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229 articles for “augmented”
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In Vivo and In Vitro Antioxidative Efficacy of Naringenin on Cadmium -Induced Toxicity in Rats
Abstract: The aim of the study was to investigate the in vivo circulating antioxidants level such as vitamin C, vitamin E and GSH in Cadmium (Cd) induced toxic rats and the protective efficacy of naringenin via in vitro free radical scavenging assays. In this investigation cadmium chloride (5 mg/kg body weight (b.w) was administered orally (p.o) for 28 days to induce toxicity. Naringenin was pre-administered orally (50 mg/kg body weight) for …
Published in Research and Reviews: A Journal of Toxicology · Vol. 3, Issue 3, 2013 · pp. 9–16 Read article
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Energy and Performance Optimization of Airfoil Blade of Horizontal Axis Wind Turbine by CFD
Abstract: Wind Energy is a fast-developing type of intensity age the world over. This is to a limitedextent because of worries over worldwide environmental change and vitality security whileinterest for electrical vitality keeps on developing. Power request is proposed to develop at ayearly pace of 2.4% all around. Modern horizontal axis wind turbines have become afinancially reasonable type of clean and renewable power production. As a result, the windindustry has recently …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 11, Issue 3, 2020 · pp. 1–12 Read article
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Digital Therapeutics for Management of Chronic Diseases
Abstract: Digital therapeutics (DTx) mark a major advancement in modern medicine, using advanced software technologies to deliver clinically validated treatments for various physical and mental health conditions. Unlike general wellness apps that focus on fitness or lifestyle tracking, DTx are evidence-based medical interventions developed through rigorous clinical research to ensure safety, efficacy, and measurable outcomes. Delivered via digital platforms such as smartphones, tablets, or specialized medical devices, DTx aim to prevent, …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 2, 2026 Read article
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Digital Twins in Human Anatomy and Physiology Education: Current Status and Future Perspectives
Abstract: Digital Twin (DT) technology has emerged as a promising innovation in healthcare by creating dynamic virtual representations of physical systems through the integration of artificial intelligence (AI), computational modeling, real-time data, and advanced visualization technologies. Although DTs have been widely investigated in precision medicine and clinical decision-making, their application in Human Anatomy and Physiology (HAP) education remains in its early stages. This review aims to examine the current status, educational …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 3, 2026 Read article
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Toxicology 4.0: Integrating Artificial Intelligence, Big Data, Health Informatics, and Precision Analytics for Predictive Toxicity Assessment, Real-Time Toxicovigilance, and Personalized Patient Safety
Abstract: Background: Toxicology is undergoing a major transformation, increasingly described as Toxicology 4.0, driven by the integration of artificial intelligence (AI), big data analytics, health informatics, and precision analytics. Conventional toxicity testing is limited by high costs, lengthy timelines, and challenges in translating animal and low-throughput in vitro findings to humans. Aim and Objectives: To comprehensively evaluate the emerging role of Toxicology 4.0 in predictive toxicity assessment, real-time toxicovigilance, and personalized …
Published in Research and Reviews: A Journal of Toxicology · Vol. 16, Issue 2, 2026 Read article
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Graph Neural Networks for Molecular Scale Property Prediction and Inverse Design of Thermoset Polymer Nanocomposites: A Computational Framework
Abstract: Thermoset polymer nanocomposites exhibit properties that are highly sensitive to molecular scale formulation decisions, yet the vast design space remains largely unexplored because of the high cost of experimental characterisation and fully atomistic simulation. This paper presents TNC GNN, a dual mode graph neural network framework developed for the computational design of thermoset nanocomposite formulations. The forward module employs an attention augmented Message Passing Neural Network with 3D geometric encoding …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Mathematical Analysis and Stability Analysis of Coupled Orbital–Attitude Dynamics for Autonomous Spacecraft under Perturbative Forces
Abstract: Autonomous spacecraft operating in Earth orbit are subjected to coupled translational and rotational dynamics influenced by gravitational and environmental perturbations. Accurate mathematical characterization of these interactions is essential for trajectory prediction, attitude stabilization, autonomous navigation, and mission reliability. This study develops a nonlinear mathematical framework for coupled orbital–attitude dynamics of an autonomous spacecraft under perturbative forces. The translational dynamics are formulated using the two-body gravitational model augmented by the second …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 2, 2026 Read article
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Comparative Evaluation of Pedigree-Based and Genomic BLUP Models for Wheat Grain Yield Prediction
Abstract: Before genome-wide molecular markers became affordable, plant and animal breeding relied on pedigree-based prediction (P-BLUP), using the expected additive relationship matrix (A) derived from recorded ancestry. Genomic selection replaced or augmented this with a marker-derived genomic relationship matrix (G; GBLUP), and single-step methods combining A and G are now standard when only a subset of a breeding population is genotyped. It is less clear whether pedigree information retains any predictive …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 1–9 Read article
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Confident but Wrong: A Lifecycle Analysis of Hallucination in Large Language Models Understanding AI's Confidence Problem
Abstract: A Large Language Model (LLM) is likely to produce sentences that are fluent and confident in many instances, but completely wrong. In many cases a Large Language Model (LLM) will produce a fluent and confident sentence that is completely incorrect. This review tries to analyze this phenomenon using the most recent literature regarding natural language generation, computational learning theory and benchmark experiments. The argument is that it is not a …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article