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94 articles for “nonlinear systems”
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Facile Synthesis and Characterization of Cd²⁺ Doped NiAl₂O₄ Nanoparticles for Advanced Polymer Composite Integration
Abstract: Ni1-xCdxAl2O4, where 0 ≤ x ≤ 0.5, was successfully synthesized using the chemical co-precipitation technique, a scalable and cost-effective route suitable for composite material applications. X-ray diffraction (XRD) analysis revealed a crystalline cubic spinel structure for the resulting nanoparticles, with determined crystallite sizes ranging from 36 to 12 nm for NiAl2O4 and Cd-doped NiAl2O4. The respective band gaps for the materials were found to be 3.51 and 4.18 eV. Infrared …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 59–72 Read article
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Robust Control System for Missiles in the Presence of Uncertainty and Disturbances: A Comprehensive Review
Abstract: In today's warfare and defense systems, when precise and quick maneuvering is essential for mission accomplishment, missiles play a critical role. Nonetheless, the intricacy of missile dynamics by itself, combined with external disruptions and unpredictabilities in operational circumstances, provides challenging but ambitious conditions for achieving precise trajectory tracking and robust flight control. Missiles perform in highly dynamic and uncertain environments where factors such as aerodynamic disturbances, various atmospheric conditions, and …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 27–34 Read article
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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks
Abstract: Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 332–347 Read article
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From Differential Equations to Data Science: A Survey on Analytical Methods in Contemporary Problems
Abstract: The integration of differential equations and data science methods represents a dynamic and evolving approach to solving contemporary challenges across a wide range of disciplines, including engineering, physics, biology, economics, and finance. Differential equations have long served as fundamental tools for modeling continuous systems and processes, offering powerful insights into the behavior of natural and man-made phenomena. For example, they describe how heat diffuses through materials, how populations grow in …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge
Abstract: Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 1–5 Read article
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Self-similarities in Hilbert Envelope Energy Regions on Motion Waveforms Application in Detecting Motion Irregularities in Video Frames
Abstract: This paper introduces self-similar Hilbert envelope energy regions that are commonly found in oscillating spectral curves. The time-varying amplitudes and peak value frequencies in typical spectral curves reflect the response of a system to external stimuli. A Hilbert envelope is a smooth curve connected between spectral curve peak values. Each peak value on an envelope curve at time identifies an envelope-bounded region with interior area (called Hilbert envelope energy region …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 34–49 Read article
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Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems
Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
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Integrated, Geospatial Risk Assessment of Air, Water, and Soil Pollution Impacts on Agricultural Sustainability using Advanced Digital Technologies
Abstract: The systemic threat posed by the convergence of air, water, and soil contaminants represents a critical challenge to global agricultural resilience and food security. Traditional, site-specific pollutant monitoring methods are insufficient for capturing the dynamic, diffuse, and often nonlinear nature of environmental risk pathways that permeate agrarian landscapes. This study presents a robust framework for comprehensive risk assessment utilizing a synergistic suite of modern tools designed for spatial, temporal, and …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 2, 2025 · pp. 28–37 Read article
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Modeling of MHD Hybrid Nanofluid Flow with Radiation and Chemical Reaction Effects for Advanced Composite and Energy Applications
Abstract: Hybrid nanofluids reinforced with polymer-based matrices and nanoparticles have emerged as promising working fluids for advanced composite processing and thermal management systems. Their superior thermo-physical properties enable efficient cooling and energy transport, making them suitable for applications in polymer extrusion, composite curing, thermal insulation coatings, solar energy devices, electronic packaging, and nuclear system cooling. In this study, we investigate the heat and mass transfer behavior of magnetohydrodynamic (MHD) hybrid nanofluid …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 648–660 Read article
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AI/ML-Based Approach to Solar Irradiance Prediction and Energy Suitability
Abstract: In this paper, due to challenges in precisely predicting solar irradiance, which is essential for solar power system optimization, we employed six diverse machine learning (ML) techniques: Linear Regression, Decision Tree, Random Forest, Gradient Boosting methods (including XGBoost), and Neural Networks—to analyze and predict outcomes using a dataset containing meteorological and temporal features. Key variables include wind speed, humidity, and temperature, which significantly influence the model’s predictive capability. Each method …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 16, Issue 3, 2025 · pp. 36–48 Read article
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Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes
Abstract: The rapid expansion of intelligent sensing in the Internet of Things (IoT) has revealed the pressing need for materials and algorithms capable of self-adaptation in volatile environments. Conventional polymer-based sensors and static control strategies often fail to capture nonlinear thermo-mechanical dynamics, leaving them unsuitable for unpredictable operating conditions. Although prior studies have improved polymer composites or introduced algorithmic optimization independently, few attempts have coupled the adaptability of smart materials with …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 370–391 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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A study on Controlling Artificial Heart
Abstract: The artificial heart has moved from a visionary concept to a clinically viable organreplacement technology, yet its longterm success hinges on the sophistication of its control architecture. This paper presents a comprehensive investigation of closedloop control strategies that enable an artificial heart to mimic the dynamic, beattobeat adaptability of its biological counterpart. We first construct a highfidelity cardiovascular model that integrates ventricular elastance, systemic and pulmonary vascular compliance, and realtime …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 14–23 Read article
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Synthesis and Applications of Copper- Doped Pyridine Precipitate
Abstract: A metal organic nonlinear optical single crystal, copper doped pyridine material, was created at room temperature using the slow evaporation solution growth technique (SEST). The developed material's crystalline nature was revealed by a powder X-ray diffraction (XRD) analysis. Single crystal XRD study shows that the material synthesized possesses monoclinic system of cell parameters, a = 11.44 Å, b = 7.92 Å, C = 11.74 Å and alpha and gamma is …
Published in Journal of Materials & Metallurgical Engineering · Vol. 15, Issue 1, 2025 · pp. 51–60 Read article
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Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 Read article
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Advanced Lithium-Ion Battery Prognostics: A Comprehensive Review of Machine Learning Approaches for Remaining Useful Life Prediction
Abstract: The lithium-ion battery (LIB), as one of the main sources for portable power systems, has been increasingly popular owing to its widespread applications in electric vehicles, consumer electronics, aerospace and renewable energy. Despite their advantages in high energy density and long cycle life, LIBs suffer from degradation over time of aging and cycling, resulting in loss of performance, safety issues, and economic bottlenecks. Predicting their Remaining Useful Life (RUL) is …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 2, 2025 · pp. 12–27 Read article
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Experimental Analysis of Glass Fiber Composite on Low Velocity Impacts
Abstract: This research specifically deals with determining the retained tensile strength after loading Glass Fiber Reinforced Polymer (GFRP) composites following low-velocity impact. The purpose is to determine the degree to which these impacts affect the structural performance and mechanical integrity of GFRP materials. Experimental tests were conducted on glass fiber composite specimens in order to observe variation in tensile strength upon impact. The results indicated significant tensile strength reduction in impact …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 789–796 Read article
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Comprehensive Review of the Fundamental and Functional Properties of Crystalline Materials
Abstract: Crystalline materials, characterized by their highly ordered atomic arrangements, serve as the backbone of modern engineering and technology. This review provides a detailed examination of their diverse properties, categorized into mechanical, thermal, electrical, and optical domains. We analyze fundamental mechanical parameters such as the elastic modulus, yield strength, and fracture toughness, alongside functional behaviors like fatigue and creep. The discussion extends to thermal transport and expansion, electrical conductivity and resistivity, …
Published in International Journal of Crystalline Materials · Vol. 3, Issue 1, 2026 · pp. 20–24 Read article
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Stock Market Analysis Using Data Science
Abstract: Stock market prediction using data science has become a popular area of research and application in recent years. This is because the stock market is a complex system with many variables and factors that affect its behavior, making it difficult to predict with certainty. The stock market has always been the aggression of buyers and sellers of stocks, therefore in the global finance market, stock trading is one of the …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 1, 2024 · pp. 1–4 Read article