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214 articles for “padding”
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The Mycobiome Frontier – Pre & Post 2020 Status: Integrating Fungal Bioactive Compounds, Probiotics, and Antimicrobial Peptides in Modern Therapeutics and Biotechnology.
Abstract: The fungal kingdom represents an indispensable resource in modern therapeutics and biotechnology, offering a diverse array of bioactive compounds, probiotics, and antimicrobial peptides (AMPs). Functional fungal polysaccharides (FFPs), such as beta-glucans, chitin, and mannans, are centrally involved in modulating the human gut microbiota, providing novel therapeutic avenues for chronic conditions including diabetes, neurodegenerative disorders, and cancer. These compounds act as prebiotics, nourishing beneficial bacteria and enhancing metabolic parameters such as …
Published in International Journal of Fungi · Vol. 3, Issue 1, 2026 · pp. 1–11 Read article
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Computational Analysis of Leaf Spring System with Functionally Graded Materials Using ANSYS
Abstract: Leaf springs are important parts of a vehicle’s suspension system, and their main function is to absorb shocks, improve stability, and make the ride more comfortable. Traditionally, ASTM A36 steel is used because it is strong, long-lasting, affordable, and easy to get. However, this type of steel is very dense, which makes vehicles heavier. This extra weight makes the vehicle less efficient and increases emissions. Because of these issues, there …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 380–397 Read article
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Damage Evolution and Delamination Resistance in Polymer Matrix Functionally Graded Laminates
Abstract: Functionally graded laminates (FGLs) in polymer-matrix systems represent a promising pathway to enhance damage tolerance and delay delamination in advanced structural composites. In this study, we explore the mechanisms of damage initiation, propagation, and delamination resistance in polymer matrix functionally graded laminates (PM-FGLs) through a combined experimental–computational approach. Laminates with linear, exponential, and bio-inspired gradation profiles were fabricated using vacuum-assisted resin transfer molding (VARTM) and additive manufacturing techniques. Comprehensive mechanical …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 321–337 Read article
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Comparative Analysis of Processing-Property Relationships in Metal and Polymer Matrix Composites: A Unified Statistical Framework for Hardness Characterization
Abstract: Composite materials, encompassing both metal matrix composites (MMCs) and polymer matrix composites (PMCs), exhibit complex processing-property relationships that fundamentally govern their mechanical performance across diverse applications. This study presents a unified statistical framework for analyzing hardness characteristics in composite systems, using aluminum-tungsten carbide (Al-WC) metal matrix composites as a representative model system while establishing connections to polymer matrix composite behavior. The investigation employed comprehensive processing parameter optimization, microstructural characterization, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 419–430 Read article
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3D Printing of Polymer-Based Functionally Graded Materials: Recent Developments and Challenges
Abstract: Additive manufacturing (AM), specifically 3D printing, has become a useful technique for fabricating functionally graded materials (FGMs) because it can facilitate the spatial distribution of materials. Polymer FGMs (P-FGMs) have gained a great deal of interest due to their lightweight, customizable, multifunctional properties. In comparison to conventional fabrication, 3D printing allows better control of composition and microstructure, which results in materials with controlled mechanical, thermal, and biological properties. This review …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 338–347 Read article
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Hybrid Additive-Subtractive Manufacturing of Multi-Material Functionally Graded Components: Integration of Laser Powder Bed Fusion with High-Speed CNC Finishing for Aerospace Applications
Abstract: The synergy involved in the merging of additive and subtractive manufacturing technologies is the game changer to generate multi-material functionally graded components to be used in the aerospace industries. The paper is an in-depth review of a proposed hybrid additive-subtractive manufacturing, which synergistically merges laser powder bed fusion (LPBF) fashioning with rapid computer numerical control finishing production processes. The multi-material deposition, thermal issues, and optimization of post-processing are the challenges …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 398–418 Read article
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A Review on Evolution of Aircraft Fuselage Materials: From Wood to Composites & Functionally Graded Materials
Abstract: This article presents a comprehensive overview of the evolution of materials for construction of aircraft fuselages, with a focus on the expanding use of polymer composites and functionally graded materials (FGMs) in modern aerostructures. The baseline fuselage design is thought to be from wood, to be followed by metallic systems such as aluminum, titanium, and nickel alloys. All systems improved manufacturability, strength, and longevity, but there was a substantial revolution …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 447–455 Read article
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AI-Accelerated Development of Gradient Polymer Nanocomposite Thin Films
Abstract: Gradient polymer nanocomposite thin films are an active field of materials research due to the fact that it enables scientists to de-facto regulate the optical, electrical, and mechanical properties of a film by merely altering its composition on a layer-by-layer basis. This type of control opens the gate to the improved flexible electronics, long lasting protective coats, and the new smart gadgets. The problem is, though, that it is a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 456–469 Read article
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AI-Driven Prediction of Mechanical and Thermal Properties in Polymer-Based Functionally Graded Composites
Abstract: The proposed architecture of the current paper is an artificial intelligence (AI)-driven model of forecasting mechanical and thermal aspects of polymer-based functionally-graded composites (FGCs). Traditional micromechanical and finite element models, which are practical in homogeneous composites, might not be able to account in nonlinear interaction that is caused by compositional gradient. To overcome the challenge, machine learning (ML) models like artificial neural network (ANN), support vectors regression (SVR), and gradient-boosted …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 70–89 Read article
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AI-Designed Functionally Graded Polymer Composites for Multifunctional Thin Films
Abstract: The design of multifunctional polymer composite thin films requires simultaneous optimization of mechanical, optical, barrier, and thermal properties—objectives often in conflict when using conventional homogeneous materials. This study presents an artificial intelligence-driven framework for designing functionally graded material (FGM) architectures in polymer nanocomposite thin films. We integrated machine learning with physics-based modeling to optimize compositional gradients across film thickness, achieving superior performance compared to homogeneous and discrete multilayer alternatives. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1026–1041 Read article
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Forecasting Climate-Driven Healthcare Demand in Agricultural Regions: A Multi-Modal AI Approach
Abstract: The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 28–38 Read article
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AI-Based Early Diagnosis & Prevention of Diabetes
Abstract: The worldwide burden of Diabetes Mellitus, especially Type 2 diabetes (T2D) has escalated to a critical level. Early detection of diabetes is essential to reduce long‑term complications and healthcare costs. This study explores the use of artificial intelligence (AI) techniques to improve the early diagnosis and prevention of diabetes. We developed an AI model using the Random Forest algorithm, the model predicts diabetes risk based on clinical and lifestyle variables …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Experimental and CFD Investigation of an Epoxy-Based Thermally Conductive Polymer Composite U-Tube Shell-and-Tube Heat Exchanger: A Lightweight Alternative to Conventional Metal Systems
Abstract: Shell-and-tube heat exchangers continue to play a critical role in industrial thermal systems; however, their conventional design based on fully metallic materials often leads to challenges related to weight, corrosion, and cost. In recent years, thermally conductive polymer composites have emerged as promising alternatives, offering improved corrosion resistance and design flexibility. In this study, the thermo-hydraulic performance of a U-tube shell-and-tube heat exchanger is investigated by partially replacing conventional metallic …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 685–700 Read article
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Multi-Objective Optimization of Polymer-Based Functionally Graded Composites for Lightweight Structures
Abstract: Functionally graded composites (FGCs) improve lightweight structural performance by allowing material properties to change smoothly across a component. Polymer-based FGCs (P-FGCs), in particular, are gaining prominence in aerospace, automotive, and biomedical industries due to their excellent strength-to-weight ratio, tunability, and ease of processing. However, optimizing these materials for lightweight structural applications requires addressing conflicting design objectives, such as maximizing stiffness while minimizing weight or enhancing thermal resistance while maintaining manufacturability. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 961–973 Read article
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Differential Gene Expression Analysis of Human Atrial Fibroblasts Reveals Dysregulation of RNA Metabolism and Translational Machinery in Atrial Fibrillation
Abstract: Atrial fibrillation (AF) is a complex cardiac arrhythmia characterized by extensive structural remodeling and the activation of atrial fibroblasts, which drive the progression of fibrosis. To identify the underlying transcriptomic alterations, we analyzed six human atrial fibroblast RNA-Seq datasets (three control and three AF) retrieved from the Sequence Read Archive. After performing rigorous quality control and adapter trimming, we aligned the reads to the GRCh38 human reference genome using a …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 15–25 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Seasonal Dynamics of Coastal Landscapes: A Critical Review Using Remote Sensing and GIS
Abstract: Coastal landscapes are among the most dynamic environments on Earth, undergoing continuous transformation due to both natural processes and anthropogenic activities. In India, particularly along the southern coastal regions of Andhra Pradesh, Tamil Nadu, and Kerala, shoreline morphology and sediment transport patterns are significantly influenced by seasonal monsoons, cyclones, storm surges, waves, tides, and changing river discharges. These factors contribute to varying rates of coastal erosion, accretion, inundation, and land- …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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Traversal Speed Comparison of BFS and DFS in Balanced and Skewed Binary Trees
Abstract: In this paper, we provide an analysis of how well both breadth-first search (BFS) and depth-first search (DFS) algorithms perform while wandering through two kinds of binary trees: balanced and skewed. The research was motivated by the practical application of storing files and directories in a certain type of parent-child relationship through the use of hierarchical file systems (e.g., windows explorer). The result of measuring how fast and versatilely these …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 2, 2026 Read article
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Impact of a Nurse-Led Work-Based Intervention on Job Performance and Job Satisfaction Among Nurses in Selected Hospitals in Erode
Abstract: Job satisfaction is a critical aspect of nurses’ professional lives, influencing patient safety, staff morale, productivity, performance, and overall quality of care. Higher levels of job satisfaction have been linked to improved patient outcomes. This study aimed to assess the level of job satisfaction among staff nurses before and after a nurse-led work-based intervention, evaluate the effectiveness of the intervention, and examine the association between posttest job satisfaction scores and …
Published in International Journal of Evidence Based Nursing And Practices · Vol. 4, Issue 1, 2026 · pp. 19–27 Read article
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Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article