⚡ Category

eess

524 papers

⚡ electrical engineering

Learning Analytics-Driven Personalized Exercise Prescriptions in Secondary School Physical Education: A Quasi-Experimental Evaluation of an AI-Supported Formative Assessment System

This quasi-experimental study demonstrates that an AI-supported, learning analytics-driven system significantly improves secondary school physical education outcomes by delivering personalized exercise prescriptions and actionable feedback, with the greatest benefits observed among students with lower baseline fitness levels.

bingbing shao, jing su, yongrong zhang, chuan fu2026-07-17
⚡ electrical engineering

Human-centric multi-objective optimisation of operator safety and productivity

This study employs multi-objective metaheuristic optimization to identify cutting parameters that balance operator safety (minimizing heart rate and perceived risk) with productivity (maximizing material removal rate) in milling operations, revealing that the Genetic Algorithm offers the best trade-off while feed rate is the primary driver of operator stress.

Gregoire Tambwe MBANGU, George STILWELL, Yilei ZHANG, Zuzhen Ji, Dirk PONS2026-07-17
⚡ electrical engineering

Pipe Network Parameter Estimation Using a Wave Propagation Path Strategy

This paper introduces a novel, scalable parameter estimation framework for pipeline networks that utilizes a wave-propagation-path strategy and probabilistic methods to efficiently estimate wave speeds and friction factors by focusing on coarse pressure signal features, thereby avoiding complex nonlinear equations and demonstrating robustness in both theoretical and realistic urban water distribution systems.

Aaron C. Zecchin, Nhu Do, Wei Zeng, Alireza Keramat, Martin F. Lambert2026-07-17
⚡ electrical engineering

Aquivo: An open-source interactive tool for holistic water allocation under competing economic, environmental, and social objectives

This study introduces Aquivo, an open-source, interactive Python-based decision-support tool that utilizes multi-objective optimization and the TOPSIS method to generate empirically grounded, ranked water allocation strategies balancing economic, environmental, and social objectives.

Michael Yehya, Fatima Mansour, Ali Ayoub, Tang Wang2026-07-17
⚡ electrical engineering

AI-Guided Adaptive Control in Membrane Bioreactors for Real-Time Micropollutant Removal: Bridging the Explainability and Multi-Contaminant Gap in Intelligent Wastewater Treatment

This research introduces the XAI-AMBR framework, an explainable AI system combining LSTM, SHAP, and reinforcement learning that enables real-time, multi-contaminant adaptive control in full-scale Membrane Bioreactors, achieving superior micropollutant removal while significantly reducing energy consumption.

Devesh ojha, Anuradha Misra2026-07-17
⚡ electrical engineering

Underwater Photogrammetric Documentation as a Testbed for 3DF Zephyr Software: An Evaluation Study in the Search for an Alternative to Agisoft Metashape

Driven by the need to find alternatives to Agisoft Metashape due to geopolitical restrictions, this study evaluates 3DF Zephyr's capability in underwater photogrammetry and confirms its ability to produce high-quality 3D models comparable to industry standards, demonstrating that reconstruction quality depends primarily on input data rather than software limitations.

Wojciech Gajtkowski2026-07-17
⚡ electrical engineering

Experimental Realization of a Zero-Entropy Wireless Power Grid via Spatiotemporal Resonance and Reversible Charge Recovery

This paper claims to demonstrate a zero-entropy wireless power grid achieving over 92% efficiency through evanescent-wave coupling and reversible charge recovery, though the abstract contains scientifically impossible assertions such as "antigravity AI," "zero mineral reliance," and "zero-entropy" energy transmission that contradict established laws of thermodynamics and physics.

Min Ho Jung2026-07-17
⚡ electrical engineering

Physics-Informed Federated Learning for Decentralized Pharmaceutical Crystallization: Achieving Personalized Predictive Accuracy with Minimal Data

This study introduces a Physics-Informed Federated Learning (F-PINN) framework that integrates Population Balance Equations into a decentralized training process to achieve highly accurate, personalized predictions of pharmaceutical crystallization dynamics across multiple sites while preserving data privacy and ensuring robustness against data scarcity and noise.

Sai Vinay Thattukolla2026-07-17
⚡ electrical engineering

Physics-Driven Hierarchical Cascade Surrogate for Compositional Simulation of Geological CO2 and H2S Storage

This study introduces the Hierarchical Cascade Surrogate for CO2 Storage (HCS-CO2S), a physics-driven machine learning model that accurately predicts the full compositional state of reservoir fluids over centennial timescales by decomposing the prediction task into five causally ordered levels to suppress error accumulation in blind forecasts.

Stepan Zainulin, Anna Storozheva2026-07-16