In this study, the friction stir technique is proposed to process aluminum nanocomposites
reinforced with alumina nanoparticles. The effects of different processing parameters …

Motivated by the nonlinear behavior of composite materials under different machining
operations, this work aims at developing an optimized artificial intelligence model to predict …

Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin
development in energy and engineering systems, but these models must be fair, unbiased …

Accurate transformer fault diagnosis is crucial for maintaining the power system stability.
Due the complex operation condition of the transformer, its faults are with the characteristic …

In this study, advanced machine learning techniques were utilized to forecast the
thermohydraulic performance of a double pipe heat exchanger (DPHE). Key variables …

Abstract Surgical Site Infections (SSIs) is one of the common postoperative complications in
hospitals. SSIs risk is positively correlated to the air contamination level in operating room. In …

The main contribution in this article is proposing a modern meta-heuristic algorithm for
solving nonlinear, nonconvex, and complex OPF problems effectively in distribution …

The current energy crisis is a pressing global challenge, with the industrial sector accounting
for half of global energy consumption. Scheduling is considered one of the potential …

In this study, three machine learning (ML) algorithms (scaled conjugate gradient–SCG,
multilayer perceptron–MLP, and a novel hybrid artificial neural network algorithm–ANN …

The occurrence of vibration in engineering vehicles during operation is a common
phenomenon, and the utilization of vibration-enhanced heat transfer serves as a viable …