The aim of the research is to demonstrate the impact of organizational diversity management on achieving success, The problem of research is to create an appropriate work environment that stimulates everyone regardless of each dimension of diversity, So as to reach the best levels of success, The importance of research is to identify how the organization in question can manage organizational diversity effectively and contribute to its success, The research started from two main hypotheses which were derived from sub-hypotheses to identify the correlation and influence between the search variables by analyzing the answers of the research sample which included (81)

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

The main goal of this paper is to introduce and study a new concept named d^*-supplemented which can be considered as a generalization of W- supplemented modules and d-hollow module. Also, we introduce a d^*-supplement submodule. Many relationships of d^*-supplemented modules are studied. Especially, we give characterizations of d^*-supplemented modules and relationship between this kind of modules and other kind modules for example every d-hollow (d-local) module is d^*-supplemented and by an example we show that the converse is not true.

The research includes a clinical study of Preptin with other parameters. The normal value of preptin in hypothyroidism (2638.4±280.0) in female while (2960.4±256.6) in male, in hyperthyroidism (589.0±90.1) in male, while in female (993.2±103.9), diabetes (2465.6±282.4) in female, in male (2085.5±282.8), in diabetes & hypothyroidism (3314.3±177.3) in male,(3179.4±265.7) in female, but control group in female (427.8±60.4), in male (384.7±62.4) at age (20-45) years they were divided into five groups: group one (G1) consisted of 30 hypothyroidism. The two group (G2) consisted of 30 patients with hyperthyroidism. And three group (G3) consisted of 30 healthy group, four group (G4) consisted of 30 patient with diabetes, and five group (G

Drug solubility and dissolution remain a significant challenge in pharmaceutical formulations. This study aimed to formulate and evaluate repanglinide (RPG) nanosuspension-based buccal fast-dissolving films (BDFs) for dissolution enhancement. RPG nanosuspension was prepared by the antisolvent-precipitation method using multiple hydrophilic polymers, including soluplus®, polyvinyl alcohol, polyvinyl pyrrolidine, poloxamers, and hydroxyl propyl methyl cellulose. The nanosuspension was then directly loaded into BDFs using the solvent casting technique. Twelve formulas were prepared with a particle size range of 81.6-1389 nm and PDI 0.002-1 for the different polymers. Nanosuspensions prepared with soluplus showed a favored mean particle size o

Background: left ventricular hypertrophy is independent risk factor for cardiovascular morbidity and mortality. The presence of diabetic complications such as autonomic neuropathy and retinopathy may predict cardiac structural changes in diabetic patients. Objective: To explore the chance of occurrence of left ventricular hypertrophy in diabetic patients and whether it is related to the presence of other diabetic complications. Methods: ninty seven (97) normotensive diabetic patients (57) type II with mean duration of diabetes of (12±6 y) and forty (40) type I with mean duration of (8±6 y) were studied by echocardiography and compared with 41 patients as control. Results: The LVMI was significantly higher in type II diabetics compared

Hypothesis CO2 geological storage (CGS) involves different mechanisms which can store millions of tonnes of CO2 per year in depleted hydrocarbon reservoirs and deep saline aquifers. But their storage capacity is influenced by the presence of different carboxylic compounds in the reservoir. These molecules strongly affect the water wetness of the rock, which has a dramatic impact on storage capacities and containment security. However, precise understanding of how these carboxylic acids influence the rock’s CO2-wettability is lacking. Experiments We thus systematically analysed these relationships as a function of pressure, temperature, storage depth and organic acid concentrations. A particular focus was on identifying organic acid conce

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra