A thin film of AgInSe2 and Ag1-xCuxInSe2 as well as n-Ag1-xCuxInSe2 /p-Si heterojunction with different Cu ratios (0, 0.1, 0.2) has been successfully fabricated by thermal evaporation method as absorbent layer with thickness about 700 nm and ZnTe as window layer with thickness about 100 nm. We made a multi-layer of p-ZnTe/n-AgCuInSe2/p-Si structures, In the present work, the conversion efficiency (η) increased when added the Cu and when used p-ZnTe as a window layer (WL) the bandgap energy of the direct transition decreases from 1.75 eV (Cu=0.0) to 1.48 eV (Cu=0.2 nm) and the bandgap energy for ZnTe=2.35 eV. The measurements of the electrical properties for prepared films showed that the D.C electrical conductivity (σd.c) increase

In this article, we developed a new loss function, as the simplification of linear exponential loss function (LINEX) by weighting LINEX function. We derive a scale parameter, reliability and the hazard functions in accordance with upper record values of the Lomax distribution (LD). To study a small sample behavior performance of the proposed loss function using a Monte Carlo simulation, we make a comparison among maximum likelihood estimator, Bayesian estimator by means of LINEX loss function and Bayesian estimator using square error loss (SE) function. The consequences have shown that a modified method is the finest for valuing a scale parameter, reliability and hazard functions.

Little is known about hesitancy to receive the COVID‐19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID‐19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.

Objective: This in vitro study is aimed to compare and evaluate the cyclic fatigue of four varying NiTi rotary instrumentation systems. Method: In this study, four types of rotary files were used in four groups (10 files for each group), namely, Group A: Wave One Gold; Group B: AF Blue R3; Group C: One Curve; Group D: F6 SkyTaper. These groups were evaluated by a cyclic fatigue apparatus to measure cyclic fatigue resistance within the artificial metallic simulating canal that has a 60 angle of curvature, the curvature radius was 5 mm, whereas the inner diameter of the canal was 1.5 mm. All the files were rotated in artificial canals until they fracture. The resistance to cyclic fatigue was determined by counting the number of cycles to frac

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne