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.
Design, Characterization, and Functional Performance of Adaptive Engineered Surfaces for Catalytic and Applied Material Systems
Adaptive engineered surfaces have emerged as a critical class of functional materials with the capacity to dynamically respond to environmental, chemical, and mechanical stimuli. These surfaces play a pivotal role in catalytic systems, energy devices, environmental remediation technologies, and advanced manufacturing applications. This research paper presents an in-depth investigation into the design principles, material architectures, characterization methodologies, and functional performance of adaptive engineered surfaces in applied material systems. Emphasis is placed on surface reactivity modulation, tunable wettability, self-regenerating catalytic interfaces, and environmentally responsive surface morphologies. Advanced surface engineering techniques, including nano-patterning, thin-film deposition, and hybrid material integration, are examined in relation to their influence on catalytic efficiency, durability, and multifunctionality. Comprehensive characterization approaches spanning structural, chemical, and functional performance analyses are discussed to establish correlations between surface design and operational behavior. The study further evaluates the role of adaptive surfaces in enhancing catalytic selectivity, reducing material degradation, and improving long-term system sustainability. Key challenges related to scalability, surface stability, and real-world deployment are critically assessed. The findings demonstrate that adaptive engineered surfaces represent a transformative pathway for next-generation catalytic and applied material systems, enabling higher efficiency, resilience, and sustainability across industrial and environmental applications.
Copyright (c) 2026 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Development and Evaluation of Human-Centered Intelligent Systems for Real-World Decision Support in Complex Applied Environments
The increasing complexity of modern socio-technical systems has intensified the demand for intelligent decision-support solutions capable of operating effectively in dynamic, uncertain, and high-stakes environments. Human-centered intelligent systems (HCIS) represent a critical evolution in applied artificial intelligence, emphasizing collaboration between computational intelligence and human cognition rather than full automation. This paper presents a comprehensive investigation into the development, evaluation, and deployment of human-centered intelligent systems for real-world decision support across complex applied environments such as healthcare, infrastructure management, industrial operations, and public policy planning. The study examines theoretical foundations, system architectures, interaction design principles, and performance evaluation methodologies that ensure transparency, adaptability, and trustworthiness. Particular emphasis is placed on integrating explainable artificial intelligence, cognitive ergonomics, and contextual awareness to enhance human decision-making rather than replace it. Through analytical synthesis of existing implementations and applied system models, the paper demonstrates how human-centered intelligence improves decision quality, reduces cognitive overload, and increases system resilience. Key challenges related to bias, ethical accountability, scalability, and real-world validation are critically discussed. The findings establish human-centered intelligent systems as a foundational paradigm for sustainable and responsible deployment of intelligent technologies in complex applied environments.
Advanced Carbon Conversion and Utilization Technologies for Sustainable Industrial Chemical Production
The mitigation of anthropogenic carbon dioxide emissions has become a critical priority for sustainable industrial development. Carbon conversion and utilization (CCU) technologies offer a promising pathway to transform CO₂ from a waste byproduct into valuable chemical feedstocks, fuels, and materials, thereby closing the carbon loop. This paper presents a comprehensive analysis of advanced CCU technologies with a focus on their integration into sustainable industrial chemical production systems. Thermochemical, electrochemical, photochemical, and biological conversion pathways are examined in terms of reaction mechanisms, catalyst design, energy efficiency, scalability, and techno-economic feasibility. Particular emphasis is placed on emerging catalytic systems, renewable energy coupling, and process intensification strategies that enhance conversion efficiency while reducing environmental impact. Life-cycle assessment perspectives are incorporated to evaluate the sustainability performance of CCU- enabled chemical processes. Key challenges, including high energy demand, catalyst degradation, and infrastructure compatibility, are critically discussed. The study demonstrates that advanced CCU technologies, when strategically integrated with renewable energy sources and industrial symbiosis frameworks, can significantly reduce carbon footprints while enabling circular chemical manufacturing. The findings provide valuable insights for researchers and industry stakeholders seeking scalable and economically viable pathways for carbon-neutral chemical production.
Artificial Intelligence–Driven Frameworks for Accelerated Materials Discovery in Energy and Sustainability Applications
The discovery and optimization of advanced materials for energy and sustainability applications have traditionally relied on time-intensive experimental and computational workflows. Recent advances in artificial intelligence (AI), particularly machine learning and data-driven modeling, have enabled a paradigm shift toward accelerated materials discovery. This paper presents a comprehensive AI-driven framework designed to streamline the identification, prediction, and optimization of functional materials for energy storage, conversion, and sustainable infrastructure applications. The proposed framework integrates materials databases, feature engineering, supervised and unsupervised learning algorithms, and iterative validation loops to reduce experimental cost and discovery time. Case-oriented analyses illustrate how AI models can predict key material properties such as bandgap, thermal stability, catalytic activity, and mechanical performance with high accuracy. Furthermore, the study highlights the role of explainable AI in enhancing model transparency and guiding experimental decision-making. Challenges related to data scarcity, model generalization, and integration with laboratory workflows are critically discussed. The findings demonstrate that AI-driven materials discovery not only accelerates innovation cycles but also supports sustainable development goals by enabling the rapid deployment of eco-friendly and high- performance materials. This research contributes a scalable and adaptable framework suitable for next-generation energy and sustainability-oriented materials research.
Development of Self-Healing Ultra-High- Performance Concrete Using Smart Nanocapsules for Enhanced Structural Resilience
Ultra-High-Performance Concrete (UHPC) represents a significant advancement in construction materials due to its exceptional strength, durability, and low permeability. However, its dense microstructure and low strain capacity result in microcrack formation under mechanical loading, thermal gradients, and shrinkage effects, which may compromise long-term structural performance. This study presents the development of a smart self-healing UHPC system incorporating polymer-filled nanocapsules designed to autonomously repair microcracks and enhance structural resilience. Nanocapsules with controlled size distribution were synthesized and embedded within the UHPC matrix at varying concentrations. Mechanical performance, crack-healing efficiency, microstructural evolution, and durability characteristics were systematically evaluated. Experimental results demonstrate that self-healing UHPC achieves up to 85% recovery in flexural strength after crack formation and exhibits significantly reduced permeability and chloride ion penetration compared to conventional UHPC. Microstructural analyses confirm effective crack sealing through polymerization and secondary hydration reactions. The findings indicate that nanocapsule- enabled UHPC offers a promising pathway toward sustainable, low-maintenance infrastructure capable of prolonged service life under aggressive environmental conditions.
AI-Enabled Multisensor Data Fusion Framework for Real-Time Structural Health Monitoring and Predictive Maintenance of Civil Infrastructure
Aging civil infrastructure systems face increasing risks due to material degradation, excessive loading, and environmental exposure, necessitating reliable real-time monitoring and proactive maintenance strategies. Conventional inspection-based structural health monitoring approaches are limited by subjectivity, low inspection frequency, and delayed damage identification. This study proposes an artificial intelligence– enabled multisensor data fusion framework for real-time structural health monitoring and predictive maintenance of civil infrastructure. The framework integrates heterogeneous data acquired from accelerometers, strain gauges, acoustic emission sensors, and environmental sensors to enhance damage detection accuracy and robustness. Advanced machine learning models are employed for feature extraction, damage classification, and remaining useful life prediction under varying operational conditions. The proposed methodology is validated through numerical simulations and experimental investigations on reinforced concrete structural components subjected to progressive damage. Results demonstrate that multisensor data fusion significantly improves damage localization accuracy and early-stage crack detection compared to single-sensor systems. The predictive maintenance module successfully forecasts structural degradation trends, enabling condition-based maintenance planning. The findings indicate that AI-enabled multisensor monitoring systems provide a scalable and reliable solution for intelligent infrastructure management, supporting safety, resilience, and sustainability objectives in modern urban environments.
Development of Smart Functional Materials for Wearable Sensors and Human–Machine Interfaces
The rapid advancement of wearable electronics and human–machine interfaces has driven significant interest in the development of smart functional materials capable of sensing, actuation, and signal transduction. These materials form the core of next-generation wearable sensors that enable continuous health monitoring, motion tracking, and intuitive human–machine interaction. This paper presents a comprehensive study on the design, properties, and applications of smart functional materials for wearable sensor technologies. Key material classes including conductive polymers, piezoelectric ceramics, stretchable nanocomposites, and bio-compatible hydrogels are examined with respect to their mechanical flexibility, electrical performance, and environmental stability. The integration of these materials into wearable sensor architectures and interface systems is analyzed, highlighting fabrication techniques and performance optimization strategies. Challenges related to durability, signal reliability, power consumption, and user comfort are critically discussed. The study demonstrates that smart functional materials play a pivotal role in advancing wearable technologies toward seamless, adaptive, and user-centric human–machine interfaces. Future research directions focusing on material intelligence, self-healing capability, and sustainable design are also outlined.
Copyright (c) 2025 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Remote Sensing and Geospatial Analytics for Climate Change Assessment and Natural Resource Management
Climate change poses unprecedented challenges to ecosystems, water resources, agriculture, and human settlements. Effective assessment and sustainable management of natural resources require continuous, large-scale, and high-resolution environmental monitoring. Remote sensing combined with geospatial analytics provides a powerful framework for observing climate-induced changes across temporal and spatial scales. This paper presents an integrated analysis of satellite-based remote sensing data and geographic information system (GIS) techniques for climate change assessment and natural resource management. The study examines the use of multispectral and hyperspectral imagery, thermal sensing, and radar data for monitoring land use change, vegetation dynamics, water availability, and climate extremes. Advanced geospatial analytics, including machine learning–based classification and trend analysis, are discussed as tools for enhancing predictive capability. The paper highlights applications in drought assessment, deforestation monitoring, coastal vulnerability analysis, and sustainable land management. Challenges related to data uncertainty, resolution limitations, and integration with socio-economic data are critically analyzed. The findings underscore the role of remote sensing and geospatial analytics as essential decision-support systems for climate adaptation and resource governance in a changing global environment.
Application of Digital Twin Technology in Manufacturing and Infrastructure Lifecycle Management
Digital twin technology has emerged as a transformative paradigm for managing complex manufacturing systems and large-scale infrastructure assets throughout their lifecycle. By creating a dynamic virtual representation of physical systems, digital twins enable continuous monitoring, predictive analysis, and informed decision-making across design, operation, and maintenance phases. This paper presents a comprehensive examination of digital twin applications in manufacturing and infrastructure lifecycle management, emphasizing system architecture, data integration, and analytics-driven optimization. The study explores how real-time sensor data, physics-based models, and data-driven intelligence are synchronized to create adaptive and evolving digital replicas. Case-oriented analysis highlights the role of digital twins in predictive maintenance, performance optimization, and risk mitigation. Additionally, the paper discusses challenges related to data interoperability, model fidelity, scalability, and cybersecurity. The findings demonstrate that digital twin–enabled lifecycle management significantly improves operational efficiency, asset reliability, and cost-effectiveness. The paper concludes by identifying future research directions aimed at intelligent automation, standardization, and large-scale deployment of digital twin systems in industrial and infrastructure domains.
Renewable Energy–Integrated Smart Grids: Control Strategies, Cybersecurity Challenges, and System Reliability
The large-scale integration of renewable energy sources into electrical power systems has fundamentally transformed the operational dynamics of modern power grids. While renewable energy technologies contribute to decarbonization and sustainability goals, their intermittent and distributed nature introduces significant challenges related to grid stability, control, and reliability. Smart grid architectures have emerged as a critical solution by incorporating advanced sensing, communication, and control mechanisms to manage complex energy flows. This paper presents a comprehensive analysis of renewable energy–integrated smart grids, focusing on advanced control strategies, cybersecurity vulnerabilities, and system reliability assessment. Centralized and decentralized control approaches are examined for managing variability in renewable generation, demand-side participation, and energy storage coordination. In parallel, the paper analyzes cyber threats targeting smart grid infrastructures and evaluates mitigation strategies to enhance system resilience. Reliability metrics and probabilistic assessment methods are employed to evaluate grid performance under high renewable penetration scenarios. The study demonstrates that coordinated control combined with robust cybersecurity frameworks significantly enhances grid reliability and operational flexibility. The findings provide critical insights for the design and deployment of resilient, secure, and sustainable smart grid systems.
Machine Learning Approaches for Real-Time Monitoring and Optimization of Industrial Process Systems
Modern industrial systems operate under increasing pressure to improve efficiency, reduce downtime, and adapt dynamically to changing operational conditions. Traditional rule-based monitoring and control mechanisms are often inadequate for handling the complexity, nonlinearity, and uncertainty inherent in large-scale industrial processes. Machine learning has emerged as a powerful enabler for real-time monitoring and optimization by leveraging data-driven models capable of learning complex system behaviors. This paper presents a comprehensive analysis of machine learning approaches applied to industrial process monitoring, fault detection, and operational optimization. Supervised, unsupervised, and reinforcement learning techniques are examined in the context of real-time data streams generated by industrial sensors and control systems. A modular framework integrating data acquisition, feature extraction, model inference, and adaptive control is proposed. Performance evaluation demonstrates that machine learning–based systems significantly enhance process stability, reduce energy consumption, and improve fault detection accuracy compared to conventional approaches. Challenges related to model interpretability, computational latency, and industrial deployment are critically discussed. The study highlights the transformative potential of machine learning in advancing intelligent, autonomous, and resilient industrial process systems.
Advanced Biodegradable Polymers for Biomedical and Environmental Applications: Performance and Lifecycle Assessment
The growing environmental burden of persistent synthetic polymers and the increasing demand for biocompatible materials have accelerated research into advanced biodegradable polymers for biomedical and environmental applications. This paper presents a comprehensive evaluation of biodegradable polymer systems with emphasis on structure–property relationships, functional performance, and lifecycle sustainability. Both natural and synthetic biodegradable polymers are examined for applications ranging from drug delivery systems and tissue scaffolds to biodegradable packaging and environmental remediation materials. Material performance is analyzed in terms of mechanical strength, degradation kinetics, biocompatibility, and functional stability under application-specific conditions. Furthermore, lifecycle assessment is employed to evaluate the environmental footprint associated with polymer synthesis, processing, use, and end-of-life degradation. The analysis reveals that advanced polymer engineering strategies, including copolymerization, blending, and surface modification, significantly enhance functional performance while maintaining biodegradability. However, trade-offs between material durability and degradation rate remain a critical challenge. The study underscores the importance of integrating performance optimization with environmental assessment to guide the development of next-generation biodegradable polymers that meet both biomedical safety standards and sustainability objectives.
Internet of Things–Driven Smart Agriculture Systems for Precision Farming and Climate- Resilient Food Production
Climate variability, resource scarcity, and population growth pose significant challenges to global food security, necessitating the adoption of intelligent and adaptive agricultural systems. Internet of Things (IoT)–driven smart agriculture has emerged as a transformative solution for precision farming and climate- resilient food production. This paper presents a comprehensive framework for IoT-enabled agricultural systems that integrate sensor networks, data analytics, and automated decision-support mechanisms. The proposed architecture enables real-time monitoring of soil, crop, and environmental parameters, facilitating data-driven irrigation, fertilization, and pest management strategies. Emphasis is placed on system scalability, energy efficiency, and adaptability to diverse agro-climatic conditions. Field-level data processing and cloud-based analytics are combined to enhance situational awareness and predictive capability. Performance evaluation demonstrates improvements in water-use efficiency, crop yield stability, and resource optimization compared to conventional farming practices. The paper also examines challenges related to connectivity, data interoperability, farmer adoption, and system cost. The findings highlight the potential of IoT-driven smart agriculture to enhance productivity while strengthening resilience against climate-induced stresses, contributing to sustainable and precision-oriented food production systems.
Sustainable Nanomaterials for Energy Storage and Conversion: Design, Characterization, and Environmental Impact
The global transition toward low-carbon energy systems has intensified the demand for high- performance, environmentally sustainable materials for energy storage and conversion technologies. Nanomaterials have emerged as critical enablers in this transition due to their unique physicochemical properties, including high surface area, tunable electronic structures, and enhanced electrochemical activity. This paper presents a comprehensive investigation into the design, synthesis, characterization, and environmental implications of sustainable nanomaterials for applications in batteries, supercapacitors, and electrocatalytic energy conversion systems. Emphasis is placed on green synthesis routes, bio-derived precursors, and low-toxicity nanostructures to reduce environmental burdens associated with conventional material fabrication. Advanced characterization techniques are employed to correlate nanoscale structural features with electrochemical performance metrics. Furthermore, lifecycle assessment is used to evaluate the environmental footprint of nanomaterial production, deployment, and end-of-life management. The study demonstrates that sustainability-oriented nanomaterial design can achieve competitive energy performance while significantly mitigating ecological risks. The findings contribute to the development of next- generation energy materials that align technological advancement with environmental responsibility.
Artificial Intelligence–Enabled Predictive Analytics for Smart Healthcare and Disease Early Warning Systems
The rapid digitization of healthcare systems has resulted in unprecedented volumes of clinical, physiological, and behavioral data, creating new opportunities for predictive analytics-driven disease surveillance. Artificial Intelligence (AI) techniques, particularly machine learning and deep learning models, have emerged as powerful tools for transforming heterogeneous healthcare data into actionable intelligence. This paper presents a comprehensive framework for AI-enabled predictive analytics in smart healthcare, with a focus on disease early warning systems. The proposed approach integrates electronic health records, wearable sensor data, and population-level epidemiological indicators to enable real-time risk prediction and early intervention. Various supervised and unsupervised learning algorithms are evaluated for disease onset prediction, anomaly detection, and outbreak forecasting. Performance metrics such as accuracy, sensitivity, specificity, and prediction lead time are analyzed to assess system effectiveness. Additionally, the study examines critical challenges related to data quality, algorithmic bias, privacy preservation, and system scalability. The findings demonstrate that AI-driven predictive analytics can significantly enhance early disease detection, reduce healthcare costs, and improve patient outcomes when embedded within smart healthcare infrastructures. The paper concludes by outlining future research directions aimed at explainable AI, federated learning, and ethical governance of intelligent healthcare systems.
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.