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392 articles for “artificial environment”
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Innovations in Civil Engineering: Advancing Infrastructure through AI Technology
Abstract: A comprehensive exploration of the intersection of civil engineering and artificial intelligence (AI) technology, highlighting the transformative impact of AI on infrastructure development, management, and sustainability. The journal encompasses a wide array of research articles, case studies, and reviews that demonstrate the integration of AI into various facets of civil engineering, including but not limited to smart infrastructure, predictive maintenance, structural analysis, and urban planning. By showcasing cutting-edge applications and …
Published in Journal of Structural Engineering and Management · Vol. 11, Issue 2, 2024 · pp. 1–11 Read article
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Symmetry Principles in Digital Twin Systems: Modeling, Integration, and Applications
Abstract: This systematic review comprehensively examines the burgeoning field of Digital Twin (DT) technology, analyzing its current state, diverse applications, and future trajectory. Through a rigorous methodology involving a systematic search of academic databases and relevant industry literature, this review synthesizes findings across a spectrum of disciplines. We identify the core components and underlying principles of DTs, distinguishing them from traditional simulation models by their dynamic, realtime data integration and bi-directional …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 06–24 Read article
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Impact of various Fibers on the Mechanical and Durability Performance of Fibre-Reinforced Concrete with SBR latex
Abstract: Recent years included a proliferation of research efforts focused on fibre reinforced concrete (FRC), an innovation that perceives extensive application in the construction industry. FRC's enhanced mechanical properties ahead of conventional concrete have broadened its significance recently. Natural fibers have been produced in response to expanding environmental deterioration in recent years, and research is currently continuing with the goal of utilizing them in the construction field. This study explores the …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 26–40 Read article
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Library Science Education in India: Evolution, Emerging Trends, and Challenges
Abstract: Library and Information Science (LIS) education in India has undergone significant transformation over the past century, evolving from traditional library training to a dynamic academic discipline shaped by technological advancements and changing information needs. This study examines the origin, development, and current status of LIS education in India, with particular emphasis on its evolution in the contemporary digital environment. The research adopts a qualitative and exploratory approach and is based …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 75–87 Read article
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Facial Emotion Detection and Its Applications
Abstract: Facial emotion detection (FED) is an interdisciplinary field that integrates artificial intelligence, computer vision, and machine learning to recognize and interpret human emotions based on facial expressions. The development of FED systems has been propelled by advancements in deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which enhance recognition accuracy. Feature extraction techniques, including geometric and appearance-based methods, play a crucial role in classifying emotional states. …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 8–12 Read article
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Electronic Drones: Technology, Applications, and Future Directions
Abstract: Electronic drones, commonly referred to as Unmanned Aerial Vehicles (UAVs), have transitioned from exclusively military platforms to indispensable tools across commercial, scientific, industrial, and recreational domains. The rapid evolution of electronics, flight control systems, communication networks, onboard sensors, and artificial intelligence has reshaped drone capabilities, enabling high-precision remote sensing, autonomous navigation, swarm behavior, and integration into complex systems like the Internet of Drones (IoD). This paper examines the technological building …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 15–19 Read article
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Adaptive Task Scheduling and Resource Optimization Using AI Middleware
Abstract: Modern distributed and heterogeneous computing systems face significant challenges in dealing with dynamically changing workloads, resource fragmentation, and changing latencies; existing traditional, or rule-based, specialized schedulers are no longer useful in achieving the best system performance. Such limitations highlight the importance of the adaptive scheduling paradigms that can learn, forecast, and react to the actual real-world conditions in the system. Artificial intelligence middleware is also an attractive solution to this …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 23–31 Read article
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Artificial Intelligence and Edge Computing in Oil and Gas: Applications, Architectures, and Operational Realities
Abstract: Artificial intelligence has arrived in oil and gas, and unlike some previous waves of digital enthusiasm in the sector, this one is sticking. Saudi Aramco analyses approximately 10 billion data point every day and reported USD 4 billion in technology-driven operational gains in 2024. ExxonMobil uses AI to increase shale well output by more than 5 percent. Shell has deployed machine learning across more than 10,000 assets using C3.ai to …
Published in Journal of Petroleum Engineering & Technology · Vol. 16, Issue 2, 2026 · pp. 01–06 Read article
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Advances in Simulation and Surgical Skill Training Evolution: Narrative Integrative Review
Abstract: Simulation-based education has emerged as a cornerstone of contemporary general surgery training, driven by increasing emphasis on patient safety, competency-based education, and rapid technological innovation. Traditional apprenticeship models, while foundational, are constrained by reduced operative exposure, work-hour limitations, and variability in clinical case mix. In this context, simulation provides a structured, reproducible, and safe environment for acquisition, assessment, and refinement of surgical skills across the training continuum. This narrative review …
Published in Research and Reviews : Journal of Surgery · Vol. 15, Issue 1, 2026 · pp. 7–13 Read article
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Green AI-Enabled Opto-Electronic Communication Systems for Carbon-Neutral Digital Networks
Abstract: The rapid expansion of digital communication infrastructure, driven by cloud computing, Internet of Things (IoT), 6G networks, and artificial intelligence applications, has significantly increased the energy consumption and carbon footprint of modern communication systems. Conventional optical communication networks often rely on static resource allocation and energy-intensive signal processing mechanisms, resulting in inefficient utilization of network resources and elevated operational costs. This study proposes a Green Artificial Intelligence (Green AI)-Enabled Opto-Electronic …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 Read article
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A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture
Abstract: Agriculture is undergoing a digital transformation driven by machine learning (ML) and artificial intelligence. The integration of ML techniques with data from sensors, drones, satellites, and IoT devices has enabled precision agriculture, early disease detection, optimized resource use, and improved yield prediction. This paper presents a comprehensive review of machine learning applications in modern agriculture, covering key areas such as crop monitoring, soil analysis, irrigation scheduling, pest, and disease detection, …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 1, 2026 · pp. 39–45 Read article
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Dielectric Elastomers in Actuation and Energy Applications: Material Behavior and Design Strategies
Abstract: Dielectric elastomers (DEs), a class of electroactive polymers, have attracted significant attention for their ability to undergo large, reversible deformations under electric stimulation. This unique capability makes them highly suitable for a range of actuation and energy harvesting applications, especially in the emerging fields of soft robotics, flexible electronics, artificial muscles, and sustainable power generation systems. DEs offer compelling advantages such as low weight, mechanical flexibility, high energy density, and …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 3, Issue 1, 2025 · pp. 13–18 Read article
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Ethical Consideration in the Use of Artificial Intelligence in Medicine and Healthcare
Abstract: Artificial Intelligence (AI) in medicine and healthcare offers tremendous potential for improving patient care, increasing the precision of diagnoses, and increasing operational efficiency. To ensure responsible application, however, the swift uptake of AI technologies also brings up important ethical concerns that need to be addressed. This article explores various ethical challenges in healthcare AI, including concerns about algorithmic bias, data privacy, informed consent, and accountability. Patients must be aware of …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 1, 2025 · pp. 23–28 Read article
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Seasonal Dynamics of Coastal Landscapes: A Critical Review Using Remote Sensing and GIS
Abstract: Coastal landscapes are among the most dynamic environments on Earth, undergoing continuous transformation due to both natural processes and anthropogenic activities. In India, particularly along the southern coastal regions of Andhra Pradesh, Tamil Nadu, and Kerala, shoreline morphology and sediment transport patterns are significantly influenced by seasonal monsoons, cyclones, storm surges, waves, tides, and changing river discharges. These factors contribute to varying rates of coastal erosion, accretion, inundation, and land- …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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Enhanced Sustainable Concrete Mix Design Using LLMs and Advanced Machine Learning Techniques
Abstract: Large Language Models (LLMs) are emerging as transformative tools in materials science, offering human-like reasoning, zero-shot problem solving, and the ability to integrate fuzzy laboratory knowledge with structured data. This study extends and reinterprets the original systematic benchmark for using LLMs in sustainable concrete design, particularly for Alkali-Activated Concrete (AAC). We introduce an enhanced, multi-model framework combining LLM-based inverse design, Random Forest regression, Gaussian Process Regression (GPR), and a lightweight …
Published in Recent Trends in Civil Engineering & Technology · Vol. 16, Issue 2, 2026 · pp. 26–33 Read article
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Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Comparative Analysis of Energy Storage System’s Hybridization for Electric Vehicles: Evaluating Lithium-Ion Batteries, Supercapacitors, and Fuel Cells on Performance Metrics
Abstract: High-performance energy storage systems (ESS) in electrically powered cars are becoming more and more necessary as transportation options become more environmentally conscious. This research provides a thorough comparison of hybrid energy storage systems (HESS) that link fuel cell technology, supercapacitors, and batteries made of lithium ion. Critical performance metrics are assessed for each technology, including energy density, power density, efficiency, lifecycle durability, thermal performance, cost and sustainability. It summarizes the …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 3, Issue 1, 2025 · pp. 12–29 Read article
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Multi-layer Perceptron Ensemble for Estimation of Available Transfer Capability in Deregulated Environment
Abstract: The available transfer capability (ATC) estimation problem can be considered as a unique answered puzzle to be solved with highly nonlinear complexity of power systems and the uncertain state of the energy market. To implement the open access in deregulated markets, system operator should post ATC at a small and regular interval on openly accessible network. This forces the quick computation of ATC of transmission path. In these situations, the …
Published in Trends in Electrical Engineering · Vol. 6, Issue 1, 2016 · pp. 57–71 Read article
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Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article
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A Physics-Informed Graph Neural Network Framework for Real- Time Thermal-Aware Fault Prediction and Adaptive Power Optimization in Heterogeneous System-on-Chip Architectures
Abstract: Heterogeneous System-on-Chip (SoC) architectures are increasingly adopted in edge computing, artificial intelligence, autonomous systems, and high-performance embedded platforms due to their superior computational efficiency and flexibility. However, increasing integration density and workload diversity introduce severe thermal hotspots, accelerated device degradation, and unexpected hardware faults that adversely affect system reliability and energy efficiency. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for real-time thermal- aware fault prediction and adaptive …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 2, 2026 · pp. 19–28 Read article