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257 articles for “Varian”
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LQR-Based Optimal Control of Inverted Pendulum System with State Estimation and Stability Analysis
Abstract: The inverted pendulum on a cart is a canonical benchmark problem in control systems engineering, capturing the essential challenges of stabilizing an inherently unstable, underactuated, and nonlinear plant. Classical Proportional-Integral-Derivative (PID) controllers, while widely employed in industrial practice, exhibit fundamental performance limitations when applied to such systems, primarily due to their inability to account for multivariable coupling, process noise, and the absence of a systematic optimization framework. This paper presents …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 1, 2026 · pp. 31–43 Read article
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Investigating the Influence of Process Parameters on Photochemical Machining of Phosphor Bronze Alloy Microchannels
Abstract: Microchannels are widely employed in microfluidic devices, biomedical systems, and compact heat exchangers, where their functional efficiency depends strongly on surface finish, dimensional control, and edge quality. Traditional machining techniques often face limitations in producing such features with the required precision, prompting the use of advanced micromachining methods. In the present work, photochemical machining (PCM) has been applied to fabricate serpentine-shaped microchannels in phosphor bronze. The study systematically investigates the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 837–846 Read article
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Integrative Structural-Functional Genomics of Fc and Fab: Precision Models for Monoclonal Antibody Stability and Anti-Aggregation Engineering
Abstract: Monoclonal antibodies (mAbs) represent the cornerstone of biotherapeutics, yet aggregation propensity compromises up to 50% of candidates during development, driven by Fab hypervariability and Fc vulnerabilities.(1,2) This review integrates functional genomics from OAS (4B+ sequences)(5) and structural databases (SAbDab: 10K+ structures)(6) with machine learning models achieving R=0.97 for SAP prediction.(11) We dissect biophysical mechanisms, benchmark predictive tools (DeepSP, ESM2), and engineering strategies (YTE, FW mutations) that enhance Tm by 5-10°C …
Published in International Journal of Molecular Biotechnological Research · Vol. 4, Issue 1, 2026 Read article
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Fabrication and Characterization of Hybrid Composites Using Natural Fibers Reinforced in HDPE and Optimization of Injection Moulding Process Parameters to Minimize Shrinkage
Abstract: Materials are crucial for advancing human living standards. Over the past 20 years, composite materials have garnered significant attention owing to their unique properties and applications across various sectors. This study outlines the creation and analysis of a novel series of natural fiber-based composites, which incorporate jute, sisal, and hemp as reinforcements, with high-density polyethylene (HDPE) serving as the matrix, and produced through injection moulding. This study examines the effects …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 556–585 Read article
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Importance of Thesaurus in Natural Language Processing for Scholarly Data Extraction
Abstract: The exponential growth of scholarly literature needs advanced methods for efficient data extraction and knowledge discovery. Natural Language Processing (NLP) has emerged as a crucial technology in automating the analysis and organization of academic texts. Among various linguistic resources, thesauri serve as important tool for enhancing semantic understanding by providing structured vocabularies, synonyms, and hierarchical relationships between terms. This paper examines the importance of thesauri in enhancing NLP-based scholarly data …
Published in Emerging Trends in Languages · Vol. 3, Issue 1, 2026 · pp. 19–24 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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Development of Neuromorphic Polymer Composites Using IoT Sensing and Brain-Inspired Learning Algorithms
Abstract: This research aims to develop neuromorphic polymer composites by combining conductive sensing materials, IoT-based sensing data collection and brain-inspired learning models for adaptive response. Hybrid conductive polymer composites were developed by adding carbon nanofibers and graphene Nano platelets to a thermoplastic polymer. IoT sensors (strain, temperature) were employed to collect real-time sensing data that was combined with environmental data. A material-aware neuromorphic learning algorithm was created with event-driven spike coding …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 755–784 Read article
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Synergistic Effects of Hybridized Nano-Silica and Hemp Fiber Reinforcement on Bio-Epoxy Composites
Abstract: Natural fiber-reinforced bio-epoxy composites have become promising sustainable alternatives to conventional synthetic-fiber petroleum-based materials, but are often limited by low fiber-matrix interfacial bonding, low thermal stability, and high moisture absorption which limit their application in structural applications. These issues are discussed in this work, by carrying out a systematic investigation of the synergistic effects of the hybridization of nano-silica particles with alkali-treated hemp fibers in a bio-epoxy matrix the composites …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 214–228 Read article
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Mechanical Characterization and Machinability Optimization of Stir-Cast Al6061–SiC Metal Matrix Composites
Abstract: This study presents the fabrication, mechanical characterization, metallurgical analysis, and machinability optimization of Aluminum 6061 reinforced with Silicon Carbide (SiC) metal matrix composites (MMCs) at three weight fractions: 5%, 7.5%, and 10%. Composites were manufactured using the stir casting technique, followed by comprehensive mechanical testing (tensile, hardness, and impact), optical microscopy, and scanning electron microscopy (SEM). Machinability was assessed through turning experiments on a lathe using an L9 Taguchi orthogonal …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 833–848 Read article
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Spatiotemporal Analysis of Mean-Field Coupled Lorenz Oscillators for Applications in Electronic Network Design and Chaotic Synchronization
Abstract: This study investigates the spatiotemporal behavior of a network of 100 coupled Lorenz oscillators interacting through mean-field coupling with coupling strength κ = 0.1 and explores its relevance to electronic system design and nonlinear network architectures. While each oscillator follows classical Lorenz dynamics, the coupling mechanism enables collective behavior that resembles synchronization phenomena observed in distributed electronic and communication systems. Numerical simulations reveal rich dynamical characteristics including partial synchronization, emergent …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 · pp. 1–9 Read article
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AI-Driven Pharmacogenomics and Precision Medicine: Future of Personalized Therapy
Abstract: Pharmacogenomics and artificial intelligence (AI) are emerging as important drivers of precision medicine, enabling healthcare systems to adopt individualized therapeutic approaches. Pharmacogenomics examines how genetic variations influence drug response, efficacy, metabolism, and toxicity, while AI provides advanced computational tools for analyzing complex genomic and clinical data. This review highlights the integration of AI-driven pharmacogenomics in personalized therapy and its potential to improve treatment outcomes. Machine learning, deep learning, natural language …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 1–12 Read article
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Gastro Retentive Drug Target System: Its Needs, Advantages, Limitation and Approaches
Abstract: Background: Gastro retentive drug delivery systems (GRDDS) address limitations of oral therapy for drugs with poor solubility, instability at intestinal pH, or narrow absorption windows by prolonging gastric residence and increasing contact with the gastric mucosa. Objectives: To review GRDDS approaches—floating systems, mucoadhesive systems, and super porous hydrogels—with emphasis on formulation strategies and polymer–excipient selection to enhance gastric retention, modulate buoyancy/swelling/adhesion, and ultimately improve oral bioavailability and therapeutic outcomes for …
Published in Trends in Drug Delivery · Vol. 13, Issue 2, 2026 · pp. 47–58 Read article
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Evaluating UX Design Factors Affecting Efficiency of Composite Material Design and Analysis Platforms
Abstract: Within engineering software platforms that involve the design, simulation and characterization of composite materials, user experience (UX) design has become a key determinant for efficient use. This research aims to quantify how user experience design parameters relate to productivity in composite engineering workflows by analyzing the relationship between usability, learnability, accessibility, complexity of the UI, navigation efficiency and users engineering results satisfaction. Computational techniques in python were used in the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 341–366 Read article
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Passive Augmentation Studies in a Double Pipe Heat Exchanger with Ellipsoidal Dimples
Abstract: In this work, the numerical studies of heat transfer augmentation of a counter-current double pipe heat exchanger with ellipsoidal dimples (inward-facing, raised dimples) on the inner tube was performed using SOLIDWORKS software. The effect of dimple depth, dimple pitch and number of dimples/angles between two dimples on heat transfer was investigated at a constant volumetric flow rate of 4 LPM for hot (water) and cold (water) fluids. The effect of …
Published in Journal of Thermal Engineering and Applications · Vol. 13, Issue 2, 2026 Read article
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Tailoring the Compressive Behavior of Tetrachiral Auxetic Structures Through FDM Process-Parameters
Abstract: This research evaluates how fused deposition modeling (FDM) fabrication process parameters affect the compressive behavior of tetrachiral auxetic structures created from Polylactic Acid (PLA). Auxetic materials have several useful properties, including reversible deformation and high-energy absorbing capabilities, which are beneficial to creating ultra-lightweight structural, protective, and shock-resistance designs. Among the available auxetic topologies, the tetrachiral configuration is particularly attractive for engineering use, because its rotation-dominated node–ligament deformation gives a negative …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 · pp. 58–70 Read article
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Farmer Producer Organizations as Catalysts of Rural Transformation: A Multidimensional Framework from Punjab, India
Abstract: Farmer Producer Organizations (FPOs) have evolved as an important institutional mechanism for resolving the socio-economic challenges of small and marginal farmers by fostering collective action, expanding market access, and boosting bargaining power. In the broader context of sustainable rural development, FPOs are increasingly acknowledged as catalysts of rural transformation by contributing to numerous dimensions of farmer well-being. However, current study has generally analyzed FPOs through economic and operational indicators, with …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 3, 2026 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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A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction
Abstract: In an earlier comparative study of machine-learning methods for genomic prediction of wheat grain yield, we reported a counter-intuitive result: automated nested-cross-validation tuning of the Lasso regularization penalty reduced mean predictive ability relative to a fixed, arbitrarily chosen penalty (mean Pearson r falling from 0.408 to 0.349 across four environments), the opposite of the expected effect of hyperparameter tuning. We hypothesized two possible explanations at the time — high-variance penalty …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 42–51 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 · pp. 15–24 Read article
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Performance and Enhancement of Car Air Conditioning System
Abstract: In internal combustion engine, car air conditioning system consumes large amount of energyabout 30% of the fuel in tropic areas. In this experiment, ANOVA (Analysis of Variance)manual method is used to reduce car AC energy consumption. People set their AC to operate attemperature 18°C, even though we can set indoor temperature in between 20°C to 24°Caccording to human comfort chart. In this experiment, AC efficiency is optimized by takingvarious readings …
Published in Journal of Thermal Engineering and Applications · Vol. 7, Issue 3, 2020 · pp. 20–24 Read article