VW Engineering International is poised to be a frontier journal in the area of Engineering and Technology. It is an international journal publishing the finest peer-reviewed research in all fields of engineering on the basis of its originality, importance, interdisciplinary interest, timeliness, accessibility, elegance, and surprising conclusions. Title: VW Engineering International Editor-in-Chief: Updating soon Online ISSN: Updating soon 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 Engineering International 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.
MXene-Based Nanomaterials for Next- Generation Energy Storage and Flexible Electronics
The increasing demand for efficient energy storage technologies and flexible electronic devices has stimulated extensive research into advanced nanomaterials capable of simultaneously delivering high electrochemical performance and mechanical adaptability. MXenes, a rapidly emerging family of two- dimensional transition metal carbides, nitrides, and carbonitrides, have demonstrated exceptional potential in these applications owing to their metallic conductivity, hydrophilic surfaces, tunable chemistry, and layered structures. Among the various MXene compositions reported to date, Ti₃ C₂ Tₓ has received considerable attention due to its ease of synthesis and outstanding electrochemical properties. This study explores the application of MXene-based nanomaterials in next-generation energy storage systems and flexible electronic devices. Particular emphasis is placed on understanding the relationship between MXene structure, surface functionalization, and performance characteristics relevant to supercapacitors, rechargeable batteries, flexible sensors, and wearable technologies. Current advances in synthesis techniques, composite engineering strategies, and device fabrication methodologies are critically discussed. Furthermore, the challenges associated with oxidation stability, sheet restacking, and scalable manufacturing are examined in detail. The findings indicate that MXene nanomaterials possess substantial potential to revolutionize sustainable energy storage technologies and enable the development of mechanically robust electronic systems. Continued efforts directed toward environmentally responsible synthesis routes, interfacial engineering, and multifunctional material design may accelerate the translation of MXene research from laboratory investigations to industrial applications.
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Quantum Computing Applications in Solving Complex Optimization Problems in Engineering Systems
The increasing complexity of modern engineering systems has created a demand for advanced computational techniques capable of solving large-scale optimization problems efficiently. Classical computing approaches, while effective for many applications, often struggle with combinatorial complexity and high-dimensional problem spaces. Quantum computing has emerged as a promising paradigm that leverages quantum mechanical principles to perform computations beyond the capabilities of classical systems. This study explores the application of quantum computing in solving complex optimization problems in engineering domains, including power systems, transportation networks, and manufacturing processes. The research focuses on quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), evaluating their performance in comparison to classical optimization methods. Simulation results demonstrate that quantum approaches offer significant advantages in solving combinatorial optimization problems, particularly in terms of speed and solution quality. The study also addresses current limitations, including hardware constraints and error rates, while highlighting future prospects for quantum-enhanced optimization in engineering systems.
Waste-to-Energy Conversion Using Advanced Thermochemical Processes for Sustainable Urban Development
Rapid urbanization has led to a dramatic increase in municipal solid waste generation, posing significant environmental and public health challenges. Traditional waste disposal methods, such as landfilling and open burning, contribute to greenhouse gas emissions, soil contamination, and air pollution. Waste-to-energy technologies have emerged as sustainable alternatives that convert waste materials into useful forms of energy, thereby addressing waste management and energy generation simultaneously. This study investigates advanced thermochemical conversion processes, including pyrolysis, gasification, and plasma arc technology, for efficient waste-to-energy conversion. The research integrates process modeling, energy analysis, and environmental impact assessment to evaluate the feasibility and performance of these technologies. The results demonstrate that thermochemical processes can achieve high energy recovery efficiency while reducing environmental impact. The study highlights the role of waste-to-energy systems in sustainable urban development and emphasizes the need for technological innovation and policy support to promote their adoption.
Application of CRISPR-Based Gene Editing in Developing Climate-Resilient Crop Varieties
Climate change has emerged as a major threat to global food security, significantly affecting crop productivity through extreme weather conditions, drought, salinity, and pest outbreaks. Traditional breeding techniques, while effective, are time-consuming and often limited in their ability to address rapidly changing environmental conditions. CRISPR-based gene editing has revolutionized plant biotechnology by enabling precise, efficient, and targeted modifications of plant genomes. This study explores the application of CRISPR-Cas systems in developing climate-resilient crop varieties, focusing on traits such as drought tolerance, heat resistance, and disease resistance. The research integrates genomic analysis, gene editing techniques, and phenotypic evaluation to assess the effectiveness of CRISPR-mediated modifications. The results demonstrate significant improvements in stress tolerance and crop performance, highlighting the potential of CRISPR technology in sustainable agriculture. The study also addresses ethical, regulatory, and biosafety considerations, emphasizing the need for responsible implementation.
Precision Agriculture, Smart Sensors, IoT, Crop Yield, Resource Efficiency
Smart Sensor Networks for Precision Agriculture: Enhancing Crop Yield and Resource Efficiency
Precision agriculture has emerged as a transformative approach to modern farming, enabling efficient resource utilization and enhanced crop productivity through data-driven decision-making. Smart sensor networks, integrated with Internet of Things technologies, provide real-time monitoring of environmental and soil parameters, facilitating targeted agricultural interventions. This study presents a comprehensive framework for the deployment of smart sensor networks in precision agriculture, focusing on improving crop yield and resource efficiency. The system integrates distributed sensors for monitoring soil moisture, temperature, humidity, and nutrient levels, combined with wireless communication and data analytics platforms. Machine learning algorithms are employed to analyze collected data and generate actionable insights for irrigation scheduling, fertilization, and pest management. The results demonstrate significant improvements in water-use efficiency, crop yield, and cost reduction compared to traditional farming methods. The study also addresses challenges related to scalability, energy consumption, and data reliability, highlighting the potential of smart sensor networks to revolutionize agricultural practices and support sustainable development.
Carbon Capture and Utilization Technologiesfor Industrial Emission Reduction and CircularEconomy
Industrial carbon dioxide emissions are a major contributor to global climate change, necessitating the development of advanced mitigation strategies that align with sustainable development goals. Carbon capture and utilization technologies have emerged as promising approaches for reducing greenhouse gas emissions while enabling the conversion of captured carbon into value-added products. This study presents a comprehensive analysis of carbon capture methods, including post-combustion, pre- combustion, and oxy-fuel combustion, along with emerging utilization pathways such as chemical synthesis, mineralization, and bio-conversion. The research evaluates the efficiency, cost, and environmental impact of various technologies, integrating process modeling and life cycle assessment to determine their feasibility. The findings indicate that advanced solvents, solid sorbents, and membrane-based systems significantly improve capture efficiency, while utilization pathways contribute to the development of a circular carbon economy. The study highlights the potential of integrating carbon capture and utilization systems into industrial processes to reduce emissions, enhance resource efficiency, and support sustainable industrial growth.
Quantum Machine Learning Frameworks for Secure Next-Generation 6G Communication Networks and Intelligent Cyber-Physical Systems
The transition from fifth-generation wireless systems to sixth-generation communication networks is expected to redefine global connectivity through ultra-low latency, native artificial intelligence, holographic communication, integrated sensing, and autonomous cyber-physical ecosystems. However, these capabilities also expand the attack surface of future networks and create unprecedented optimization complexity. Conventional machine learning methods have improved resource allocation, intrusion detection, and traffic forecasting, yet they may struggle with the scale, multidimensionality, and security demands of 6G environments. Quantum machine learning has emerged as a promising paradigm that combines quantum computational principles with data-driven intelligence to solve selected classes of problems more efficiently than classical approaches. This paper develops a quantum machine learning framework for secure 6G communication networks and intelligent cyber-physical systems. The study examines quantum-enhanced optimization for spectrum allocation, federated trust architectures for edge devices, anomaly detection for zero-trust environments, and secure orchestration for distributed autonomous systems. A comparative analytical model is presented in which classical AI architectures are benchmarked against hybrid quantum- classical pipelines. Results indicate that quantum-inspired and hybrid quantum methods can significantly improve decision efficiency, adaptive security response, and dynamic network management when deployed in suitable problem domains. The paper also evaluates implementation barriers including hardware noise, cost, talent shortages, interoperability, and regulatory uncertainty. The findings suggest that quantum machine learning can become a strategic pillar of future digital infrastructure when integrated responsibly with scalable communication engineering and cybersecurity governance.
Copyright (c) 2019 VW Engineering International
Sustainable Smart City Infrastructure Using IoT, Edge Computing, and Renewable Energy for Climate-Resilient Urban Development
Rapid urbanization, climate stress, and infrastructure overload have intensified the need for cities that are intelligent, efficient, and environmentally resilient. Conventional urban systems were designed for static populations and predictable resource flows, whereas modern cities face flooding, heatwaves, congestion, air pollution, waste accumulation, and rising energy demand. This paper proposes a sustainable smart city infrastructure model integrating Internet of Things technologies, edge computing architectures, and renewable energy systems for climate-resilient urban development. The study examines how distributed sensing, real-time analytics, decentralized decision systems, and clean energy networks can improve mobility, water governance, waste management, public safety, and environmental quality. A unified urban framework is developed in which transport systems, utilities, buildings, and governance platforms exchange data through secure interoperable layers. Edge intelligence reduces latency and bandwidth pressure while enabling rapid local response during emergencies. Renewable microgrids, solar rooftops, battery storage, and demand-responsive loads strengthen urban energy security. Comparative scenario analysis indicates that integrated smart infrastructure can reduce emissions, improve service reliability, lower operational costs, and enhance adaptive capacity under climate shocks. The paper also discusses governance, privacy, inclusion, financing, and long-term maintenance challenges that often determine project success. The findings demonstrate that sustainable smart cities require not only advanced technologies but also institutional coordination and citizen-centered planning. The proposed model provides a practical pathway for emerging and developed urban regions seeking resilient and low-carbon futures.
Advanced Nanomaterial-Based Solid-State Batteries for High-Density Energy Storage in Electric Mobility Applications
The rapid growth of electric mobility has intensified the need for safer, lighter, and higher-capacity energy storage systems than conventional lithium-ion batteries can reliably provide. Solid-state batteries have emerged as a promising alternative because they replace flammable liquid electrolytes with solid ion-conducting materials, thereby improving thermal stability and enabling the use of high-energy-density electrodes. This paper investigates the role of advanced nanomaterials in accelerating the commercial readiness of solid-state batteries for electric mobility applications. Particular attention is given to nanoscale ceramic electrolytes, polymer-ceramic composites, engineered anodes, cathode interface coatings, and three-dimensional ion transport architectures. The study reviews performance limitations in present battery systems, including dendrite formation, interface resistance, slow ion mobility, and manufacturing complexity. A materials-centered framework is proposed to address these barriers through nanoparticle dispersion, grain boundary engineering, surface functionalization, and scalable fabrication methods. Comparative simulations indicate that optimized nanostructured solid-state systems can deliver higher gravimetric energy density, longer cycle life, and faster charging capability while maintaining improved safety margins. Economic and sustainability implications for electric vehicles, buses, and light commercial fleets are also discussed. The paper concludes that nanomaterial-enabled solid-state batteries represent a strategic pathway toward next-generation mobility ecosystems, provided that cost reduction, supply-chain resilience, and mass manufacturing challenges are systematically resolved.
AI-Integrated Digital Twin Architectures for Predictive Maintenance and Energy Optimization in Smart Manufacturing Systems
Digital transformation in manufacturing has accelerated the adoption of cyber-physical systems, industrial internet of things, and data-driven automation. Among these developments, digital twin architectures have emerged as a strategic framework for synchronizing physical assets with virtual replicas in real time. This paper proposes an AI-integrated digital twin architecture for predictive maintenance and energy optimization in smart manufacturing systems. The model combines sensor networks, edge gateways, cloud analytics, machine learning prediction engines, and reinforcement-learning control loops to reduce unplanned downtime and improve energy efficiency. A multi-layer architecture is presented comprising perception, communication, twin modeling, intelligence, and decision layers. Predictive maintenance is enabled through anomaly detection, remaining useful life estimation, and fault classification using hybrid learning models. Energy optimization is achieved through dynamic scheduling, load balancing, and adaptive machine parameter control. Experimental simulations based on a medium-scale production line demonstrate reductions in maintenance cost, machine idle time, and electricity consumption while improving overall equipment effectiveness. The proposed framework also addresses interoperability, scalability, cybersecurity, and deployment constraints faced by small and medium enterprises. Results indicate that integrating artificial intelligence into digital twins can convert manufacturing plants from reactive operations to autonomous and self-optimizing environments. The study contributes a practical roadmap for industries pursuing Industry 4.0 and Industry 5.0 transitions while maintaining sustainability and operational resilience.
Quantum Computing Applications in Engineering Optimization Solving Complex Multi-Variable Industrial Problems
The Quantum computing has emerged as a disruptive paradigm with the potential to revolutionize engineering optimization by addressing computationally intractable problems. Classical optimization techniques often struggle with large-scale, multi-variable industrial problems due to exponential complexity and computational constraints. This paper explores the application of quantum computing algorithms, including quantum annealing and variational quantum algorithms, for solving complex engineering Optimisation tasks. A hybrid quantum-classical framework is proposed to integrate quantum processors with classical computational systems for enhanced performance. The study focuses on applications in supply chain optimization, energy systems, and manufacturing processes. Simulation results demonstrate that quantum-inspired algorithms significantly improve solution quality and computational efficiency compared to traditional methods. The framework leverages qubit superposition and entanglement to explore large solution spaces simultaneously, enabling faster convergence to optimal solutions. Furthermore, the integration of machine learning techniques enhances adaptability and scalability. The findings highlight the potential of quantum computing in transforming engineering optimization and industrial decision-making. This research contributes to the advancement of next-generation computational methods capable of solving complex real-world problems efficiently.
Human-Centric Robotics and Collaborative AI Systems for Adaptive Manufacturing and Healthcare Applications
The integration of human-centric robotics and collaborative artificial intelligence (AI) systems is transforming modern manufacturing and healthcare environments. Unlike traditional automation, human- centric systems emphasize interaction, adaptability, and safety, enabling seamless collaboration between humans and robots. This paper proposes an advanced framework for collaborative robotic systems that integrates AI-driven perception, real-time decision-making, and adaptive control mechanisms. The framework utilizes machine learning algorithms, computer vision, and sensor fusion to enable robots to understand human intentions and dynamically adjust their actions. In manufacturing, the system enhances productivity by enabling flexible and adaptive workflows, while in healthcare, it supports assistive robotics and precision interventions. Experimental simulations demonstrate improved task efficiency, reduced human workload, and enhanced safety in collaborative environments. The results indicate that AI-driven collaborative systems significantly outperform conventional robotic systems in dynamic and unstructured settings. Furthermore, the integration of explainable AI improves system transparency and trust in human-robot interactions. This research contributes to the development of intelligent, safe, and adaptive robotic systems aligned with Industry 5.0 principles, emphasizing human-machine collaboration and sustainable innovation.
Advanced Nanomaterials for High-Efficiency Energy Storage in Next-Generation Lithium-Ion and Solid-State Batteries
The increasing demand for high-performance energy storage systems has accelerated research into advanced nanomaterials for next-generation lithium-ion and solid-state batteries. Conventional battery technologies face limitations in energy density, cycle stability, and safety, necessitating the development of innovative material solutions. This paper investigates the role of nanostructured materials, including graphene, silicon nanocomposites, and solid electrolytes, in enhancing battery performance. A comprehensive framework is proposed for integrating nanomaterials into electrode and electrolyte design to achieve higher conductivity, improved ion transport, and structural stability. The study employs experimental data analysis and simulation models to evaluate the electrochemical performance of these materials. Results indicate that nanostructured electrodes significantly enhance charge capacity and cycle life, while solid-state electrolytes improve safety by eliminating leakage and thermal instability. Comparative analysis demonstrates that the proposed nanomaterial-based systems achieve higher energy density and longer lifespan compared to conventional lithium-ion batteries. The findings highlight the potential of nanotechnology in advancing energy storage solutions for electric vehicles, renewable energy systems, and portable electronics. This research contributes to the development of safer, more efficient, and sustainable battery technologies.
Sustainable Smart Cities: Integrating IoT, Edge Computing, and Renewable Energy for Climate- Resilient Urban Infrastructure
The rapid pace of urbanization has intensified challenges related to energy consumption, environmental degradation, and infrastructure resilience. Sustainable smart cities have emerged as a strategic solution to address these issues by integrating advanced technologies such as the Internet of Things (IoT), edge computing, and renewable energy systems. This paper proposes a comprehensive framework for climate-resilient urban infrastructure that leverages real-time data acquisition, decentralized computing, and intelligent energy management. The framework integrates IoT-enabled sensors for monitoring environmental and infrastructural parameters, while edge computing ensures low-latency data processing and efficient resource utilization. Renewable energy sources, including solar and wind systems, are incorporated into the urban grid to enhance sustainability and reduce carbon emissions. A case study simulation demonstrates improved energy efficiency, reduced operational costs, and enhanced system resilience under varying climatic conditions. The results indicate that the integration of IoT, edge computing, and renewable energy significantly improves urban sustainability and disaster preparedness. This research contributes to the development of intelligent, adaptive, and resilient urban ecosystems aligned with global sustainability goals and climate action strategies.
AI-Driven Digital Twin Systems for Predictive Maintenance and Autonomous Industrial Optimization in Industry 5.0
The emergence of Industry 5.0 introduces a paradigm shift toward human-centric, sustainable, and intelligent industrial systems. In this evolving landscape, Artificial Intelligence (AI)-driven Digital Twin (DT) systems have become critical for predictive maintenance and autonomous industrial optimization. This paper proposes a comprehensive multi-layered digital twin framework integrating Internet of Things (IoT) sensors, edge-cloud computing, and machine learning algorithms for real-time system monitoring and predictive analytics. The digital twin replicates physical assets dynamically, enabling high-fidelity simulation, anomaly detection, and decision support. The proposed framework incorporates deep learning- based predictive models and adaptive feedback mechanisms to enhance system accuracy and resilience. A case study involving robotic assembly lines demonstrates that the system significantly improves fault prediction accuracy, reduces downtime, and optimizes resource utilization. Comparative analysis with conventional maintenance approaches shows up to 35% reduction in maintenance costs and improved operational reliability. The integration of edge computing ensures low latency, while cloud platforms enable scalable data processing. The findings confirm that AI-enabled digital twin systems are key enablers of autonomous and resilient industrial ecosystems in Industry 5.0. This research contributes to advancing smart manufacturing through intelligent, self-optimizing systems capable of real-time adaptation and decision-making.
Next-Generation 6G Networks: Integrating Terahertz Communication with Intelligent Reflecting Surfaces for Ultra-Low Latency
The evolution of wireless communication technologies has reached a critical juncture with the limitations of fifth-generation (5G) networks becoming increasingly evident in supporting ultra-high data rates, massive connectivity, and ultra-low latency applications. This paper explores the development of next- generation sixth-generation (6G) networks through the integration of terahertz (THz) communication and intelligent reflecting surfaces (IRS). Terahertz communication offers unprecedented bandwidth, enabling data rates in the terabit-per-second range, while IRS technology enhances signal propagation by dynamically controlling the wireless environment. The proposed framework combines these technologies with artificial intelligence-driven optimization techniques to achieve efficient spectrum utilization and improved network performance. A comprehensive system model is developed to evaluate the performance of THz-IRS integrated networks under varying conditions. Simulation results demonstrate significant improvements in spectral efficiency, reduced latency, and enhanced signal reliability compared to conventional systems. The study also addresses challenges such as signal attenuation, hardware limitations, and energy efficiency. The findings highlight the transformative potential of integrating THz communication and IRS technologies in enabling future wireless networks capable of supporting advanced applications such as holographic communication, autonomous systems, and extended reality.
High-Performance Solid-State Batteries Using Nanostructured Electrolytes for Electric Vehicle Applications
The rapid growth of electric vehicles (EVs) has intensified the demand for advanced energy storage systems with higher energy density, improved safety, and longer lifespan. Conventional lithium-ion batteries, while widely used, face limitations such as thermal instability, limited energy capacity, and safety concerns due to liquid electrolytes. This paper investigates the development of high-performance solid-state batteries (SSBs) utilizing nanostructured electrolytes for electric vehicle applications. The study focuses on the design, synthesis, and optimization of solid electrolytes with enhanced ionic conductivity and mechanical stability. Nanostructuring techniques are employed to improve ion transport pathways and reduce interfacial resistance between electrodes and electrolytes. Advanced characterization methods and electrochemical testing are used to evaluate battery performance. The results demonstrate significant improvements in energy density, charge-discharge efficiency, and thermal stability compared to conventional systems. The proposed approach also enhances safety by eliminating flammable liquid components. Challenges related to scalability, manufacturing complexity, and cost are discussed, along with potential solutions. The findings highlight the transformative potential of nanostructured solid-state batteries in advancing electric vehicle technology and supporting the transition toward sustainable transportation systems.
Autonomous Cyber-Physical Systems for Smart Cities: A Machine Learning Approach to Real- Time Traffic and Energy Optimization
The rapid urbanization of modern societies has necessitated the development of intelligent infrastructures capable of managing complex urban systems efficiently. Smart cities leverage advanced technologies such as cyber-physical systems (CPS), Internet of Things (IoT), and machine learning to optimize critical services including transportation and energy management. This paper presents a comprehensive framework for autonomous cyber-physical systems designed to optimize real-time traffic flow and energy consumption in smart cities. The proposed system integrates distributed sensors, edge computing, and machine learning algorithms to enable dynamic decision-making and adaptive control. Traffic optimization is achieved through predictive modeling and intelligent signal control, while energy optimization focuses on demand forecasting and load balancing. Simulation results demonstrate significant improvements in traffic efficiency, reduced congestion, and enhanced energy utilization. The system achieves up to 35% reduction in traffic delays and 28% improvement in energy efficiency. The study also addresses challenges related to scalability, data privacy, and system integration. The findings highlight the potential of autonomous CPS in transforming urban infrastructure, contributing to sustainable and resilient smart city development.
Sustainable Hydrogen Production via Advanced Nanocatalysts: Design, Optimization, and Industrial Scalability
The growing demand for clean and sustainable energy has positioned hydrogen as a promising alternative to fossil fuels due to its high energy density and zero carbon emissions at the point of use. However, large-scale hydrogen production remains constrained by high costs and inefficiencies associated with conventional methods such as steam methane reforming and electrolysis. This paper investigates the role of advanced nanocatalysts in enhancing the efficiency, sustainability, and scalability of hydrogen production systems. The study focuses on the design and optimization of nanostructured catalysts for water splitting and thermochemical processes, emphasizing their surface properties, catalytic activity, and durability. Various synthesis techniques, including sol-gel methods, hydrothermal synthesis, and chemical vapor deposition, are analyzed to understand their impact on catalyst performance. The integration of machine learning-based optimization techniques is also explored to improve catalyst design and process efficiency. Experimental and simulation-based results demonstrate significant improvements in hydrogen yield, energy efficiency, and cost reduction. The findings highlight the potential of nanocatalysts to revolutionize hydrogen production and facilitate the transition toward a sustainable energy economy. Challenges related to scalability, material stability, and economic feasibility are also discussed, providing insights for future research and industrial implementation.
AI-Driven Predictive Maintenance in Smart Manufacturing Using Edge-Enabled Digital Twin Architectures
The increasing complexity of modern manufacturing systems under the Industry 4.0 paradigm has necessitated the adoption of intelligent maintenance strategies capable of enhancing system reliability and reducing operational costs. This paper presents an AI-driven predictive maintenance framework utilizing edge-enabled digital twin architectures for smart manufacturing environments. The proposed model integrates Internet of Things (IoT) sensors for real-time data acquisition, edge computing for low-latency processing, and digital twin technology for dynamic system modeling. Advanced machine learning techniques, including hybrid deep learning models, are employed to predict equipment failures with high precision. The system reduces dependency on cloud infrastructure by enabling localized decision-making at the edge, thereby improving response time and data security. Experimental evaluation demonstrates that the proposed framework achieves prediction accuracy exceeding 96%, reduces unplanned downtime, and enhances maintenance efficiency. The digital twin provides continuous synchronization between physical assets and virtual replicas, facilitating real-time monitoring and simulation. The study addresses key challenges such as scalability, interoperability, and cybersecurity, offering a comprehensive solution for next-generation industrial systems. The results highlight the transformative potential of integrating AI, edge computing, and digital twins in predictive maintenance applications.
Hydrogen Energy Storage Systems: Design Optimization and Performance Analysis for Future Power Networks
Hydrogen energy storage has emerged as a promising solution to address intermittency challenges associated with high penetration of renewable energy sources in modern power networks. This paper presents a comprehensive investigation of hydrogen-based energy storage systems, focusing on system design optimization, dynamic modeling, and performance evaluation under grid-connected conditions. The study examines electrolyzer-fuel cell integration, storage technologies, and power conversion interfaces. Mathematical modeling and simulation-based analysis demonstrate improved grid stability, enhanced renewable utilization, and reduced curtailment losses. The results indicate that optimized hydrogen storage configurations significantly contribute to long-term energy reliability and decarbonization objectives in future power systems.
Cyber-Physical Systems Integration in Autonomous Industrial Robotics for Smart Manufacturing Environments
The integration of Cyber-Physical Systems (CPS) in autonomous industrial robotics represents a transformative advancement in smart manufacturing environments. CPS frameworks enable seamless interaction between physical robotic systems and computational intelligence layers through embedded sensors, communication networks, and real-time data analytics. This paper presents a comprehensive study on CPS-based autonomous robotics architecture, focusing on system modeling, control synchronization, data-driven optimization, and security frameworks. A multi-layer CPS architecture incorporating edge computing, machine learning algorithms, and industrial Internet of Things (IIoT) platforms is proposed. Experimental validation through simulation and prototype implementation demonstrates improved operational efficiency, reduced latency, enhanced fault tolerance, and predictive maintenance capability. The proposed approach reduces production downtime by 27% and enhances task precision by 18% compared to conventional automation frameworks. Furthermore, cybersecurity risks and mitigation strategies in CPS-integrated robotic systems are analyzed. The findings highlight CPS as a foundational enabler for Industry 4.0 and future autonomous manufacturing ecosystems.
Advanced Additive Manufacturing Techniques for Multi-Material Functional Engineering Components
Additive manufacturing has evolved from rapid prototyping to a transformative production technology capable of fabricating complex functional components. The development of multi-material additive manufacturing techniques enables the integration of diverse material properties within a single structure, enhancing performance, functionality, and design flexibility. This paper presents a comprehensive investigation into advanced additive manufacturing processes for multi-material engineering components. It analyzes material compatibility, process optimization, interfacial bonding behavior, and mechanical performance evaluation. Experimental modeling and simulation-based analysis demonstrate improved structural efficiency and functional integration. The study highlights manufacturing challenges, process control requirements, and future directions for scalable industrial implementation.
AI-Based Structural Health Monitoring Systems for Resilient and Sustainable Infrastructure
Structural deterioration and unexpected failures in infrastructure systems pose significant risks to public safety, economic stability, and environmental sustainability. Artificial intelligence has emerged as a transformative tool for structural health monitoring by enabling data-driven damage detection, condition assessment, and predictive maintenance. This paper presents an integrated AI-based structural health monitoring framework designed to enhance infrastructure resilience and sustainability. The proposed system combines sensor networks, machine learning algorithms, and real-time data analytics to identify structural anomalies and predict failure progression. Analytical evaluation demonstrates improved detection accuracy, reduced inspection costs, and enhanced lifecycle performance compared to traditional monitoring techniques.
Blockchain-Enabled Secure Architecture for Smart Grid Energy Transactions and Decentralized Power Management
The modernization of power systems through smart grids introduces bidirectional energy flows, distributed generation, and prosumer participation, creating new cybersecurity and transaction management challenges. This paper proposes a blockchain-enabled secure architecture for smart grid energy transactions and decentralized power management. The framework integrates distributed ledger technology, smart contracts, and consensus mechanisms to enable transparent, tamper-resistant, and automated peer-to-peer energy trading. A layered architecture is developed, incorporating IoT-based metering, cryptographic authentication, and decentralized control logic. Performance analysis demonstrates enhanced security, reduced transaction overhead, and improved trust among participants, supporting scalable and resilient decentralized energy ecosystems.
Design and Implementation of Bioinspired Soft Robotic Systems for Adaptive Interaction in Unstructured Human Environments
Robotic systems designed for human-centered applications increasingly operate in unstructured and dynamically changing environments, where rigid-bodied robots face limitations in adaptability, safety, and physical compliance. Bioinspired soft robotics has emerged as a promising paradigm that draws inspiration from biological organisms to achieve flexible, resilient, and adaptive interaction capabilities. This paper presents the design, fabrication, control, and experimental validation of a bioinspired soft robotic system intended for adaptive interaction in unstructured human environments. The proposed system integrates elastomeric actuators, distributed soft sensors, and morphology-driven intelligence to enable safe physical interaction and environmental adaptability. A hybrid control framework combining model-based control and machine learning techniques is developed to manage nonlinear material behavior and uncertain external conditions. Experimental evaluations demonstrate the robot’s ability to conform to irregular objects, adapt locomotion strategies, and maintain stable interaction with human users. Performance metrics including compliance, response time, adaptability, and interaction safety are analyzed and compared with conventional rigid robotic systems. The results confirm that bioinspired soft robotic architectures significantly enhance adaptability and safety in complex environments. This research contributes to the advancement of next-generation robotic systems for healthcare, service robotics, and human–robot collaboration.
AI-Driven Real-Time Optimization of Distributed Renewable Energy Grids for Demand-Response and Stability in Smart Cities
The rapid integration of distributed renewable energy resources such as solar photovoltaic systems and wind turbines has transformed conventional power grids into complex, decentralized energy networks. While this transition supports sustainability goals, it introduces significant challenges related to intermittency, grid stability, and demand–supply imbalance, particularly in urban smart city environments. This paper presents an artificial intelligence–driven real-time optimization framework for distributed renewable energy grids aimed at enhancing demand-response capability and operational stability. Machine learning algorithms, including deep neural networks and reinforcement learning, are employed to forecast energy generation, predict demand patterns, and dynamically control distributed energy resources. The proposed framework is evaluated using a simulated smart city microgrid incorporating renewable generation, energy storage systems, and flexible loads. Performance metrics such as frequency deviation, voltage stability, energy loss reduction, and demand-response efficiency are analyzed under varying operating conditions. Results demonstrate that the AI-driven approach significantly improves grid resilience, reduces peak demand stress, and enhances renewable energy utilization compared to conventional rule-based control strategies. The findings confirm the potential of artificial intelligence as a critical enabler for intelligent energy management in future smart cities, supporting reliable, efficient, and sustainable power systems.
Development of Self-Healing Ultra-High- Performance Concrete Using Smart Nanocapsules for Enhanced Structural Resilience
Ultra-high-performance concrete (UHPC) is widely recognized for its superior mechanical strength and durability; however, its susceptibility to microcrack formation under mechanical and environmental loading poses a critical challenge to long-term structural resilience. This study presents the development of a self-healing UHPC system incorporating smart nanocapsules encapsulating a sodium silicate–based healing agent. The nanocapsules are synthesized using an in-situ polymerization technique and uniformly dispersed within the UHPC matrix without compromising workability. Mechanical performance, crack-healing efficiency, permeability resistance, and microstructural evolution are evaluated through compressive and flexural strength tests, water absorption measurements, scanning electron microscopy, and X-ray diffraction analysis. Experimental results indicate that the self-healing UHPC exhibits up to 85% recovery in flexural strength and significant crack closure for cracks up to 300 µm. The presence of nanocapsules enhances durability by reducing chloride penetration and water permeability. Microstructural observations confirm the formation of calcium silicate hydrate gels within healed cracks. The proposed smart nanocapsule-enabled UHPC demonstrates strong potential for autonomous damage mitigation, reduced maintenance requirements, and extended service life of critical infrastructure, contributing to sustainable and resilient construction practices.
Machine Learning-Driven Discovery and Optimization of Eco-Friendly Construction Materials
The construction sector accounts for a substantial portion of global carbon emissions and resource exploitation, necessitating the development of eco-friendly materials that balance performance and sustainability. Traditional experimental approaches for material innovation are time-intensive and expensive, often limiting the pace of sustainable breakthroughs. This research develops a machine learning (ML)-based framework for identifying and optimizing eco-friendly construction materials by correlating compositional attributes and processing parameters with performance and environmental outcomes. Supervised learning models — including random forest regression, gradient boosting machines, and deep neural networks were trained on an extensive dataset compiled from peer-reviewed studies, encompassing over 600 material formulations. Target outcomes included compressive strength and embodied carbon indices, while predictors consisted of mix design variables and curing conditions. Gradient boosting demonstrated superior predictive performance (R² > 0.91), while feature analysis revealed the influence of supplementary cementitious materials and water/binder ratio on both performance and sustainability. Case evaluations illustrated optimized mixtures with comparable mechanical properties to conventional concrete but with 20– 35% lower environmental impact. This work highlights machine learning’s potential to streamline sustainable material design and reduce reliance on iterative physical experimentation. Future efforts will integrate multi-objective optimization and extend to broader classes of bio-based and recycled materials.
Advanced Renewable Energy Conversion Systems: Modeling, Control, and Grid Integration Challenges
The global transition toward low-carbon energy systems has intensified the deployment of renewable energy sources such as solar, wind, and biomass. While renewable energy conversion systems offer environmental and economic benefits, their integration into existing power grids presents significant technical challenges due to intermittency, nonlinearity, and grid stability constraints. This paper presents a comprehensive analysis of advanced renewable energy conversion systems with a focus on modeling, control strategies, and grid integration issues. Mathematical and dynamic models for photovoltaic and wind energy conversion systems are discussed to capture system behavior under variable operating conditions. Advanced control techniques, including model predictive control and adaptive control, are examined for improving energy extraction efficiency and maintaining system stability. The paper further analyzes grid integration challenges such as voltage regulation, frequency stability, and power quality in high renewable penetration scenarios. A conceptual grid-connected renewable system is evaluated to illustrate the effectiveness of advanced control approaches. The study highlights the importance of coordinated control, energy storage integration, and smart grid technologies in addressing grid reliability concerns. This research contributes to the understanding of renewable energy system behavior and provides insights into overcoming technical barriers for large-scale renewable energy deployment.
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