Owing to their remarkable characteristics, refractory molybdenum nitride (MoN x )-based compounds have been deployed in a wide range of strategic industrial applications. This review reports the electronic and structural properties that render MoN x materials as potent catalytic surfaces for numerous chemical reactions and surveys the syntheses, procedures, and catalytic applications in pertinent industries such as the petroleum industry. In particular, hydrogenation, hydrodesulfurization, and hydrodeoxygenation are essential processes in the refinement of oil segments and their conversions into commodity fuels and platform chemicals. N-vacant sites over a catalyst’s surface are a significant driver of diverse chemical phenomena. Studies

Owing to their remarkable characteristics, refractory molybdenum nitride (MoNx)-based compounds have been deployed in a wide range of strategic industrial applications. This review reports the electronic and structural properties that render MoNx materials as potent catalytic surfaces for numerous chemical reactions and surveys the syntheses, procedures, and catalytic applications in pertinent industries such as the petroleum industry. In particular, hydrogenation, hydrodesulfurization, and hydrodeoxygenation are essential processes in the refinement of oil segments and their conversions into commodity fuels and platform chemicals. N-vacant sites over a catalyst’s surface are a significant driver of diverse chemical phenomena. Studies on

Background: The main purpose of this study is to find if there is any correlation between the level of C-reactive protein (CRP) in gingival crevicular fluid with its serum level in chronic periodontitis patients and to explore the differences between them according to the probing depth. Materials and methods: Forty seven male subjects enrolled in this study. Thirty males with chronic periodontitis considered as study group whom further subdivided according to probing depth into subgroup 1 with pocket depth ≤6mm, subgroup 2 with pocket depth >6mm. The other 17 subjects considered as controls. For all subjects, clinical examination where done for periodontal parameters plaque index (PLI), gingival index (GI), bleeding on probing (BOP),

The education, especially higher education, is an essentially factor in the progress of any society, if we consider the higher education, represents the top  of  the education`s  pyramid which take part in developing the human  resources and provide the human staff to raise the productive efficiency, and improve the social , economic level                                  

In order to face the increasing importance of higher education, great capabilities and expenditures must be available in a continous way, such expe

Objective : Alzheimer’s disease (AD) continues to be a major challenge because handling high-dimensional data is time-consuming and expensive due to its complexity. A large feature space often increases computational costs and reduces model interpretability. This study addresses this problem by evaluating and comparing multiple feature selection techniques to identify the most informative biomarkers for AD diagnosis.

Methods : Our study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to implement and test three feature selection a

This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,

Expansion the engineering consultancy offices in the universities of Iraq, about (14) offices, leading to increas the competition between them, especially after the great trends of Iraqi government agencies to use the academic experiences and their efficiencies in general, due to non-existence of the engineering qualification in the government institutions to do the engineering designs ,supervision of projects and other engineering works which are practicing by the engineering consultancy offices in order to get the best performance of the work.Within this serious competition, needing a specific approach to enable government agencies to choose the optimal and alternative consultancy office to meet specific project and not rely on cronyis

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an