In this study, the effect of pumping power on the conversion efficiency of nonlinear crystal (KTP) was investigated using laser pump-power technique. The results showed that the higher the pumping power values, the greater the conversion efficiency (η) and, as the crystal thickness increases within limitations, the energy conversion efficiency increases at delay time of (0.333 ns) and at room temperature. Efficiency of 80% at length of KTP crystal (L = 1.75 X 10^-3 m) and P_in = 28MW, and also, compare the experimental results with numerical results by using MATLAB program. 

This paper investigated in the numerical simulation model to calculate the Earth magnetic field components at north provinces of Najaf city (Longitude 44.316 ^o -44.3592^o E and Latitude 32.0508^o - 32.0256^o N). The components of the Earth magnetic field (total intensity (F), horizontal intensity (H), declination (D), inclination (I), the north component(X), the east component(Y), and Down component(Z)) were found by using spherical harmonic world magnetic model (WMM2010). A great deal of anomaly has been discovered in all components of the Earth magnetic field at the selected region (Long. 44.345^o-44.335^o E, Lat.32.042^o-32.032^o N) using Kriging method.

The study employs Critical Discourse Analysis (CDA) to analyze how technological discourses are influenced by AI-generate d English texts. The research marries Fairclough’s three-dimensional discourse analysis, Van Dijk’s socio-cognitive approach, and Corpus-Assisted Discourse Studies (CADS) in the use of mixed-methods research, integrating primarily qualitative analysis with quantitative corpus-based data, to perform a thorough analysis of twenty AI-produced English texts. The findings identify the sophisticated linguistic mechanisms through which AI language employs modality, nominalization, passive voice, and interdiscursive blending to normalize and legitimize dominant contemporary ideologies. These mechanisms serve to legitimize te

The present study experimentally and numerically investigated the impact behavior of composite reinforced concrete (RC) beams with the pultruded I-GFRP and I-steel beams. Eight specimens of two groups were cast in different configurations. The first group consisted of four specimens and was tested under static load to provide reference results for the second group. The four specimens in the second group were tested first under impact loading and then static loading to determine the residual static strengths of the impacted specimens. The test variables considered the type of encased I-section (steel and GFRP), presence of shear connectors, and drop height during impact tests. A mass of 42.5 kg was dropped on the top surface at the m

Background: Prevention against nosocomial infection is an important issue of health care field and considered a challenge of patients’ since it reflects its effect on their quality of life. This due to that it will lead in most cases to prolonged hospitalization and also more cost.Objective: To determine the prevalence of different types of nosocomial infection and to demonstrate the association of different risk factors (hospital environment, workers, visitors) with nosocomial infection.Patients: this study was carried out in eleven months at Ba’quba general Hospital; Iraq. A total of 81 clinical specimens (urine, pus from abscess , burn swab, nasal swab, ear swab and wound swab) taken from surgical patients,102 specimens from

This study aims at shedding light on the linguistic significance of collocation networks in the academic writing context. Following Firth’s principle “You shall know a word by the company it keeps.” The study intends to examine three selected nodes (i.e. research, study, and paper) shared collocations in an academic context. This is achieved by using the corpus linguistic tool; GraphColl in #LancsBox software version 5 which was announced in June 2020 in analyzing selected nodes. The study focuses on academic writing of two corpora which were designed and collected especially to serve the purpose of the study. The corpora consist of a collection of abstracts extracted from two different academic journals that publish for writ

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with