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427 articles for “efficient frameworks”
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Optimized Utilization of Kota Stone Slurry Waste in Fly Ash–Based Geopolymer Mortar: A Taguchi-Driven Approach
Abstract: The large-scale generation of stone-processing wastes presents a critical sustainability challenge and an opportunity for value-added reuse in construction materials. This study develops a high-performance fly ash geopolymer mortar by partially replacing Class F fly ash with Kota stone slurry waste (KSSW) and optimizing the key mix parameters using a Taguchi design framework. Five governing factors—binder replacement level, NaOH molarity, sodium silicate–to–sodium hydroxide ratio (SS/SH), curing temperature, and alkaline solution-to-binder …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 426–445 Read article
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Optimizing Glass to Metal Composite Seal Performance: An integrated Approach with Artificial Neural Network, Multiple Regression, and Taguchi
Abstract: Composite materials, particularly glass to metal composites, are critical components in solar receiver tubes, where vacuum leakage can significantly compromise the efficiency of solar plants. This research addresses the technical barriers associated with the development of durable and high-quality glass to metal composite seals. We investigate the principles that can enhance the physical and chemical properties of these composite seals, focusing on the incorporation of TiO2 and MgO nanoparticles into …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 418–435 Read article
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AI-Assisted Gain Scheduling for Real-Time Temperature Control in Chemical Reactors
Abstract: Temperature control in continuous stirred-tank reactors (CSTR) represents a critical challenge in chemical process industries due to inherent nonlinearities, time-varying dynamics, and parametric uncertainties. Conventional proportional-integral-derivative (PID) controllers with fixed gains often fail to maintain optimal performance across varying operating conditions, leading to temperature excursions that compromise product quality and safety. This paper presents a novel AI-assisted gain scheduling framework that integrates artificial neural networks (ANN) with adaptive PID control …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 24–33 Read article
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Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network
Abstract: The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 3, 2025 Read article
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Design and Manufacturing of Suspension and Steering System of a F3 Vehicle
Abstract: The suspension and steering systems are critical subsystems of any formula-style racing vehicle, directly influencing its stability, handling, and driver safety. This paper focuses on the design, analysis, and manufacturing of suspension and steering systems for a Formula Student F3 vehicle. The primary objective is to develop a lightweight, reliable, and efficient design that complies with Formula Student competition rulebooks while ensuring optimum ride quality and performance. Using advanced computer-aided …
Published in Trends in Mechanical Engineering & Technology · Vol. 15, Issue 3, 2025 · pp. 1–12 Read article
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Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article
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A Trust-Enhanced Security Architecture for Authenticating Customer Records in Banking Institutions
Abstract: The growing digital disruption of banking and financial services has completely altered the face of customer onboarding, money transactions, and financial service deliveries. Even as digital technologies provide unparalleled levels of efficiency and accessibility for consumers of financial services, they have also presented new challenges that are equally daunting. Among the growing number of financial threats that digital technology has spawned is the risk of synthetic identity fraud. Unlike identity …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 · pp. 16–22 Read article