Ultraviolet photodetectors (UV PDs) are important devices that can be used in various scientific, commercial and military applications. In this work, a numerical simulation study of nitride-based "p+-n-n+" front illuminated UV PD, designed with an aluminum composition achieving a true solar blindness, has been reported using the commercially available Atlas package from Silvaco international. It has been found that the proposed structure is sensitive to the UV rays in the wavelength range investigated, where the spectral response reaches its maximum then declines sharply with a good performance of solar-blind at room temperature and zero-bias voltage. Furthermore, it was also found by simulating the evolution of the current density according to different wavelengths of the incident radiation that the designed structure is able to act as a wavelength selector device.

This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

T There are several forms of electricity generation, first, by burning fuels, such as coal, natural gas or oil, which have an effect on the atmosphere, especially increasing greenhouse gases, or, second, from renewable sources, such as wind, hydro and solar, which are clean and renewable sources of energy. Our work focuses on solar sources, especially photovoltaics; we have treated the steering part of photovoltaic generators using artificial intelligence methods, specifically, case-based reasoning. The system we have built generates actions to be applied to the generator based on its current state and reasoning from previous cases recorded in the case base.