In this work, a multivariate time-series machine learning meta-model is developed to predict
the transient response of a typical nuclear power plant (NPP) undergoing a steam generator …

The production of crops in greenhouses will ensure the demand for food for the world's
population in the coming decades. Precision agriculture is an important tool for this purpose …

The advancement of safety and reliability in Nuclear Power Plants (NPP) is essential for
ensuring the protection of human life, the environment, and the sustainable use of clean …

This work addresses a novel technique for selecting the best coal for a thermal power station
using a thermodynamically assisted Machine Learning model. This work includes 32 coal …

Abstract Reactivity Initiated Accident (RIA) scenarios require special attention using
advanced simulation techniques due to their complexity and importance for nuclear power …

To expedite the decision-making process under Nuclear Power Plant (NPP) accident
conditions, at a reduced computational cost, a Machine Learning (ML) time-series meta …

Enhancing safety and dependability within nuclear power facilities holds paramount
importance in safeguarding both individuals and the environment. The adoption of machine …

In this work, a machine learning (ML) metamodel is developed for the time‐series
forecasting of a typical nuclear power plant response undergoing a loss of coolant accident …

This study assesses and compares the radiological risks posed by two nuclear accident
scenarios: Loss of Coolant Accident (LOCA) and Steam Generator Tube Rupture (SGTR) …

The nuclear power plant (NPP) plays a crucial role in providing clean energy, significantly
contributing to mitigating global warming. However, this advantage is accompanied by …