In this research the a-As flims have been prepared by thermal evaporation with thickness 250 nm and rata of deposition r_d(1.04nm/sec) as function to annealing temperature (373 and 473K), from XRD analysis we can see that the degree of crystalline increase with T_a, and I-V characteristic for dark and illumination shows that forward bias current varieties approximately exponentially with voltage bias. Also we found that the quality factor and saturation current dependence on annealing temperatures.

This contribution investigates the impact of adding transition metal of Ti to CeOy samples at various concentrations referring to 0, 15.84, 24.46, 34.46, 36.23, 38.46, 45.38% and pure TiOy, correspondingly. The samples were fabricated by the magnetron sputtering technique. X-ray diffraction (XRD) configurations demonstrate the presence of α-Ce2O3 and Ce2O3 phases with increased Ti contents in the systems. X-ray photoelectron spectroscopy (XPS) experimentation confirms the purity of the S1-sample (CeO2) and the purity of the S8-sample (TiO2). Further XPS analysis reveals that Ti incorporation in the doped systems functions as a reducing agent because of the existence of α-Ce2O3 and Ce2O3 phases. Moreover, based on UV–vis spectroscopy res

This contribution investigates the impact of adding transition metal of Ti to CeOy samples at various concentrations referring to 0, 15.84, 24.46, 34.46, 36.23, 38.46, 45.38% and pure TiOy, correspondingly. The samples were fabricated by the magnetron sputtering technique. X-ray diffraction (XRD) configurations demonstrate the presence of α-Ce2O3 and Ce2O3 phases with increased Ti contents in the systems. X-ray photoelectron spectroscopy (XPS) experimentation confirms the purity of the S1-sample (CeO2) and the purity of the S8-sample (TiO2). Further XPS analysis reveals that Ti incorporation in the doped systems functions as a reducing agent because of the existence of α-Ce2O3 and Ce2O3 phases. Moreover, based on UV–vis spectroscopy res

In this research the a-As flims have been prepared by thermal evaporation with thickness 250 nm and rata of deposition (1.04nm/sec) as function to annealing temperature (373 and 373K), from XRD analysis we can see that the degree of crystalline increase with , and I-V characteristic for dark and illumination shows that forward bias current varieties approximately exponentially with voltage bias. Also we found that the quality factor and saturation current dependence on annealing temperatures.

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices