STM Journals Peer-reviewed research from Consortium e-Learning Network

6 publications

  • Published Subscription Original Research

    Generative AI-Based Inverse Design of Sustainable Biodegradable Polymers with Target Mechanical and Thermal Properties

    By Darapu Uma, Manas Kumar Yogi, Pendyala Devi Sravanthi, N. Prasanthi

    Abstract: The escalating global plastic pollution crisis has intensified the urgent need for sustainable biodegradable polymer alternatives that can match or exceed the performance of conventional petroleum-based plastics while minimizing environmental impact. However, traditional polymer discovery approaches are severely constrained by high experimental costs, protracted development cycles spanning years, and fundamental inability to simultaneously optimize multiple conflicting material properties such as mechanical strength, thermal stability, and degradation kinetics. This study presents …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article

  • Published Subscription Original Research

    Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning

    By Palisetti Jhnana Prasuna Alekhya, Manas Kumar Yogi

    Abstract: The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the …

    Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 22–31 Read article

  • Published Subscription Review Article

    Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System

    By G.V. Rajeswari, Manas Kumar Yogi

    Abstract: Convolutional Neural Networks exhibit remarkable capabilities in flow pattern recognition, pressure drop prediction, leak detection, and system optimization through their ability to process complex spatial and temporal data patterns. The study examines CNN architectures specifically adapted for fluid dynamics applications, including data preprocessing techniques, feature extraction methods, and performance optimization strategies. Key applications include real-time flow monitoring, predictive maintenance, design parameter optimization, and anomaly detection in pipe networks. Comparative analysis …

    Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 1–9 Read article

  • Published Subscription Review Article

    Radiation-Resilient AI: Next-Generation Robotic Systems with Adaptive Machine Learning for Nuclear Facility Management

    By K.V.V. Subba Rao, Manas Kumar Yogi

    Abstract: The increasing complexity of nuclear facility operations, decommissioning activities, and emergency response scenarios necessitate the development of advanced autonomous systems capable of functioning in highly radioactive environments. This paper presents a comprehensive review of radiation-resilient artificial intelligence systems integrated with next-generation robotic platforms, specifically designed for nuclear facility management applications. We examine the convergence of adaptive machine learning algorithms, radiation-hardened hardware architectures, and intelligent robotic systems that can operate autonomously …

    Published in Journal of Thermal Engineering and Applications · Vol. 15, Issue 2, 2025 · pp. 12–21 Read article

  • Published Subscription Review Article

    Investigative Study of Adaptive Fault Tolerance in Optical Networks

    By Yamuna Mundru, Manas Kumar Yogi

    Abstract: Optical networks have become the backbone of modern telecommunications infrastructure, enabling high-speed data transmission across global networks. However, these networks face significant reliability challenges due to component failures, signal degradation, and environmental factors. This investigative study examines adaptive fault tolerance mechanisms in optical networks, focusing on emerging technologies and methodologies that enhance network resilience. The research analyzes various fault detection techniques, including machine learning-based approaches, self-healing protocols, and dynamic reconfiguration …

    Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 2, 2025 · pp. 24–30 Read article

  • Published Subscription Review Article

    Radiation-Resilient AI: Next-Generation Robotic Systems with Adaptive Machine Learning for Nuclear Facility Management

    By K.V.V. Subba Rao, Manas Kumar Yogi

    Abstract: The increasing complexity of nuclear facility operations, decommissioning activities, and emergency response scenarios necessitates the development of advanced autonomous systems capable of functioning in highly radioactive environments. This paper presents a comprehensive review of radiation-resilient artificial intelligence systems integrated with next-generation robotic platforms, specifically designed for nuclear facility management applications. We examine the convergence of adaptive machine learning algorithms, radiation-hardened hardware architectures, and intelligent robotic systems that can operate autonomously …

    Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 12–21 Read article

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