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.
Renewable Energy Hybrid Systems: Optimization of Solar–Wind Microgrids Using AI Algorithms
The increasing demand for sustainable energy solutions has accelerated the adoption of renewable energy systems, particularly in decentralized and off-grid environments. Hybrid renewable energy systems combining solar and wind resources offer significant potential for improving energy reliability and efficiency. However, the intermittent and stochastic nature of these energy sources presents challenges in system optimization and energy management. This study proposes an artificial intelligence– based optimization framework for solar–wind hybrid microgrids, integrating machine learning and evolutionary algorithms to enhance system performance. The framework incorporates real-time data on solar irradiance, wind speed, and load demand to optimize energy generation, storage, and distribution. Algorithms such as genetic algorithms, particle swarm optimization, and artificial neural networks are employed to achieve optimal system configuration and operational efficiency. The results demonstrate improved energy utilization, reduced operational costs, and enhanced system reliability compared to conventional optimization methods. The study highlights the potential of AI-driven approaches in advancing renewable energy systems and supporting the transition toward sustainable energy infrastructures.
Copyright (c) 2026 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Digital Twin Technology for Real-Time Monitoring and Optimization of Industrial Processes
The rapid evolution of Industry 4.0 has introduced advanced technologies aimed at improving efficiency, productivity, and sustainability in industrial systems. Among these, digital twin technology has emerged as a transformative approach for real-time monitoring and optimization of industrial processes. A digital twin is a virtual representation of a physical system that continuously updates using real-time data, enabling simulation, analysis, and predictive decision-making. This study presents a comprehensive framework for implementing digital twin technology in industrial environments, integrating Internet of Things sensors, cloud computing, and advanced analytics. The proposed system enables real-time monitoring of process parameters, predictive maintenance, and performance optimization through data- driven insights. Machine learning models are employed to analyze system behavior and predict potential failures, reducing downtime and maintenance costs. The results demonstrate significant improvements in operational efficiency, resource utilization, and system reliability. Furthermore, the study explores challenges related to data integration, interoperability, and cybersecurity, providing insights into future developments. The findings highlight the potential of digital twin technology to revolutionize industrial operations by enabling intelligent, adaptive, and efficient systems.
Sustainable Biopolymer Development from Agricultural Waste for Eco-Friendly Packaging Solutions
The escalating environmental concerns associated with plastic pollution have intensified the demand for sustainable and biodegradable alternatives in packaging industries. Conventional petroleum- based plastics, while offering durability and cost efficiency, contribute significantly to environmental degradation due to their non-biodegradable nature. This study explores the development of sustainable biopolymers derived from agricultural waste for eco-friendly packaging applications. Agricultural residues such as rice husk, wheat straw, corn stover, and sugarcane bagasse are utilized as raw materials for biopolymer synthesis. The research focuses on extraction, processing, and modification of natural polymers including cellulose, starch, and lignin to enhance their mechanical and barrier properties. Various fabrication techniques, including solvent casting and extrusion, are employed to produce biodegradable films. The results demonstrate that biopolymer composites reinforced with natural fibers exhibit improved tensile strength, flexibility, and moisture resistance. Furthermore, the study evaluates the biodegradability and environmental impact of the developed materials, highlighting their potential as sustainable alternatives to conventional plastics. The findings contribute to the advancement of green materials and support the transition toward circular economy practices in the packaging sector.
Machine Learning Approaches for Early Detection and Control of Emerging Infectious Diseases
The rapid emergence and re-emergence of infectious diseases pose a significant threat to global public health, necessitating the development of advanced detection and control mechanisms. Traditional epidemiological approaches often rely on delayed reporting and limited data integration, resulting in slow response times and increased transmission risk. This study explores the application of machine learning techniques for the early detection and control of emerging infectious diseases. By integrating heterogeneous datasets, including clinical records, genomic data, environmental variables, and mobility patterns, the research develops predictive models capable of identifying outbreaks at an early stage. Algorithms such as logistic regression, random forest, support vector machines, and deep neural networks are evaluated for their effectiveness in disease prediction and classification. The results demonstrate that machine learning models significantly improve detection accuracy and response time compared to conventional methods. Furthermore, the study highlights the role of predictive analytics in guiding public health interventions, including resource allocation and containment strategies. The findings underscore the potential of machine learning in transforming infectious disease surveillance into a proactive, data-driven system capable of mitigating global health risks.
Assessment of Nursing Strategies to Minimize Medication Errors in Low-Resource Healthcare Facilities
Medication errors continue to represent a critical challenge to patient safety worldwide, particularly in low-resource healthcare settings where systemic limitations, workforce shortages, and lack of standardized protocols increase the likelihood of such errors. This study aims to assess the underlying causes of medication errors and evaluate the effectiveness of nursing strategies in minimizing their occurrence in resource-constrained environments. A descriptive cross-sectional study was conducted among 40 registered nurses working in a hospital setting characterized by limited resources and high patient load. Data were collected through a structured questionnaire focusing on demographic characteristics, experiences with medication errors, perceived causes, and applied nursing interventions. The findings revealed that a significant proportion of nurses had either witnessed or been involved in medication errors, highlighting the widespread nature of the issue. The primary contributing factors included heavy workload, communication breakdowns, fatigue, illegible prescriptions, and absence of standardized procedures. Nursing interventions such as adherence to the “Five Rights” of medication administration, double-checking protocols, and improved communication practices were found to be effective in reducing errors. The study concludes that strengthening nursing competencies, promoting a culture of safety, and implementing system-level improvements can significantly reduce medication errors. These findings emphasize the need for continuous education, structured communication systems, and supportive organizational policies to enhance patient safety in low-resource healthcare environment
Integration of IoT and Edge Computing for Smart Water Resource Management in Urban Ecosystems
The Urban water management has become increasingly complex due to rapid urbanization, population growth, and climate-induced variability in water availability. Conventional water management systems, characterized by centralized infrastructure and delayed response mechanisms, are inadequate in addressing real-time challenges such as leakage detection, demand forecasting, and quality monitoring. This study presents an integrated framework combining Internet of Things (IoT) technologies with edge computing to enable intelligent, real-time water resource management in urban ecosystems. The proposed system utilizes distributed sensor networks to collect data on water flow, pressure, and quality parameters, while edge computing nodes process data locally to reduce latency and bandwidth requirements. Machine learning algorithms are deployed at the edge to perform predictive analytics, including demand forecasting and anomaly detection. The results demonstrate significant improvements in system responsiveness, operational efficiency, and resource optimization compared to traditional cloud-centric approaches. The study further explores scalability, security, and interoperability challenges, highlighting the potential of IoT edge integration to transform urban water management into a sustainable, adaptive, and resilient system.
Advanced Nanocomposite Materials for High- Efficiency Energy Storage in Next-Generation Batteries
The rapid global transition toward renewable energy and electrification has intensified the demand for high-performance energy storage systems with superior energy density, power density, and cycle stability. Conventional battery materials face limitations in conductivity, structural degradation, and electrochemical inefficiencies, thereby restricting their applicability in next-generation technologies such as electric vehicles and grid-scale storage. This study explores the development and application of advanced nanocomposite materials for high-efficiency energy storage in modern battery systems. By integrating nanostructured materials such as graphene, metal oxides, and conductive polymers, the research proposes hybrid architectures that enhance ion transport, electrical conductivity, and structural integrity. Experimental and computational analyses demonstrate that nanocomposite electrodes significantly improve charge- discharge rates, reduce internal resistance, and enhance cycling stability compared to traditional materials. The study further investigates synthesis techniques, including sol-gel processing, hydrothermal methods, and chemical vapor deposition, to optimize material properties. The findings highlight the transformative potential of nanocomposites in overcoming existing battery limitations and enabling the development of sustainable, high-performance energy storage technologies for future applications.
Artificial Intelligence–Driven Predictive Modeling for Climate-Resilient Agricultural Systems in Semi-Arid Regions
Climate change has intensified the vulnerability of agricultural systems in semi-arid regions, where limited water availability and unpredictable weather patterns significantly affect crop productivity. Traditional agricultural decision-making approaches, largely dependent on historical observations, are increasingly inadequate in addressing dynamic climatic uncertainties. This study proposes an artificial intelligence–driven predictive modeling framework to enhance climate resilience in semi-arid agricultural systems. The framework integrates multi-source datasets, including meteorological records, soil properties, crop growth parameters, and satellite-derived vegetation indices, to develop predictive models using machine learning algorithms such as random forest, support vector machines, and deep neural networks. The results demonstrate that AI-based models outperform conventional statistical techniques in predicting crop yield, drought occurrence, and soil moisture variability. Furthermore, the integration of predictive models into intelligent decision support systems enables real-time optimization of irrigation scheduling and crop management strategies. The study highlights the potential of artificial intelligence to improve resource efficiency, reduce climate-induced risks, and promote sustainable agricultural practices in semi-arid regions.
Smart Waste Management Systems Using Robotics, Computer Vision, and Circular Economy Principles
Rapid urbanization, changing consumption patterns, and population growth have intensified the global solid waste crisis. Traditional waste management systems based on collection, landfilling, and low- efficiency recycling are increasingly unsustainable due to rising costs, environmental pollution, greenhouse gas emissions, and resource depletion. Smart waste management systems that combine robotics, computer vision, Internet of Things technologies, and circular economy principles offer a transformative pathway toward cleaner and more resource-efficient cities. This paper investigates smart waste management systems using robotics, computer vision, and circular economy principles. It examines intelligent waste segregation, sensor-enabled collection infrastructure, autonomous sorting lines, route optimization, material recovery analytics, reverse logistics, and data-driven governance. Applications in municipal waste, industrial waste, e-waste, healthcare waste, plastics recovery, and organic waste valorization are analyzed. Particular attention is given to recycling efficiency, contamination reduction, labor safety, citizen participation, lifecycle sustainability, and economic viability. Benefits include reduced landfill dependence, higher material recovery rates, lower emissions, safer operations, and creation of secondary resource markets. Major barriers include capital cost, informal sector integration, data fragmentation, policy inconsistency, and behavioral resistance. A future roadmap is proposed involving AI-native recycling plants, robotic micro-factories, blockchain material passports, urban mining ecosystems, and zero-waste city platforms. The paper concludes that smart waste systems can convert waste streams into strategic resource flows when technological innovation is aligned with inclusive governance and circular design principles.
Copyright (c) 2019 VW Applied Sciences https://creativecommons.org/licenses/by/4.0
Digital Twin Frameworks for Predictive Maintenance and Autonomous Decision-Making in Industry 5.0
The transition from Industry 4.0 to Industry 5.0 emphasizes human-centric, resilient, and sustainable manufacturing systems where intelligent machines collaborate with people rather than merely automating tasks. Digital twin frameworks have emerged as a foundational technology for this transformation by creating dynamic virtual replicas of physical assets, processes, and production environments. These virtual models continuously synchronize with real-world data to support predictive maintenance, optimization, simulation, and autonomous decision-making. This paper investigates digital twin frameworks for predictive maintenance and autonomous decision-making in Industry 5.0. It examines enabling technologies including Internet of Things sensors, cyber-physical systems, cloud-edge computing, artificial intelligence, augmented reality, and real-time analytics. Applications in machinery health monitoring, quality assurance, energy management, robotics coordination, supply-chain visibility, and adaptive production planning are analyzed. Particular attention is given to failure prediction, remaining useful life estimation, human-machine collaboration, sustainability performance, and resilience under disruptions. Benefits include reduced downtime, improved asset utilization, lower maintenance cost, faster innovation cycles, and safer operations. Major barriers include data interoperability, cybersecurity, model fidelity, integration with legacy equipment, workforce skill gaps, and governance concerns. A future roadmap is proposed involving cognitive twins, federated industrial intelligence, self-optimizing factories, and ethical AI oversight. The paper concludes that digital twin ecosystems can become the operating intelligence of Industry 5.0 by combining predictive insight, autonomous response, and human expertise within adaptive industrial environments.
Carbon Capture, Utilization, and Storage Technologies for Net-Zero Industrial Emission Pathways
Achieving net-zero emissions requires deep decarbonization of industrial sectors such as cement, steel, chemicals, refining, and power generation, where process emissions and high-temperature energy demands are difficult to eliminate through electrification alone. Carbon capture, utilization, and storage (CCUS) technologies have emerged as a critical pathway for reducing industrial carbon dioxide emissions while enabling transitional and long-term climate strategies. This paper investigates CCUS technologies for net-zero industrial emission pathways. It examines capture methods including post-combustion, pre- combustion, oxy-fuel combustion, adsorption, membranes, and direct air capture. It further analyzes carbon utilization routes such as synthetic fuels, chemicals, mineralization, enhanced materials, and biological conversion, alongside geological storage in depleted reservoirs, saline aquifers, and basalt formations. Particular attention is given to techno-economic feasibility, lifecycle emissions, infrastructure requirements, policy incentives, and integration with hydrogen and renewable energy systems. Benefits include mitigation of hard-to-abate emissions, preservation of industrial competitiveness, and support for circular carbon economies. Major barriers include high capital cost, energy penalties, transport network limitations, storage verification, public acceptance, and regulatory uncertainty. A future roadmap is proposed involving low-cost solvents, modular capture systems, carbon hubs, digital monitoring, and market mechanisms rewarding negative or avoided emissions. The paper concludes that CCUS is not a standalone substitute for broader decarbonization, but an essential complement for sectors where direct elimination of carbon emissions remains technologically or economically constrained.
Quantum-Inspired Computing Approaches for Complex Optimization Problems in Industrial Engineering
Industrial engineering increasingly depends on solving large-scale optimization problems involving scheduling, routing, resource allocation, inventory control, energy management, and supply-chain coordination. Classical exact methods often become computationally expensive when problem size, uncertainty, and combinatorial complexity grow rapidly. Although practical quantum computers are still developing, quantum-inspired computing approaches have emerged as powerful alternatives that adapt principles from quantum mechanics to run on classical hardware. This paper investigates quantum-inspired computing approaches for complex optimization problems in industrial engineering. It examines methodologies including simulated annealing, tensor-network optimization, Ising formulations, quantum- inspired evolutionary algorithms, digital annealers, and hybrid classical-intelligent heuristics. Applications in production scheduling, facility layout, vehicle routing, maintenance planning, portfolio optimization, warehouse operations, and smart manufacturing are analyzed. Particular attention is given to solution quality, scalability, stochastic decision-making, real-time adaptability, and integration with Industry 4.0 data systems. Benefits include faster near-optimal solutions, improved operational efficiency, reduced energy consumption, and better responsiveness under uncertainty. Major challenges include formulation difficulty, parameter tuning, benchmarking inconsistency, interpretability, and transition barriers within traditional industrial workflows. A future roadmap is proposed involving hybrid quantum-classical ecosystems, AI- guided optimization engines, digital twins, and autonomous decision platforms. The paper concludes that quantum-inspired computing can deliver practical industrial value today by bridging the gap between conventional optimization and future quantum advantage, especially in environments where speed, flexibility, and complexity management are essential.
Machine Learning Models for Early Disease Detection Using Wearable Biosensor Data in Personalized Healthcare
Healthcare is shifting from hospital-centered treatment to continuous, preventive, and personalized care supported by digital technologies. Wearable biosensors such as smartwatches, patches, rings, textile sensors, and portable electrochemical devices generate real-time physiological data that can reveal subtle changes before symptoms become clinically severe. Machine learning models have become essential for transforming these high-volume and heterogeneous data streams into actionable medical insights. This paper investigates machine learning models for early disease detection using wearable biosensor data in personalized healthcare. It examines sensor modalities including heart rate, electrocardiography, blood oxygen saturation, skin temperature, glucose, motion signals, sleep patterns, and stress indicators. The study analyzes supervised, unsupervised, deep learning, and federated learning approaches for detecting cardiovascular disease, diabetes complications, respiratory infections, neurological disorders, sleep abnormalities, and mental health risks. Particular attention is given to signal preprocessing, multimodal fusion, anomaly detection, interpretability, and privacy-preserving analytics. Benefits include earlier intervention, reduced hospitalization, remote monitoring, improved adherence, and individualized treatment planning. Major challenges include noisy data, algorithmic bias, battery constraints, interoperability gaps, clinical validation, and ethical concerns surrounding surveillance and data ownership. A future roadmap is proposed involving digital twins, edge AI, generative health assistants, and adaptive closed-loop therapeutic systems. The paper concludes that wearable biosensor intelligence can significantly improve healthcare outcomes when robust models are combined with clinical oversight, secure data governance, and equitable access across diverse populations.
Bioinspired Self-Healing Materials for Durable Infrastructure and Extreme Environmental Applications
Modern infrastructure is increasingly exposed to heavy loads, corrosion, freeze-thaw cycles, chemical attack, fatigue damage, seismic stress, and climate-driven extremes. Conventional materials often accumulate microcracks and hidden defects that gradually reduce strength, service life, and safety while increasing maintenance costs. Inspired by biological systems such as skin, bone, vascular tissues, and plant regeneration mechanisms, self-healing materials have emerged as an advanced strategy for autonomous damage repair and long-term durability. This paper investigates bioinspired self-healing materials for durable infrastructure and extreme environmental applications. It examines healing mechanisms based on microcapsules, vascular networks, bacteria-induced mineralization, reversible polymers, shape-memory systems, and nano-engineered composites. Applications in concrete, asphalt pavements, protective coatings, aerospace structures, marine systems, pipelines, and energy infrastructure are analyzed. Particular emphasis is placed on crack sealing, corrosion prevention, fatigue recovery, and lifespan extension under severe thermal, mechanical, and chemical conditions. Sustainability benefits include lower repair frequency, reduced raw material consumption, lower lifecycle emissions, and improved resilience of critical assets. Major challenges include scalability, cost, healing reliability, long-term monitoring, compatibility with conventional manufacturing, and regulatory acceptance. A future roadmap is proposed involving sensor-integrated smart materials, AI-guided damage prediction, additive manufacturing, and multifunctional composites that combine structural strength with autonomous repair. The study concludes that bioinspired self-healing materials can redefine engineering durability by shifting maintenance from reactive repair to built-in regeneration, thereby supporting safer and more sustainable infrastructure systems.
Renewable Energy Microgrids Integrated with IoT for Smart and Resilient Rural Electrification
Universal access to reliable electricity remains a major developmental challenge in many rural and remote regions. Conventional grid expansion is often constrained by difficult terrain, low population density, high infrastructure cost, and weak maintenance capacity. Renewable energy microgrids integrated with Internet of Things (IoT) technologies have emerged as an effective solution for smart and resilient rural electrification. This paper examines the design, operation, and socio-economic impact of decentralized micro-grids powered by solar, wind, biomass, and hybrid renewable resources. It explores how IoT-enabled sensors, smart meters, predictive analytics, and remote control platforms improve generation efficiency, load balancing, fault detection, demand response, and system reliability. Particular attention is given to battery storage management, community energy governance, productive rural loads, and climate resilience during extreme weather events. The paper also analyzes barriers such as financing gaps, policy uncertainty, cybersecurity risks, affordability concerns, and shortage of technical skills in rural areas. A future roadmap is proposed involving AI-assisted dispatch, blockchain-enabled peer-to-peer trading, modular storage systems, and digital service models for maintenance. The study concludes that renewable IoT-enabled microgrids can accelerate inclusive development by powering households, schools, healthcare facilities, irrigation systems, and small enterprises while reducing dependence on fossil fuels. Their long-term success depends on robust policy frameworks, local participation, interoperable technologies, and sustainable business models adapted to rural realities.
Advanced Nanomaterial Membranes for Next- Generation Water Purification and Desalination Technologies
Freshwater scarcity has become one of the most critical global challenges due to population growth, industrial expansion, pollution, and climate-induced hydrological changes. Conventional water treatment systems often face limitations in energy consumption, fouling, selectivity, and operational cost, especially when treating complex wastewater streams or saline water sources. Advanced nanomaterial membranes have emerged as a transformative solution for next-generation water purification and desalination technologies. This paper investigates the role of nanostructured membranes based on graphene oxide, carbon nanotubes, metal-organic frameworks, nanofibers, zeolites, and hybrid composites in enhancing permeability, selectivity, anti-fouling behavior, and durability. The study examines membrane fabrication methods, transport mechanisms, contaminant removal performance, and applications in reverse osmosis, nanofiltration, ultrafiltration, forward osmosis, and membrane distillation. Particular emphasis is placed on desalination efficiency, heavy metal removal, pathogen rejection, and treatment of industrial effluents. Sustainability dimensions such as reduced energy demand, lower chemical usage, membrane regeneration, and circular water management are also discussed. Major barriers include scaling challenges, membrane degradation, nanomaterial leaching risks, manufacturing cost, and regulatory uncertainty. A future roadmap is proposed involving smart self-cleaning membranes, AI-assisted process optimization, renewable-energy-powered desalination, and decentralized treatment systems. The paper concludes that advanced nanomaterial membranes offer a powerful pathway toward secure and sustainable water systems, provided that material innovation is matched by affordability, lifecycle safety, and large-scale deployment strategies.
Artificial Intelligence–Driven Precision Agriculture Systems for Sustainable Crop Yield Optimization Under Climate Change Conditions
Agriculture faces severe pressure from climate variability, population growth, shrinking natural resources, and the need for sustainable food production. Traditional farming methods based on generalized practices often fail to address field-level variability and emerging climatic risks. Artificial intelligence (AI) has become a transformative force in precision agriculture by enabling data-driven decisions that optimize crop productivity while conserving resources. This paper examines AI-driven precision agriculture systems for sustainable crop yield optimization under climate change conditions. It analyzes the integration of remote sensing, Internet of Things (IoT) sensors, machine learning, robotics, and predictive analytics into modern farming ecosystems. AI models can estimate yield, detect diseases, optimize irrigation, recommend fertilizer dosage, forecast pest outbreaks, and adapt cropping schedules according to changing weather conditions. These capabilities reduce input waste, improve profitability, and strengthen resilience against droughts, floods, and heat stress. The paper also evaluates implementation barriers such as fragmented landholdings, high capital costs, weak digital infrastructure, limited farmer literacy, and concerns regarding data ownership. A comprehensive framework is proposed in which satellite data, drone imagery, soil information, and farm operations are processed through cloud-edge intelligence systems to generate real-time recommendations. The study concludes that AI-driven precision agriculture offers one of the most practical pathways toward climate-smart and sustainable farming. However, inclusive adoption requires affordable technologies, policy support, open data ecosystems, rural connectivity, and continuous capacity building among farming communities.
Carbon-Negative Manufacturing via AI-Optimized Direct Air Capture and Circular Material Design
The urgency of mitigating climate change has driven the need for transformative industrial strategies that move beyond carbon neutrality toward carbon-negative manufacturing. This study presents an integrated framework combining artificial intelligence–optimized direct air capture (DAC) systems with circular material design to enable sustainable industrial production. The proposed approach leverages machine learning algorithms to optimize carbon capture efficiency, energy consumption, and material lifecycle processes. A hybrid modeling architecture integrates thermodynamic simulations, process optimization and lifecycle assessment to evaluate system performance. The framework incorporates closed- loop material flows, waste valorization, and carbon utilization pathways, ensuring minimal environmental impact. Simulation results demonstrate that AI-optimized DAC systems can achieve up to 40% improvement in capture efficiency while reducing energy consumption significantly. Furthermore, circular material strategies enable net-negative emissions by reintegrating captured carbon into production cycles. The study also evaluates economic feasibility and policy implications, highlighting pathways for large-scale implementation. This research contributes to the advancement of sustainable manufacturing and provides a scalable model for achieving carbon-negative industrial systems in alignment with global climate goals.
Digital Twin–Driven Climate Resilience Modeling Using Multiscale Earth System Data Integration
Climate change has intensified the frequency and severity of extreme environmental events, necessitating advanced predictive and adaptive modeling frameworks for resilience planning. This study introduces a digital twin–driven climate resilience modeling framework that integrates multiscale Earth system data with artificial intelligence to simulate, predict, and optimize responses to climate-induced disruptions. Unlike traditional climate models that operate on static or semi-dynamic datasets, the proposed system constructs a continuously evolving digital twin of regional and global ecosystems, incorporating atmospheric, oceanic, hydrological, and socio-economic variables. The framework employs hybrid deep learning architectures and data assimilation techniques to enhance prediction accuracy and temporal resolution. Multiscale modeling enables the integration of satellite observations, ground-based sensors, and historical climate data, facilitating real-time scenario analysis. Experimental results indicate substantial improvements in predictive reliability, early warning capabilities, and adaptive policy modeling compared to conventional approaches. The system demonstrates the ability to simulate cascading effects of climate events across interconnected systems. This research contributes to the advancement of intelligent climate modeling and provides a scalable platform for decision-makers to design resilient infrastructure and mitigation strategies in the face of accelerating environmental change.
Self-Healing Smart Materials for Autonomous Infrastructure Repair in Extreme Environments
Infrastructure systems operating in extreme environments, including polar regions, deep-sea installations, and high-radiation zones, are highly susceptible to structural degradation, leading to increased maintenance costs and safety risks. This research presents an advanced framework for the development and application of self-healing smart materiadls capable of autonomous damage detection and repair without external intervention. The study integrates microencapsulation techniques, vascular healing networks, and stimuli-responsive polymers with embedded sensing mechanisms to create multifunctional materials. A coupled thermo-mechanical and chemical modeling approach is proposed to simulate crack formation and healing kinetics under extreme environmental conditions. Experimental simulations demonstrate that the proposed materials significantly enhance structural lifespan, reduce crack propagation rates, and maintain mechanical integrity under cyclic loading and thermal stress. Furthermore, machine learning-assisted predictive modeling is incorporated to optimize healing efficiency and material performance. The results reveal that self-healing materials can restore up to 85–95% of original mechanical strength, depending on environmental parameters. This work contributes to the advancement of autonomous infrastructure systems and offers a sustainable solution for long-term structural resilience in harsh environments.
Biohybrid Neural Interfaces: Integrating Living Cells with AI Systems for Adaptive Biomedical Devices
The convergence of biological systems and artificial intelligence has given rise to biohybrid neural interfaces, a transformative paradigm in biomedical engineering aimed at creating adaptive, self-regulating medical devices. This study presents a comprehensive framework for integrating living neural cells with AI-driven computational architectures to develop responsive biomedical systems capable of real time adaptation. Unlike conventional neural interfaces, which rely on static signal processing, the proposed biohybrid model leverages the inherent plasticity of biological neurons combined with machine learning algorithms to achieve dynamic feedback and control. The research explores the design, implementation, and evaluation of hybrid systems incorporating cultured neuronal networks, microelectrode arrays, and deep learning models. Experimental simulations demonstrate enhanced signal fidelity, improved adaptability, and reduced latency in response to physiological changes. The framework further incorporates reinforcement learning mechanisms to enable continuous system optimization based on biological feedback. Ethical considerations, biocompatibility challenges, and long-term stability are critically examined. The findings indicate that biohybrid neural interfaces hold significant potential for applications in neuroprosthetics, brain- computer interfaces, and personalized medicine. This work contributes to the emerging interdisciplinary domain at the intersection of neuroscience, artificial intelligence, and bioengineering, offering a scalable pathway toward next-generation intelligent biomedical devices capable of seamless integration with human physiology.
Quantum-Inspired Machine Learning Frameworks for Ultra-Efficient Optimization in Smart Energy Grids
The rapid evolution of smart energy grids, characterized by decentralized generation, renewable energy integration, and real-time demand fluctuations, has introduced complex optimization challenges that exceed the capabilities of classical computational methods. This study proposes a quantum-inspired machine learning (QIML) framework designed to achieve ultra-efficient optimization in smart grid environments. Drawing from principles such as quantum superposition, probabilistic state representation, and annealing- inspired search strategies, the framework enhances the scalability and convergence efficiency of optimization processes. The proposed system integrates digital twin modeling with adaptive machine learning mechanisms to enable real-time monitoring and decision-making. Extensive simulations conducted on large-scale energy datasets demonstrate that the QIML framework significantly outperforms traditional algorithms in terms of computational efficiency, energy loss minimization, and grid stability. The model effectively addresses high-dimensional, nonlinear optimization problems while maintaining robustness under uncertain conditions. Furthermore, the framework supports dynamic learning, allowing continuous adaptation to changing grid parameters. This research contributes to the convergence of artificial intelligence, quantum-inspired computation, and energy systems engineering, offering a novel pathway for the development of resilient and sustainable smart grid infrastructures.
Machine Learning Approaches for Climate- Resilient Smart Agriculture Using Remote Sensing and IoT Technologies
Agricultural production systems are increasingly vulnerable to climate variability, extreme weather events, and changing environmental conditions. These challenges necessitate the development of intelligent agricultural technologies capable of supporting adaptive decision-making and resource-efficient farming practices. Smart agriculture integrates Internet of Things (IoT) technologies, remote sensing data, and machine learning algorithms to monitor environmental conditions and optimize agricultural operations. This study proposes a machine learning–driven framework for climate-resilient smart agriculture that combines satellite-based remote sensing, ground-level IoT sensor networks, and predictive analytics. The framework continuously collects environmental parameters including soil moisture, temperature, humidity, rainfall, and vegetation indices derived from satellite imagery. Machine learning models are then used to predict crop health conditions, irrigation requirements, and potential climate-related risks. The research introduces a data processing architecture consisting of a sensing layer, data integration module, machine learning prediction engine, and agricultural decision support system. Several machine learning algorithms including Random Forest, Support Vector Machine, and Artificial Neural Networks are evaluated for their performance in crop yield prediction and climate stress detection. Experimental analysis using simulated agricultural datasets demonstrates that machine learning-based predictive models can significantly improve the accuracy of crop monitoring and irrigation scheduling. Results indicate that the proposed framework enhances crop yield prediction accuracy while reducing water consumption through intelligent irrigation recommendations. The findings highlight the potential of integrating remote sensing technologies and machine learning algorithms to support climate-resilient agricultural systems capable of adapting to environmental uncertainty and supporting sustainable food production.
Advanced Nanomaterial-Based Energy Storage Systems for Next-Generation Lithium-Ion and Solid-State Batteries
Energy storage technologies are critical components in modern energy systems, supporting applications ranging from portable electronics to electric vehicles and renewable energy integration. Lithium-ion batteries have dominated the energy storage market due to their high energy density, long cycle life, and relatively stable performance. However, conventional lithium-ion battery systems face limitations related to safety, energy density, and material degradation during repeated charge–discharge cycles. Nanomaterial-based electrode architectures have emerged as promising solutions for improving battery performance and enabling next-generation energy storage systems. This study investigates the use of advanced nanostructured materials for enhancing the electrochemical performance of lithium-ion and solid-state batteries. The research presents a comprehensive framework that integrates nanomaterial synthesis techniques, electrode architecture design, and electrochemical characterization methods. Nanostructured materials including graphene-based composites, silicon nanoparticles, and transition metal oxide nanostructures are analyzed for their potential to improve battery capacity and cycle stability. A theoretical model is developed to evaluate lithium-ion diffusion dynamics in nanostructured electrodes. Experimental simulation results demonstrate that nanomaterial-based electrodes exhibit improved ion transport characteristics and higher surface area for electrochemical reactions. Comparative analysis indicates that the proposed nanostructured electrode systems can improve specific capacity and reduce internal resistance compared with conventional electrode materials. The findings highlight the potential of nanotechnology- driven materials engineering to enable high-performance lithium-ion and solid-state battery systems suitable for future sustainable energy applications.
Edge Computing Enabled Cyber-Physical Systems for Real-Time Monitoring in Autonomous Industrial Environments
Industrial automation has experienced rapid development with the emergence of cyber-physical systems and intelligent robotics. Autonomous industrial environments require continuous monitoring of equipment, processes, and environmental parameters to ensure operational safety and efficiency. Traditional centralized cloud architectures often introduce latency and bandwidth limitations that restrict real-time responsiveness in industrial applications. Edge computing has emerged as a promising solution by bringing computational capabilities closer to data sources. This research proposes an edge computing enabled cyber- physical system architecture designed to support real-time monitoring in autonomous industrial environments. The proposed framework integrates distributed sensors, embedded edge processing units, and machine learning models capable of performing on-site data analysis. Data streams from industrial sensors are processed locally using edge nodes to detect anomalies and generate alerts without relying solely on centralized cloud servers. A layered architecture consisting of sensing infrastructure, edge computing modules, communication networks, and cloud analytics platforms is introduced. The system incorporates lightweight deep learning models for anomaly detection and predictive analytics. Experimental evaluation was performed using simulated industrial monitoring scenarios involving temperature, vibration, and power consumption datasets. Results demonstrate that the edge computing architecture significantly reduces response latency and network congestion while maintaining high monitoring accuracy. The proposed system achieved a latency reduction of approximately 35 percent and improved anomaly detection accuracy compared with traditional cloud-based monitoring frameworks. The findings highlight the importance of edge-enabled cyber-physical systems for enhancing reliability, safety, and operational efficiency in modern autonomous industrial environments.
Digital Twin–Driven Optimization of Smart Manufacturing Systems for Sustainable Production
Smart manufacturing has become a cornerstone of modern industrial development, emphasizing flexibility, efficiency, and sustainability in production processes. Digital Twin technology has emerged as a transformative approach for modeling, monitoring, and optimizing manufacturing systems by creating a dynamic virtual representation of physical assets. This study presents a Digital Twin–driven optimization framework designed to enhance operational efficiency and environmental sustainability in smart manufacturing environments. The proposed framework integrates real-time sensor data, simulation models, and machine learning algorithms to create a continuously updated virtual model of production systems. This virtual model enables predictive analysis, operational optimization, and scenario-based decision making. A multi-layer architecture consisting of physical production systems, data acquisition modules, digital twin simulation models, and optimization algorithms is introduced. The framework utilizes reinforcement learning techniques to improve scheduling efficiency and reduce energy consumption. Experimental evaluation was conducted using a simulated manufacturing cell representing assembly line operations. Results indicate that the proposed digital twin system improves production throughput while reducing energy usage and machine idle time. Comparative analysis demonstrates that the framework achieves up to 21 percent improvement in production efficiency and approximately 17 percent reduction in energy consumption compared with conventional manufacturing optimization methods. The research highlights the potential of Digital Twin technology to support sustainable manufacturing practices and adaptive production management within Industry 4.0 environments.
Artificial Intelligence–Based Predictive Maintenance Framework for Industrial Equipment Using IoT Sensor Networks
Industrial equipment failure leads to significant operational losses and unplanned downtime in manufacturing environments. Predictive maintenance has emerged as an effective strategy to mitigate such risks by anticipating failures before they occur. This research proposes an Artificial Intelligence–based predictive maintenance framework that integrates Internet of Things sensor networks with machine learning analytics to monitor equipment health in real time. The framework collects operational data including vibration, temperature, acoustic emissions, and electrical current from industrial machines using distributed IoT sensors. Data is transmitted through a low-latency communication architecture and processed using an intelligent analytics pipeline that includes feature extraction, anomaly detection, and predictive modeling. A hybrid machine learning model combining Long Short-Term Memory networks and Random Forest classifiers is employed to predict equipment failure patterns. The proposed system architecture supports edge computing for preliminary analysis and cloud-based platforms for deep learning model training. Experimental evaluation using industrial machinery datasets demonstrates improved prediction accuracy and reduced downtime compared to conventional preventive maintenance strategies. The results indicate that the proposed system can achieve failure prediction accuracy above 92 percent while reducing maintenance costs and improving equipment availability. The research contributes a scalable predictive maintenance framework suitable for modern smart factories and Industry 4.0 environments.
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.
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.