Aims and objective This study investigates the impact of varying electrical conductivity and
viscosity on flow using a unique design of intelligent Bayesian regularization neural …

In this article, we report a new empirical formula for quick calculation of cross sections of (n,
α) reactions with 14–15 MeV neutrons. Cross sections are analysed in terms of the …

Prediction of neutron-induced reaction cross-sections at around the 14.5 MeV neutron
energy is crucial to calculate nuclear transmutation rates, nuclear heating, and radiation …

Many studies have investigated the influence of theoretical models and factors involved in
the acquisition of cross-section data of a nuclear reaction. The implications of different …

Numerous research endeavours have delved into comprehending the dynamics of the (n, 3
He) reaction cross sections. In this study, a novel and straightforward empirical formula is put …

Copper (Cu), which is produced in cyclotrons or reactors, is a significant tracer in the human
body. Bayesian regularized artificial neural networks (ANNs) algorithm, which is one of the …

In the field of nuclear science, obtaining and utilizing nuclear data, including nuclear
reaction data, nuclear structure information, and radioactive decay data, is crucial. Neutron …

This study presents an application of an Artificial Neural Network (ANN) to detect fluids in an
annular flow regime using Prompt-Gamma Neutron Activation Analysis (PGNAA). The ANN …

In this groundbreaking study, artificial neural networks (ANNs) are employed to predict the
production cross-sections of crucial radioisotopes, namely 18 O, 209 Bi, 232 Th, and 68 Zn …

The double-differential cross sections (DDX) of neutron induced and neutron emission
reaction of 209Bi isotope are calculated and analysed at neutron emission energies below 7 …