Optimal Location to Use Solar Energy in an Urban Situation

This study conducted in Lima, Peru, a combination of spatial decision making system and machine learning was utilized to identify potential solar power plant construction sites within the city. Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study. Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared. Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase. On the basis of the findings, it was determined that the GEP is the most suitable network for predicting the location. The test state’s Nash-Sutcliffe efficiency (NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Following the generation of the final map based on the results of the GEP model, it was determined that 9.2% of the province’s study area is suitable for the construction of photovoltaic solar power plants, while 53.5% is acceptable and 37.3% is unsuitable. The ANN model reveals that only 1.7% of the study area is suitable for the construction of photovoltaic solar power plants, while 66.8% is acceptable and 31.5% is unsuitable.     

Circuit Analysis and Optimization of GAA Nanowire FET Towards Low Power and High Switching

Silicon - The main aim of this work is to study the effect of symmetric and asymmetric spacer length variations towards source and drain on n-channel SOI JL vertically stacked (VS) nanowire (NW)...     

Physical effects of nanoaluminum oxide and nanographene on kenaf epoxy composite; vacuum bagging process

In this study, the effect of secondary filler nanographene and primary filler nanoaluminum oxide with kenaf epoxy was investigated. The vacuum bagging process was performed, and a magnetic stirrer was used for blending additives. Water absorption (borewell, distilled, normal, and seawater) and chemical absorption (acid, base, and neutral solution) tests were carried out to evaluate the water absorption, chemical absorption, thickness swelling, and diffusivity of the composite with varying percentages of fillers. The result demonstrates that the addition of 6% primary and 3% secondary filler together, increases the density of the sample by 13.5% and decreases void content by 77%. The addition of secondary filler lowers water absorption in normal water by 1.3% compared to other types of water, as the addition of secondary filler increases the water absorption decreases by 70% in borewell water, 75% in distilled water, 76% in normal water, and 69% in seawater. Thickness swelling of the samples decreases upon the addition of secondary fillers. In acid, the primary and secondary fillers increase the resistance to dissolution. The diffusivity rate of the sample with a combination of fillers 1.3 × 10⁻⁵.