⚡ Category

eess

1710 papers

⚡ electrical engineering

Energy Neutrality and Nutrient Recovery through ANAMMOX Pretreatment for Potable Reuse: A Transferable Technical Protocol from the Netherlands to Peru

This paper synthesizes verifiable technical parameters from Dutch energy-neutral and nutrient-recovering water treatment systems to propose a transferable protocol for implementing ANAMMOX pretreatment in Peru, including an experimental design aligned with national regulations and an assessment of institutional capacity for adoption.

PAUL RICARDO PRUDENCIO GALVEZ2026-07-10
⚡ electrical engineering

Assessment of the Inertia of Peru's National Interconnected Electric System, 2040: Regulatory Need for Synthetic Inertia

This paper assesses the declining synchronous inertia in Peru's National Interconnected Electric System (SEIN) by 2040 under high renewable penetration, demonstrating that critical frequency stability risks necessitate the regulatory implementation of synthetic inertia, a finding quantified through a newly proposed Integrated Frequency Security Index (IISF-SEIN).

PAOLO MARTIN CHANG OLIVARES2026-07-10
⚡ electrical engineering

Mechanical Strength Assessment of Eco-Friendly Bricks Produced Using Waste Marble Dust and Crushed Stone Dust as Partial Cement Replacements

This study demonstrates that partially replacing cement with a 20% blend of marble dust and crushed stone dust (10% each) in brick production offers a sustainable and cost-effective solution that maintains adequate mechanical strength while mitigating environmental waste disposal challenges.

Buhari Abdulwahab Hamza, Zakiyyu Sunusi Suleiman, Azhar Uddin2026-07-10
⚡ electrical engineering

Residual strength of corroded reinforced concrete beams: Experimental, analytical, numerical and machine learning approach

This study evaluates the residual flexural capacity of corroded reinforced concrete beams through a comprehensive integrated approach combining experimental testing, modified analytical formulations, finite element numerical modeling, and machine learning, ultimately demonstrating that Random Forest algorithms offer the most efficient and accurate prediction tool compared to traditional methods.

Suryansh Sharma, Harish Chandra Arora, Aman Chauhan, Aditya Sharma, Madhu Sharma2026-07-10
⚡ electrical engineering

A Physics-Informed Hypoplasticity–Machine Learning Framework for Offshore Foundation Design: Comparative Insights from the German North Sea and the Peruvian Pacific Coast

This paper proposes a technology-transfer pathway for adapting a physics-informed hypoplasticity–machine learning framework, originally developed for German offshore wind conditions, to the Peruvian Pacific Coast by identifying directly transferable methodological components and outlining necessary local recalibrations for soil data, seismic coupling, and liquefaction screening.

PAUL RICARDO PRUDENCIO GALVEZ2026-07-10
⚡ electrical engineering

Nanofluidic memristor with an ultra-low operating voltage

This study introduces a Cu–PAF/CuSO₄–Cu nanofluidic memristor that achieves ultra-low voltage (sub-100 mV) switching and energy-efficient synaptic emulation by optimizing electrode-electrolyte pairs and utilizing reversible ion adsorption within nanochannels to overcome previous voltage limitations in neuromorphic computing.

Yike Xiao, Yukun Bao, Tianyong Peng, Kangrui Li, Pingyuan Yan, Wei Huang, Chuanxiang Sheng, Qi Wang, Ye Feng Yu2026-07-10
⚡ electrical engineering

A Mesoscale Damage Mechanics Framework for Predicting Damage Initiation in Cross-Ply Composite Laminates under Low-Velocity Impact Using Artificial Neural Networks

This study proposes a hybrid framework that integrates a mesoscale damage mechanics finite element model with an Artificial Neural Network to accurately and efficiently predict damage initiation and evolution in cross-ply composite laminates subjected to low-velocity impact.

Subhabrata Koley, Pravin Kumar Gaurav, Prithwish Kumar Das2026-07-10
⚡ electrical engineering

Interpretable hybrid XGBoost-MH models for predicting crack coalescence stress from direct shear testing of rock-like specimens containing non-persistent joints

This study develops an interpretable hybrid XGBoost-Butterfly Optimization model, validated on 450 direct shear tests and integrated into a user-friendly GUI, to accurately predict crack coalescence stress in jointed rock masses while identifying key mechanical drivers through SHAP analysis.

Fariborz Matinpour, Shadman Mohammadi Bolbanabad, Vahab Sarfarazi, Mohammad Rezaei, Matin Eghdami, Mahdi Hasanipanah2026-07-10