Compounds from tin oxide doped with gallium oxide and cerium oxide were synthesized. Thin films from (SnO2:Ga2O3,CeO2) with different doping ratios were prepared on glass and single crystal silicon as substrates with the use of the pulsed laser deposition technique. The results of FTIR spectrums analysis was presented. Varies heterojunctions were fabricated from (SnO2:Ga2O3,CeO2 /Cu2S/c-pSi) heterojunctions solar cell which are formed from two layers: the first was Cu2S and the second was layer from tin oxide doped with two oxides named gallium oxide and cerium oxide with different doping ratios (0, 0.03, 0.05, and 0.07). The heterojuntion (SnO2:5%Ga2O3 /Cu2S/c-pSi photovoltaic structure exhibits open circuit voltage (Voc

Background Solar irradiance is a nonlinear and intermittent function, which makes accurate forecasting of solar power generation a challenge. The high variability of meteorological conditions is not well represented by conventional atmospheric models, thus hampering forecasting skill and model robustness. In this work, an advanced hybridization of multi-population cuckoo search (HMPCS) algorithm with machine learning (ML) methods is developed to enhance the prediction performance of photovoltaic (PV) power forecasting with more reliability. Methods In this study, a hybrid modeling framework is proposed, called HMPCS–ML framework which captures the global search capacity of HMPCS and predictive power of sophisti

The composites were manufactured and study the effect of addition of filler (nanoparticles SiO2 treated with silane) at different weight ratios (1, 2, 3, 4 and 5) %, on electrical, mechanical and thermal properties. Materials were mixed with each other using an ultrasound, and then pour the mixture into the molds to suit all measurements. The electrical characteristics were studied within a range of frequencies (50-1M) Hz at room temperature, where the best results were shown at the fill ratio (1%), and thermal properties at (X=3 %), the mechanical properties at the filler ratio (2%).