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169 articles for “grid”
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An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study
Abstract: As the Earth’s climate enters a state of unprecedented volatility, the traditional methods of ecological observation—characterized by delayed reporting and fragmented data—are no longer sufficient. This study investigates the paradigm shift toward AI-driven IoT (KSK Approach)-based decision-making frameworks as the primary frontier in climate science. By deploying a "planetary nervous system" of interconnected sensors—measuring everything from soil moisture in the Sahel to glacial melt rates in the Arctic—we generate a …
Published in International Journal of Climate Conditions · Vol. 3, Issue 1, 2026 · pp. 1–10 Read article
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Advanced Solid-State Battery Technologies for Sustainable Energy Storage
Abstract: The increasing global demand for sustainable and high-performance energy storage systems has accelerated research in advanced battery technologies. Conventional lithium-ion batteries, while widely used in portable electronics, electric vehicles, and grid storage systems, face limitations such as safety risks, limited energy density, slow charging rates, and degradation over repeated cycles. To overcome these challenges, advanced solid-state battery technologies have emerged as a promising solution for next-generation energy storage applications. This …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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Autonomous 6G Physical Layer Architectures for Space-Air-Ground Integrated Networks
Abstract: The emergence of sixth generation (6G) wireless systems calls for a significant shift away from conventional deterministic communication models. As communication infrastructures evolve into Space- Air-Ground Integrated Networks (SAGIN), traditional physical layer (PHY) techniques struggle to operate effectively under the severe Doppler effects and long propagation delays associated with space environments. This paper examines the role of artificial intelligence embedded directly within the 6G transceiver architecture to enable ultra-reliable and …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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Battery Energy Storage Optimization for Enabling Black Start in Offshore Wind Farms
Abstract: The transition toward power systems dominated by renewable energy sources (RES) introduces significant challenges to the provision of black start services. Black start units are essential for restoring power following a system-wide outage; however, the gradual retirement of conventional generation reduces the availability of such resources, potentially compromising grid resilience. Offshore wind farms (OWFs) are increasingly considered as viable alternatives for black start support. Nevertheless, their inherent variability and intermittent …
Published in Trends in Electrical Engineering · Vol. 16, Issue 2, 2025 Read article
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Bird-Tree Associations and Co-occurrence Patterns in Delhi: Implications for Urban Ecological Restoration and Invasive Species Management
Abstract: Urban trees and birds are intertwined indicators of ecological resilience in cities. This study maps citywide bird-tree interactions in Delhi, India, hosting more than 300 avian species. The city was divided into hexagonal grids, and bird-tree data (506 interactions) were collected through field surveys across 131 sites using standardised 1-kilometre transects. Native keystone species like ficus supported the greatest bird diversity, while invasive Prosopis juliflora was widely used for perching …
Published in Research & Reviews : Journal of Ecology · Vol. 16, Issue 2, 2026 Read article
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Solar Panel Defect Detection Using Geospatially-Aware Deep Learning framework
Abstract: Large-scale photovoltaic (PV) systems demand reliable inspection techniques to maintain efficiency, as manual methods remain labor-intensive and inconsistent. This study introduces a geospatially informed deep learning framework for defect detection and localization in PV panels from drone and satellite imagery. The framework incorporates an adaptive tiling mechanism that adjusts tile boundaries according to object size, reducing information loss and enhancing detection performance. In addition, coordinate transformation between image pixels and …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach
Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article
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Genomic Selection for Grain Yield in Wheat Using Machine Learning on DArT Molecular Markers: A Comparative Evaluation Across Multi-Environment Trials
Abstract: Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction
Abstract: In an earlier comparative study of machine-learning methods for genomic prediction of wheat grain yield, we reported a counter-intuitive result: automated nested-cross-validation tuning of the Lasso regularization penalty reduced mean predictive ability relative to a fixed, arbitrarily chosen penalty (mean Pearson r falling from 0.408 to 0.349 across four environments), the opposite of the expected effect of hyperparameter tuning. We hypothesized two possible explanations at the time — high-variance penalty …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article