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270 articles for “election prediction”
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Dielectric Breakdown and Electrical Aging of Insulating Polymer Materials in High Voltage Systems
Abstract: In this paper, a detailed analysis of dielectric breakdown and electrical aging behavior of high-voltage insulating polymer material has been proposed through sophisticated MATLAB simulation. The research involves electric field modeling, aging life prediction, partial discharge (PD) behavior and uncertainty modeling using Monte Carlo analysis. Electric field hotspots causing critical behavior, sensitivity of the lifespan to electric stress, and the stochastic PD build-up allow predictive diagnostics of the health of …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 173–187 Read article
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Simulation and Experimental Analysis of Abuse Testing for Prediction of Life Cycle for Lithium Ion Battery Cell and Pack Level
Abstract: Lithium-ion batteries play a crucial role in contemporary technology, serving as the power source for everything from consumer gadgets to electric vehicles. However, their safety and longevity are significant influenced by the reperformance under extreme conditions, commonly referred to as ab use testing .This paper explores the simulation and analysis of ab use testing and life cycle prediction for lithium-ion batteries at both the cell and pack levels. Abuse testing …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 2, Issue 2, 2024 · pp. 1–24 Read article
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Process of Decolourisation of Textile Dye Using Electrocoagulation and It’s Modelling Using Artificial Neural Network
Abstract: Electrochemical technology encompasses a wide spectrum of technologies and makes numerous contributions towards a cleaner environment. In this work, the decolourization of the synthetic fabric dye solution containing CIBA (Company for Chemical Industry Basel) Red by electrocoagulation method has been investigated. Investigations have also been conducted on the impact of operational variables on colour removal effectiveness, including beginning pH, electrolysis duration, distance between electrodes. An electrode retention time, dye focus. …
Published in Trends in Electrical Engineering · Vol. 14, Issue 2, 2024 · pp. 28–38 Read article
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Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article
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A Web Application for Predicting Diabetes Using Machine Learning Methods
Abstract: Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 92–102 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 45–54 Read article
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Study of Conversion of Vibration Energy into Electric Energy
Abstract: AbstractIn the upcoming time, there is a serious threat on fossil fuels like diesel, petrol and natural gases, because all these fuels are non-renewable forms of energy. These source of energy depleted very rapid rate and after some decades they completely exhausted, so we should have to prepare to face this challenges. Alternative energy will become increasingly important as fossil fuel supplies inevitably run out or environmental damage sparks consumer …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 8, Issue 2, 2017 · pp. 40–44 Read article
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Reduction of Electric Vehicle Driving Range Due to Battery Capacity Fading
Abstract: This paper is mainly focused to the estimation of the reduction of the driving range in electric vehicles due to battery capacity fading as a consequence of the aging effects with cycling. A scale model reproducing the real driving conditions in electric vehicles has been used to develop a simulation process to evaluate the incidence of the cycling on the battery capacity fading. The model on which the simulation has …
Published in Journal of Automobile Engineering and Applications · Vol. 8, Issue 2, 2021 · pp. 1–16 Read article
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Seasonal Variation of Electric Vehicles Autonomy: Application to AC/DC Dual Voltage Operation
Abstract: In this work, a study to predict battery performance and autonomy for a dual type of electric engine, 480 VAC and 360 VDC, has been developed. The study has been conducted in vehicles of mass between 1000 and 3000 kg with battery capacity in the range of 40 to 100 kWh. The process has been simulated using reference values and modeled for specific driving conditions. The simulation has been developed …
Published in Journal of Mechatronics and Automation · Vol. 7, Issue 3, 2020 · pp. 1–15 Read article
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Computational Modeling of Polymer Semiconductors for Electronic Applications
Abstract: Polymer semiconductors have become important materials in modern electronic applications because they combine semiconducting behavior with mechanical flexibility, low-cost processing, and tunable molecular structure. Their growing use in organic field-effect transistors, organic photovoltaics, organic light-emitting diodes, and flexible sensing devices has increased the need for accurate computational approaches that can predict material properties and device performance before experimental fabrication. This paper reviews the major computational modeling techniques used for polymer …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 132–146 Read article
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Implement Artificial Intelligence and Machine Learning for Engineering Design, Predictive Modeling, and Optimizing Polymer Nanocomposites
Abstract: Polymer nanocomposites are high performance engineered materials obtained by inclusion of nano-sized fillers into the polymer matrix to enhance mechanical, thermal, electrical, barrier and functional properties. However, the complex and non-linear interactions among polymer chemistry, nanofiller characteristics, filler concentration, dispersion, interfacial bonding and processing conditions make it challenging to anticipate and maximize their properties. Artificial intelligence (AI) and machine learning (ML) offer powerful data-driven solutions to these difficulties by establishing …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 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. 11–23 Read article
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An Overview Mechatronics Systems Design of Modelica-Based Model Predictive Control Strategies for CO₂ Heat Pump Systems
Abstract: The growing adoption of renewable energy technologies poses challenges to energy supply stability, necessitating increased flexibility in energy demand. This paper introduces a Modelica-based Model Predictive Control (MPC) strategy aimed at keeping the supply water temperature within the range of 55°C to 75°C while minimizing energy consumption and electricity expenses throughout a year. A comprehensive, high-fidelity model representing a school building in Oslo, Norway, was developed using Modelica and exported …
Published in Journal of Mechatronics and Automation · Vol. 13, Issue 1, 2026 · pp. 15–27 Read article
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IOT and algorithmic intelligent motor health monitoring as well as maintenance prediction
Abstract: Manufacturing, transportation, and energy systems rely largely on industrial electric motors, and their untimely failure can result in expensive downtime, safety hazards, and decreased operational efficiency. The majority of traditional motor maintenance procedures rely on reactive methods or routine inspections, which frequently miss early-stage problems and lead to needless maintenance or unexpected breakdowns. This project offers an Intelligent Motor Health Monitoring and Predictive Maintenance System that combines Internet of Things …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 28–37 Read article
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Switching Loss Calculation of Power MOSFET using the Estimation Technique
Abstract: An accurate analytical model is very important to measure the switching losses of PowerMOSFET. The perfect estimation of Power MOSFET switching loss is very difficult to predictthe efficiency of the power electronics circuit. This paper investigates the basic internalphysics of the Power MOSFET device and proposed the best possible analytical model tocalculate the power loss. The pre-existing widely accepted power loss calculation method isfound to be useless and inaccurate. In …
Published in Journal of VLSI Design Tools and Technology · Vol. 10, Issue 1, 2020 · pp. 37–44 Read article
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A Combined ECG and PPG Signal Powered Artificial Intelligence-Based Prediction Model for Stroke
Abstract: Stroke is one of the most common causes of morbidity and mortality around the world, and emphasis on prevention and early detection strategies cannot be overstated. This review aims to integrate techniques of artificial intelligence with electrocardiogram and photoplethysmogram signals to enhance stroke prediction and monitoring of cardiovascular health. All in all, the application of artificial intelligence that incorporates machine learning, deep learning, or hybrid models gives robust tools toward …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 2, 2025 · pp. 18–26 Read article
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Assessment of a Wind Energy Conversion System for Sustainable Hydrogen Production by Alkaline Water Electrolysis in India: Effect of Geographical Location and Wind Turbine Type
Abstract: The use of wind energy for electricity generation and use of this electricity for hydrogen production by alkaline water electrolysis promises to be a truly sustainable scheme for the postulated hydrogen economy. This work addresses the feasibility assessment of a standalone wind energy based turbine generator system that meets the energy requirements of the electrolysis process at several locations in India. Energy requirement for electrolysis depends on the hydrogen production …
Published in Emerging Trends in Chemical Engineering · Vol. 4, Issue 2, 2017 · pp. 5–22 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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Demonstrative Research of Group VII Elements of the Periodic Table: Characteristics and Properties of Chemical Behavior
Abstract: The elements in Group VII, commonly referred to as halogens, are non-metals that exhibit similar chemical characteristics and a high level of reactivity, which can be attributed to their electron configuration. This paper seeks to provide an overview of the relationship between the electron structure and chemical behavior of the Group VII elements found in the periodic table. The primary goals include elucidating how the electron configuration influences the physical …
Published in Emerging Trends in Chemical Engineering · Vol. 12, Issue 2, 2025 · pp. 18–26 Read article