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558 articles for “structural modelling”
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Illuminating the Frontier of Drug Discovery: Unleashing the Power of Bioinformatics for Unprecedented Breakthroughs
Abstract: It takes a long time and a lot of effort to discover and develop new drugs, which necessitates extensive study and testing. With the help of computational techniques and data analysis, bioinformatics has grown to be a potent tool for drug discovery in recent years, allowing researchers to find new drugs faster. In this review, we examine the role of bioinformatics in drug discovery, including the use of ligand- and …
Published in International Journal of Molecular Biotechnological Research · Vol. 1, Issue 2, 2023 · pp. 1–10 Read article
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Advances in Polymer-Modified Concrete using XAI
Abstract: Industry 4.0 technologies are being quickly adopted by the construction sector, opening new avenues for enduring operational and environmental issues. This sector looks at how explainable AI can forecast air and enhance the quality of building materials. XAI, AI, ML, and big data drive a new paradigm in polymeric material development. The effective XAI and ML-assisted design creates innovative, high-performance polymeric materials. It covers building a database and representing structures, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 133–144 Read article
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Free Vibration Analysis of Circular Perforated Laminated Plates
Abstract: This study investigates the free vibration characteristics of circular perforated laminated composite plates. A finite element model is created to analyze the effects of perforation patterns, hole sizes, and laminate configurations on the natural frequencies and mode shapes of these structures. The model incorporates shear deformation theory and accounts for the anisotropic nature of composite materials. Parametric studies were held to examine how varying perforation geometries, including hole diameter, spacing, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 281–296 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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Structure–Property Correlation of Infill Topology and Density on Tensile and Flexural Performance of FDM-Printed PLA and ABS
Abstract: Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has become a widely adopted manufacturing technology due to its design flexibility, low cost, and capability for rapid prototyping. However, the mechanical performance of FDM-printed components is strongly influenced by internal structural parameters such as infill pattern and infill density, in addition to the intrinsic material behaviour. This study investigates the structure–property relationship between infill topology, density, and mechanical performance of PLA …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 4, Issue 1, 2026 · pp. 26–37 Read article
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QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
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Designing a Pure Talent Management Model in the Growth Centers of Technological Units of Islamic Azad University with the Foundation's Data Approach
Abstract: Talent management is one of the management fields that has experienced the greatest growth in the last two decades. Due to its competitive nature, for the first time the concept of talent management was proposed in private organizations and large multinational companies and was widely welcomed. Therefore, the main goal of this research is to design a lean talent management model in Islamic Azad University technology development centers with a …
Published in International Journal of Sustainability · Vol. 1, Issue 1, 2024 · pp. 45–50 Read article
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Accelerating Drug Discovery with AI: Transforming the Pharmaceutical Pipeline
Abstract: The revolutionary potential of artificial intelligence (AI) is examined in this essay the pharmaceutical industry, highlighting its application across the drug development lifecycle. Artificial Intelligence, specifically via deep learning models and machine learning (ML) such as GANs, RNNs, and transformers, enhances drug discovery, formulation, toxicity prediction, and clinical trials. It streamlines processes like identification of targets, virtual screening, modelling of structure-activity relationships, and medication repurposing. AI is also employed in …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 77–84 Read article
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Quantitative Image-Based Assessment of Degradation Patterns in Polymer-Based Medical Implants
Abstract: Polymer-based medical devices are widely used in clinical practice, where long-term material degradation can compromise performance and patient safety. Traditional polymer degradation studies predominantly rely on laboratory-based experiments, which often fail to capture real-world operational and usage conditions. In this study, a multimodal, data-driven framework is proposed for the quantitative assessment of degradation patterns in polymer-based medical devices using publicly available clinical failure data. Structured operational parameters, including cumulative usage …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1510–1518 Read article
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Unveiling the Engine of Efficiency: Exploring the Vital Dimensions of Warehousing for Optimal Operational Performance
Abstract: This research endeavours to comprehensively explore the multifaceted dimensions of warehousing that significantly influence operational efficiency within the bustling industrial nexus of the National Capital Region (NCR), encompassing a diverse array of warehouse types in the vicinity of Delhi-NCR. Employing a meticulously crafted structured questionnaire, respondents provided insights through a Likert scale, ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The data collection process, utilizing stratified sampling …
Published in Journal of Production Research & Management · Vol. 14, Issue 1, 2024 · pp. 29–38 Read article
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Relationship Between Mental Health, Competitive Aggression and Anger Rumination with Regard to the Role of Mental Health of Martial Arts Athletes in Gilan Province
Abstract: Due to the phenomenon of championship in sports, athletes are always very thirsty to achieve the championship and go on the podium, and this factor can cause negative behaviors by athletes, one of which is competitive aggression. Therefore, the main purpose of this study is to investigate the relationship between mental health, competitive aggression and anger rumination with regard to the role of mental health of martial artists in Gilan …
Published in Recent Trends in Sports · Vol. 1, Issue 1, 2024 · pp. 1–10 Read article
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Exploring Practical Applications of Artificial Neural Networks: A Review
Abstract: Computational models called artificial neural networks (ANNs) are modeled after the structure of the human brain. These models are designed to process information and learn from data. Artificial neural networks, or ANNs, are composed of interconnected artificial neurons layered to resemble the brain's neural network.. Through training, ANNs adjust the connections between neurons based on labeled data, enabling them to recognize patterns and perform specific tasks. Despite their efficacy in …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 2, 2024 · pp. 1–11 Read article
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The Mediating Role of Label Trust in Shaping Green Purchase Attitudes Among Young Consumers: Sustainable Chemical Transparency in FMCG Packaging
Abstract: This study looks at the function of Perceived Chemical Transparency (PCT) in influencing consumers' Green Purchase Attitude (GPA) in the Fast-Moving Consumer Goods (FMCG) sector, with Label Trust (LT) serving as a significant mediating factor and Environmental Concern (EC) acting as a direct predictor. Based on the Theory of Planned Behavior and Signaling Theory, the study hypothesizes that clear disclosure of chemical and polymer-related information increases trust in eco-labels and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1308–1319 Read article
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Assessing Nanomaterial Toxicity and Environmental Behavior: Toward Sustainable and Safe Nanotechnology
Abstract: The rapid advancement of nanotechnology has introduced engineered nanomaterials into diverse sectors including medicine, agriculture, electronics, and consumer products. However, the unique physicochemical properties that make nanomaterials valuable also raise significant concerns about their potential toxicity to human health and ecological systems. This study presents a comprehensive survey-based analysis of 400 respondents from diverse professional backgrounds across seven countries to assess perceptions and understanding of nanomaterial toxicity mechanisms and environmental …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 28, Issue 1, 2025 · pp. 1–11 Read article
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Seismic Response Characterization of Vertically Irregular Multi-Storey Buildings Incorporating Mass and Stiffness Variations Using E-TABS Software
Abstract: The increasing demand for innovative architectural designs has led to the widespread use of vertically irregular configurations in high-rise reinforced concrete (RC) buildings. However, such irregularities significantly influence structural performance under seismic loading conditions, leading to complex structural behaviour and stress concentration at specific levels This study focuses on evaluating the seismic behaviour of vertically irregular multi-storey buildings using nonlinear time history analysis, which provides a realistic representation of structural …
Published in Recent Trends in Civil Engineering & Technology · Vol. 16, Issue 3, 2026 Read article
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Advancements in Drug Design Technology and Its Impact on COVID-19 Treatment
Abstract: The deadly coronavirus disease 19 (COVID-19) pandemic has recently spread, raising concerns about global health. The search for novel therapeutic compounds is made more necessary by the persistent problem of the absence of licensed medications or vaccinations. By saving money and time, computer-aided drug design has sped up the process of finding and developing new drugs. The structured-based and ligand-based drug discovery subcategories of computer-aided drug design (CADD) are the …
Published in International Journal of Virus Studies · Vol. 1, Issue 1, 2024 · pp. 1–15 Read article
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Modeling Galaxy Formation in a Hierarchical Universe: A Fiducial Approach and Comparison with Observational Data
Abstract: We have developed a detailed model to understand how galaxies form in the framework of hierarchical theories of structure formation. Our model accounts for key processes like the formation and merging of dark matter halos, the heating and cooling of gas inside these halos, the regulation of star formation driven by energy from evolving stars and supernovae, galaxy mergers, and the changes in star populations over time. This approach is …
Published in International Journal of Universe · Vol. 1, Issue 1, 2025 · pp. 30–36 Read article
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Analysis of Proposed Diagrid Geometry Using Golden Ratio in Non-Linear Regime of Material
Abstract: Lateral loads are critical in the design of high-rise structures. The buildings need to have high stiffness and must be able to resist lateral deformations and torsional rotations without having discomfort to the user. Diagrid structural system is one of the systems used in resisting lateral forces. In this study, diagrid geometry is generated using the ‘Golden Ratio’ concept and used to model 40, 50, and 60 story structures. Two …
Published in Journal of Construction Engineering, Technology & Management Read article
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A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 Read article
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Secure Forge: Deepfake Image Detection Using Vision Transformers
Abstract: Deepfake technologies have become a major risk to the credibility and trustworthiness of digital visual information. Using powerful generative models like GANs and autoencoders, deepfakes can generate highly realistic fake videos and images, resulting in misinformation, identity theft, and public loss of trust in digital media. Classic Convolutional Neural Networks (CNNs) while being highly effective in initial-stage, deepfake detection tend to be limited by their local receptive fields and dependency …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 32–45 Read article