Backpropagation neural networks using Levenberg-Marquardt algorithm are applied to make quantum correction to nanoscale MOSFETs and the efficiency and accuracy of the neural networks with different hidden layers and different neurons are studied.

For the treatment of the quantum effect of charge distribution in nanoscale MOSFETs,a quantum correction model using Levenberg-Marquardt back-propagation neural networks is presented that can predict the quantum density from the classical density.

This article discusses on how to determine the time of steel work s recovery production and the starting time of electric energy meter failure in order to calculate readjustment electric quantity.

The authors made a distinction between the differential viscosity and the conventionally defined viscosity in capillary tubes,and some errors were corrected,and the analysis of example was given.

Analysis of the results for a Bi_2Sr_2CaCu_2O_8+δ surface intrinsic Josephson junction(SIJJ),quantum corrections are considered at the intermediate areas between MQT and thermally activated process.

The quantum corrections to the entropy of the Barriola-Vilenkin black hole due to the massless gravitational field are calculated by using the brick-wall model.

Quantum correction to the entropy of Schwarzschild black hole due to high spin fields;

The quantum correction to vacuum solution of the Barriola-Vilenkin(BV) black hole is perturbatively found by using a two dimensional(2D)effective action derived from spherically symmetric reduction of four dimensional (4D)general relativity coupled with 4D quantized massless scalar field.