Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques.

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Raman Spectroscopy-Assisted Characterization of Nanoform MoS2 Thin Film Transistor.

In this paper, we report the simple preparation and investigation of electrical transport properties of nanoform MoS2 thin film transistor (TFT) devices. MoS2 nanoparticles were synthesized by using the hydrothermal method. The physiochemical characterizations such as UV-vis, Fourier transform infrared, X-ray diffraction, and Raman spectroscopy studies were performed. Spin-coating was used to make the thin film on which silver electrodes were made. We observed nonlinear current-voltage (I-V) characteristics; however, the symmetricity was found in the I-V curve which confirms the no formation of the Schottky barrier between thin film and electrodes. Transistor transfer characteristics reveal that the TFT device is n-doped as more drain current modulation is observed when the positive gate voltage is applied. The relationship between gate-current and gate voltage studies concludes that there is no leakage gate current in the TFT device which further confirms the good reliability of transfer characteristics of a device. The device mobility was calculated as ~10.2 cm2/Vs, and the same was explained with plausible reason supported with Raman spectra analysis.

Investigation on the Effect of Thermal and Mechanical Treatment to the Offshore Corrosion Behavior of 6351 Aluminum Alloy in Red Sea Environments.

This study investigates the effect of artificial aging treatment and mechanical attrition treatment on the corrosion behavior of 6351 Al alloy in Red Sea environment. The artificial aging of the alloy is carried out at temperature range 140°C-240°C in steps of 20°C for various time periods after the solution heat treatment at 530°C for 1 hour. Based on the hardness measurements, the aged specimens are categorized into three, namely, underaged, peak aged, and overaged. The as received alloy specimens are subjected to mechanical attrition treatment in a vacuum chamber using steel balls. Vickers hardness test reveals that there is a remarkable improvement in hardness of mechanical attrition treated specimens compared to that of aged specimens. The aged and mechanical attrition treated specimens were subjected to the corrosion test in Red Sea water using the Autolab instrument. The corrosion tests reveal that the peak-aged composite corrodes more in Red Sea water when compared to that of other groups of specimens. XRD measurements and SEM analysis are carried out to study the surface nature of attrition treated specimens. It is observed that the mechanical attrition treated specimens exhibit a nanocrystalline surface and lead to a decrease in corrosion resistance. However, the annealing of the alloy after attrition treatment optimizes the mechanical and corrosion properties of the alloy.