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532 articles for “Experimental approach”
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Biopolymer–Cement Hybrid Panels from Recycled Paper Mill Reject: Experimental Characterisation and Machine Learning Optimization
Abstract: The increased rate of the accumulation of industrial residues in the developing countries is a major cause of concern for the environment. The current study brings forth the use of industrial residues in the form of the production of eco-friendly building materials as a sustainable approach to their valorization. The valorization of recycled paper mill reject, a cellulose-based biopolymeric industrial residue, is being addressed in this study as a reinforcement …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 67–90 Read article
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Neuromorphic Spiking Neural Networks and Event-Driven Hardware: Architectures, Learning Paradigms, and Experimental Realizations
Abstract: With the current computational boom the research community is seeking for more sustainable energy efficient i.e. biologically inspired models of conventional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs) known as the third generation of neural network models, provide a revolutionary approach by mimicking the asynchronized event-driven and temporally accurate signaling of the mammalian brain. Whereas conventional deep learning models operate with real-valued activations and dense matrix multiplications, SNNs use …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 39–46 Read article
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Thermo-mechanical Modeling and Analysis of Heat Exchangers for Enhanced Performance in Renewable Energy Systems
Abstract: A comprehensive review of the thermo-mechanical properties of heat exchangers used in renewable energy facilities is presented in this research study. Heat exchangers are essential components of many renewable energy resources like biomass boilers, geothermal power plants, and solar thermal systems. It is crucial to comprehend their thermo-mechanical behavior in order to maximize efficiency, guarantee dependability, and increase operational lifespan. This work explores the complex interactions between mechanical stresses and …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 1, 2024 · pp. 13–19 Read article
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Indoor Localization of Mobile Device Using Fingerprinting Technique
Abstract: AbstractIndoor localization technology is real-time tracking of any device or person in an indoor area via a control device. In this paper, the fingerprinting method is utilized to track mobile devices in a specific indoor area. Fingerprinting, also known as pattern matching or database correlation method (DCM), needs a powerful received signal strength indication (RSSI) database which helps to make signal strength maps as well as used for matching. The …
Published in Recent Trends in Electronics Communication Systems · Vol. 4, Issue 3, 2017 · pp. 1–8 Read article
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Cardiovascular Image Segmentation in Computed Tomography Angiography ImagesUsing Deep Learning Approaches
Abstract: In present time, the cardiovascular disease is one of the common causes of mortality in human. In field of medical science, Heart angiography is one of the processes to testing of heart disease. Heart angiography identifies the abnormality in heart vessels. There are mainly two approaches to identify the heart disease. Former approach is the invasive and latter one is the non-invasive approaches. Invasive process is a painful diagnostic procedure …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 1, 2023 · pp. 20–27 Read article
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Face and Iris based Multimodal Person Identification using Likelihood Ratio Score Fusion Approach.
Abstract: AbstractThe aim of this paper is to propose a multimodal biometric system where likelihood ratio score fusion technique has been used to combine face and iris uni-modal biometric system outputs. Active Shape Model (ASM) has been used to extract the facial features and standard methods have been applied to effectively extract the human iris features. To evaluate the output of each individual modality of face and iris, Discrete Hidden Markov …
Published in Journal of Computer Technology & Applications · Vol. 6, Issue 3, 2015 · pp. 60–67 Read article
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Blockchain Enabled IoT System for Tamper Proof Monitoring of Polymer Composite Manufacturing Quality
Abstract: Ensuring real-time process compliance in resin-based polymer composite manufacturing remains a persistent challenge due to non-linear material behaviors, unpredictable curing dynamics, and fragmented sensor data pipelines. Traditional centralized monitoring architectures struggle to guarantee data integrity, auditability, and adaptive response under high-frequency environmental fluctuations. Most existing frameworks fall short in unifying trust, traceability, and time-critical decision-making particularly during critical cure-phase deviations due to limited integration of blockchain with intelligent sensor systems. …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 126–145 Read article
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DFT/Data Guided Predictive Modelling of Absorption Maxima in the OLED Rubrene Derivatives
Abstract: This study investigates the optical properties of rubrene derivatives to develop an accurate predictive model for absorption maxima using computational chemistry and chemoinformatic techniques. We benchmarked various quantum chemical methods, identifying that the M06-2X/aug-cc-pVDZ method in dichloromethane (DCM) provided the strongest correlation with experimental data. Key molecular descriptors such as band gap, ionization potential, and electrophilicity index were calculated and analyzed using principal component analysis (PCA) to identify significant factors …
Published in International Journal of Cheminformatics · Vol. 4, Issue 1, 2026 · pp. 41–56 Read article
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Structural and Thermal Analysis of Ball Control Valve: A Review
Abstract: AbstractA ball valve is a type of flow control device, which is widely used to regulate a fluid flowing through a section of pipe. Currently, analyses and optimization are of special important in the design and usage of ball valves. For the analysis, finite element method (FEM) is often used to predict the safety of valve disc, and computational fluid dynamics (CFD) is commonly used to study the flow characteristics …
Published in Recent Trends in Fluid Mechanics · Vol. 5, Issue 2, 2018 · pp. 30–35 Read article
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Measuring Microstructure, Wear Resistance, and Mechanical Reliability Enhancement in Polymer Nanocomposites via Data-Driven Analysis with Deep Learning
Abstract: Polymer nanocomposites have gained great attention owing to their superior mechanical performance, better wear resistance and customizable microstructural properties for aerospace, automotive, medicinal and industrial engineering applications. However, the correct evaluation of the link between the microstructure evolution and the material reliability is a huge issue due to the intricacy of nanoscale interactions and diverse material characteristics. In this study, we propose a data-driven approach that integrates deep learning and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Integrated Computational and Bio-catalytic Transformations: DFT-Guided Mechanistic Insights, Machine Learning, and Nano-biocatalyst Engineering for Sustainable Catalysis
Abstract: Computational catalysis has emerged as a transformative scientific discipline that integrates quantum chemistry, molecular modeling, machine learning, and density functional theory (DFT) to understand catalytic mechanisms and design highly efficient catalytic systems for sustainable industrial applications. The increasing global demand for environmentally responsible chemical manufacturing has accelerated research on advanced catalytic materials including transition metal catalysts, metal–organic frameworks (MOFs), homogeneous catalysts, heterogeneous systems, and bimetallic catalysts involving nickel and iron. …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 36–44 Read article
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Anisotropic Debye-Waller Factors and Debye Temperatures in Hexagonal Close-Packed Elements: A Comprehensive Compilation and Analysis
Abstract: In this study, we have investigated the anisotropic behavior of Debye-Waller factors (DWFs) and Debye temperatures (DTs) in three distinct materials: hexagonal rhenium (Re), osmium (Os), and thallium (Tl). We conducted a comparative analysis, aligning our experimental data on directional Debye temperatures with theoretical calculations. This exercise provided valuable insights into the concurrence between practical and theoretical approaches, thereby offering a critical evaluation of the accuracy and reliability of theoretical …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 32–39 Read article
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Computational Approaches to Understanding Cellular Signaling Pathways
Abstract: Cellular signaling pathways are fundamental in regulating vital processes, such as cell growth, differentiation, and apoptosis. The intricate and interconnected nature of these signaling networks requires sophisticated methods for their analysis. Computational approaches, including mathematical modeling, network analysis, and machine learning, have revolutionized the way researchers analyze and simulate cellular signaling. This article provides a comprehensive overview of computational strategies employed to model signaling pathways, with a focus on integrating …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 2, Issue 2, 2024 · pp. 8–13 Read article
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 · pp. 10–22 Read article
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Studies on Modelling of Mechanical Properties of Polyester-based Semi-rigid Polyurethane Foams
Abstract: This paper describes the mechanical behaviour of castor oil based semi rigid polyurethane foams with relative density below 0.05moulded by varying isocyanate index (Isocyanate equivalent/hydroxyl equivalent) from 1:1 to 1:2. Mechanical properties of polyurethane foam mainly compressive strength is measured experimentally and the values were compared using three widely referred theoretical models namely: (i) the Empirical model (ii) Gibson and Ashby approach widely used for foam description and (iii) modified …
Published in Journal of Polymer & Composites · Vol. 8, Issue 1, 2020 · pp. 77–86 Read article
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EFFECT OF EXPOSURE OF CEMENT ON THE COMPRESSIVE STENGTH OF CONCRETE
Abstract: Concrete has become the bedrock of infrastructural civilization in Nigeria as over 75% of the infrastructures in Nigeria have to do with concrete. Most times, Engineers design for a particular grade of concrete and Post-construction checks shows that concrete is defective of this strength and exposure of cement is observed to be one of the causative factors. In this study, the effect of exposure of cement on the compressive strength …
Published in Trends in Transport Engineering and Applications · Vol. 6, Issue 1, 2019 · pp. 34–44 Read article
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Vaccine Candidates and Animal Models for Kala-Azar
Abstract: Kala-azar or Visceral Leishmaniasis (VL) is a serious human disease in tropical regions and rapidly emerging as an opportunistic infection in HIV patients. The available treatment against VL is associated with toxicity and drug resistance. Even though the development of vaccines against VL is received limited attention. The only successful immunization strategy in humans has been leishmanization, which is based on the development of durable immunity after recovery from infection …
Published in Research and Reviews : A Journal of Immunology · Vol. 5, Issue 1, 2015 · pp. 29–35 Read article
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A Hybrid Mathematical Model for Epidemic Outbreak Forecasting Using Machine Learning and Cloud Computing
Abstract: The increasing frequency of infectious disease outbreaks has emphasized the necessity for intelligent epidemic surveillance systems capable of predicting disease spread at an early stage. Conventional outbreak detection approaches rely heavily on delayed statistical reporting and manual monitoring techniques, resulting in reduced responsiveness during critical periods. This paper presents a mathematical predictive framework for epidemic outbreak detection using machine learning and cloud computing technologies. The proposed framework integrates the Susceptible–Infected–Recovered …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 01–06 Read article
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Optimizing Routing and Placement of VLSI Circuits with Differential Algorithms and Neural Networks
Abstract: The performance of modern VLSI systems is heavily influenced by power constraints, necessitating precise power estimation and effective optimization techniques. Traditional methods, such as gate-level simulations, are often slow and computationally intensive. This paper introduces DRPENN (Differential Algorithm for Routing and Placement Optimization using Neural Networks), an innovative solution that combines a Switching Activity Estimator (SAE) with a neural network-assisted differential algorithm. By leveraging toggle rates from simulations to train …
Published in Journal of VLSI Design Tools and Technology · Vol. 14, Issue 2, 2024 · pp. 14–20 Read article
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Artificial Neural Network Based Prediction of Impact Loads and Thickness in CFRP and GFRP Composite Laminates
Abstract: Recent technological advancements, particularly the integration of neural networks, have facilitated a predictive approach to complex engineering problems, especially those involving composite materials with directional properties. The scarcity of literature on predicting impact damage using experimental and ultrasonic flaw detection data motivated this study. Experimental assessment of impact damage on carbon fiber/epoxy (CFRP) and glass fiber/epoxy (GFRP) composites was conducted using low-velocity drop weight impact testing. Damage assessment employed an …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 2, Issue 1, 2024 · pp. 34–45 Read article