The problem of research was the lack of research that dealt with issue of the job design approach that is more suitable for knowledge work, therefore, the research aims to determine the impact of job enrichment,  and knowledge capitalon strategic success, starting from the hypothesis that there significant impact of job enrichment and knowledge capital on strategic success, to achieve this goal the researchers from the theoretical literature and  related studies conclude to the construction of the scheme shows the hypothetical relationship between the variables, which was adopted job enrichment as independent variable while knowledge capital plays two roles, the first as an independent variable and the second as an intermediate

The research deals with the concept of rationalization of consumption, which is legitimately legitimized, by indicating the permissibility of consumption and its limits in Islam, the legitimate aspects of spending and its controls, and the concept of development in the Islamic economy. The researcher followed the descriptive method by presenting the legal texts and jurisprudential opinions and linking them to contemporary economic problems.The research has reached several conclusions, the most important of which is that the goal of Islam in relation to consumption is to inform the Muslim of the amount of economic maturity, and the truth of alms is good in money and religion. It is necessary for achieving maturity that both means

Background: Chronic kidney disease is a gradual loss of kidney function with diabetes and hypertension as the leading cause. Chronic kidney disease is one of these systemic diseases that can affect salivary contents. Aims: This study aimed to assess salivary immunoglobulin A, interleukin-6 and C- reactive protein in chronic kidney disease patients on hemodialysis and those on conservative treatment in comparison with control subjects. Materials and methods: Ninety subjects were included in this study divided into three groups: 30 patients with chronic kidney disease on hemodialysis for at least 6 months ago; 30 patients with chronic kidney disease on conservative treatment and 30 healthy control subjects. Secretory immunoglobulin A, inte

The present study considers to confirming the applicability of flow with double-sided square lid driven cavity flow by using the lattice Boltzmann equation with moment-based boundary conditions for no slip boundaries.  The boundary conditions are applied over the hydrodynamic moments of the lattice Boltzmann equations locally at each node. The investigation is carried out numerically for both single and multiple relaxation time models. To simulate two-sided lid driven-cavity flow, the top and bottom walls are moving with constant velocity while other walls are stationary. Various Reynolds numbers are used in a range of 100 and up to 5000. The present method shows the effect of the moving boundaries on the two symmetrical cavities t

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to