenges, including spectrum allocation, network densification, interoperability with legacy systems, and cybersecurity threats. The assessment demonstrates that 5G integration can substantially enhance industrial automation, predictive maintenance, and remote monitoring, while also providing a scalable and future-proof communication infrastructure for IIoT ecosystems.
Integration of Blockchain Solutions for SecureHealthcare Data Exchange
Healthcare data management faces persistent challenges, including data breaches, unauthorized access, and fragmented storage across multiple institutions. Blockchain technology provides a decentralized, tamper-proof, and transparent framework for secure data exchange in healthcare systems. This paper explores the integration of blockchain solutions to facilitate secure, interoperable, and privacy-preserving healthcare data exchange. The study proposes a blockchain-based architecture that combines smart contracts, distributed ledgers, and cryptographic mechanisms to ensure data integrity, patient consent management, and controlled access. The methodology involves deploying permissioned blockchain networks, evaluating data throughput, latency, and scalability, and integrating interoperability protocols with existing healthcare information systems. Results demonstrate enhanced data security, reduced risk of breaches, and improved efficiency in cross-institutional data sharing. Additionally, the framework supports auditability, accountability, and compliance with regulatory requirements. This research highlights the transformative potential of blockchain in creating resilient, secure, and efficient healthcare data ecosystems.
Evaluation of Cyber-Physical Systems for SmartManufacturing
The advent of Industry 4.0 has accelerated the integration of Cyber-Physical Systems (CPS) into manufacturing environments, enabling real-time monitoring, predictive maintenance, and process optimization. CPS integrates computational algorithms, embedded sensors, communication networks, and physical manufacturing processes to create intelligent production systems capable of self-organization and adaptive control. This paper presents a comprehensive evaluation of CPS applications in smart manufacturing, focusing on system architecture, performance metrics, and operational benefits. The study emphasizes key CPS components, including IoT-enabled machinery, edge and cloud computing, data analytics, and human machine interfaces, to enhance manufacturing efficiency, flexibility, and safety. Case studies and pilot simulations demonstrate reductions in equipment downtime, improved production throughput, and enhanced product quality. The framework also addresses challenges such as cybersecurity, interoperability, and scalability. Results indicate that CPS adoption in manufacturing offers significant economic and operational advantages, providing a foundation for fully autonomous, resilient, and intelligent production systems.