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1198 articles for “computing”
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Molecular Docking Analysis of Phytocompounds from Ficus hispida Aagainst Epidermal Growth Factor Receptor Kinase Domain
Abstract: The Epidermal Growth Factor Receptor (EGFR) kinase domain plays a key role in cancer, as it promotes tumour growth and helps cancer cells survive, making it an important focus for developing treatments. Through comprehensive computational analysis, this study investigates the anticancer potential of phytocompounds from Ficus hispida, a medicinal plant renowned for its diverse bioactive compounds, as novel EGFR inhibitors. Molecular docking techniques using Py Rx software were employed to …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 3, Issue 2, 2025 Read article
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Molecular Docking Analysis of Phytocompounds from Ficus hispida Against Epidermal Growth Factor Receptor Kinase Domain
Abstract: The Epidermal Growth Factor Receptor (EGFR) kinase domain plays a key role in cancer, as it promotes tumour growth and helps cancer cells survive, making it an important focus for developing treatments. Through comprehensive computational analysis, this study investigates the anticancer potential of phytocompounds from Ficus hispida, a medicinal plant renowned for its diverse bioactive compounds, as novel EGFR inhibitors. Molecular docking techniques using Py Rx software were employed to …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 3, Issue 2, 2025 · pp. 48–62 Read article
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Tri-Band Edge-Cut Rectangular Microstrip Patch Antenna on Composite Substrate for RF Energy Harvesting in IoT Networks
Abstract: The increasing use of Internet of Things devices underscores the pressing need for sustainable energy solutions, since traditional batteries necessitate regular replacement and constrain scalability. Radio frequency energy harvesting is a viable option; nonetheless, antenna design continues to provide a significant problem owing to the requirements for compactness, efficiency, and multi-band functionality. A hybrid composite substrate configuration combining FR4 (εr = 4.3) and RT Duroid (εr = 2.2) is employed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 867–883 Read article
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Role of the Surgical-Ward Nurse in Identifying, Escalating, and Managing Postoperative Anastomotic Leak in Colorectal Patients: A Narrative Synthesis in an Australian Nursing Perspective
Abstract: Purpose: Postoperative colorectal anastomotic leak (AL) is one of the most feared complications after colorectal surgery because of its association with sepsis, reoperation, mortality, prolonged hospital stay, delayed adjuvant therapy, and permanent stoma formation. This narrative practice review outlines the frontline role of surgical-ward nurses in the early identification, escalation, and interim management of AL within the Australian acute-care context. Methods: A narrative synthesis of contemporary consensus statements, systematic reviews, …
Published in Research and Reviews : Journal of Surgery · Vol. 15, Issue 1, 2026 · pp. 7–12 Read article
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Investigation to Enhance Performance of Finned U-Tube Shell-and-Tube Heat Exchangers Using Epoxy Based Polymer Composite Material
Abstract: Shell-and-tube heat exchangers remain indispensable in thermal engineering systems; however, conventional metallic configurations often face challenges related to corrosion, weight, and limited thermal optimization. In this study, a novel approach is proposed by integrating epoxy-based polymer composite materials with finned U-tube geometries to enhance thermo-hydraulic performance while addressing material limitations of traditional systems. The work focuses on the development and evaluation of a hybrid heat exchanger comprising a mild steel …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 618–633 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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The Role of Artificial Intelligence in Enhancing Athlete Performance and Training Strategies
Abstract: Artificial Intelligence (AI) is transforming modern sports by improving athlete performance, training methods, and decision-making processes. The integration of AI technologies such as machine learning, data analytics, wearable sensors, and computer vision has enabled coaches and sports scientists to analyze large amounts of performance data with greater accuracy and efficiency. Athletes' physiological indicators, movement patterns, injury risks, and recuperation processes are all monitored by these technology. Training regimens can therefore …
Published in Recent Trends in Sports · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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A Smart Framework that Combines Data Mining and Optimization for Different Applications
Abstract: Blending predictive data mining with metaheuristic optimization has become essential for tackling tough, real-world problems across all kinds of fields. Most existing methods stick to fixed algorithms, each focused on a tiny slice of the puzzle, barely budging when new variables or unpredictability show up—especially with messy, human-generated data. So, here’s the idea: a Unified Metaheuristic and Predictive Data Mining (UMPDM) framework that finally connects adaptive search methods with powerful …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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Neuro-Symbolic Agentic AI for Autonomous Scientific Discovery: Integrating Deep Reinforcement Learning, Quantum Simulation, and XAI-Audited LLM Hypothesis Generation in Drug Target Identification
Abstract: The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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Spectral Intuitionistic Fuzzy Hypergraph Operators and Dominance Kernels for Resilient Discrete Network Design
Abstract: A new discrete-mathematical framework is developed for resilient network design on intuitionistic fuzzy hypergraphs, where uncertainty is explicitly represented through membership, non-membership, and hesitation degrees associated with both vertices and hyperedges. These three components are systematically integrated into an effective incidence operator that captures the underlying uncertain relationships within complex hypergraph structures. Based on this operator, both un-normalised and normalized Laplacian matrices are formulated to characterize the spectral properties and …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 41–48 Read article
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Dynamic Analysis of Honeycomb Sandwich Laminated Composite Plate using ANSYS
Abstract: A structural sandwich is a peculiar form of a laminated composite comprising of a combination of distinct materials which are bonded together to use effectively the properties of each separate component to the structural advantage of the whole assembly. Two thin, rigid and quite strong faces in the framework are partitioned by a thick, light and weaker core. The faces are attached adhesively to the core to ensure effective load …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 4, Issue 2, 2026 Read article
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Real-Time Deepfake Detection in Video Conferencing Systems
Abstract: Deepfake technology presents non-exemplary threats to video conferencing platforms, enabling advanced fraud, impression and misinformation campaigns worth billions annually. Current detection methods either exhibit latencies exceeding 100ms or rely on server-side cloud processing, raising privacy concerns. This paper presents DeepConfGuard, a lightweight hybrid architecture combining MobileNetV2 for spatial feature extraction, a bidirectional LSTM with attention for temporal modelling, and EfficientNetV2 for refinement. It reaches 94.8% accuracy with 85 ms end‑to‑end …
Published in International Journal of Electronics Automation · Vol. 4, Issue 2, 2026 Read article
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Real-Time System Monitoring and Resource Optimization Using Shell Scripts in UNIX/Linux Environments
Abstract: Real-time system monitoring is a fundamental aspect of system administration in UNIX/Linux environments, as it ensures optimal system performance, reliability, and continuous availability of services. In modern computing infrastructures, systems are expected to operate efficiently under varying workloads, and any degradation in performance, such as CPU overload, memory exhaustion, disk bottlenecks, or network congestion, can significantly impact user experience and system stability. Regular observation and prompt action are crucial for …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 1, 2026 · pp. 08–15 Read article
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Crystal Defects and Their Characterization in Modern Materials Science
Abstract: The physical and chemical properties of crystalline solids are fundamentally dictated by deviations from structural perfection, known as crystal defects. From the point-scale vacancies that drive diffusion to the planar boundaries that determine mechanical strength, defects serve as the primary "tuning knobs" in material design. This review provides a comprehensive examination of point, line, and planar defects, exploring their formation energetics and their role in plastic deformation via crystallographic slip. …
Published in International Journal of Crystalline Materials · Vol. 3, Issue 1, 2026 · pp. 15–19 Read article
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Deep Reinforcement Learning-Based Intelligent Energy Management Strategy for Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles
Abstract: As the number of EVs increases, smart solutions for energy management are needed that will optimize energy use, prolong battery life and boost vehicle performance. The application of conventional rule based and optimization-based Energy Management Strategies (EMS) for Battery–Supercapacitor Hybrid Energy Storage Systems (HESS) often leads to sub-optimal power management, supercapacitor mismatch and battery degradation when subjected to varying driving conditions. This study aims to design an intelligent energy management …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article
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Enhancing Energy Storage and Optimization of Distributed Energy Resources Using a Hybrid SWOA-MSNN Approach
Abstract: The fast growth of Distributed Energy Resources (DERs) like solar photovoltaics, wind power, and energy storage devices requires enhanced optimization methods to manage energy efficiently and stabilize operations in contemporary smart grids. A significant challenge is the dynamic optimization of the energy storage systems (ESS) and the distribution of the energy among DERs in conditions of uncertainty of loads and generation. The conventional control and optimization methods generally find it …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article