The multimetric Phytoplankton Index of Biological Integrity (P-IBI) was applied throughout Rostov on Don city (Russia) on 8 Locations in Don River from April – October 2019. The P-IBI is composed from seven metrics: Species Richness Index (SRI), Density of Phytoplankton and total biomass of phytoplankton and Relative Abundance (RA) for blue-green Algae, Green Algae, Bacillariophyceae and Euglenaphyceae Algae. The average P-IBI values fell within the range of (45.09-52.4). Therefore, water throughout the entire study area was characterized by the equally "poor" quality. Negative points of anthropogenic impact detected at the stations are: Above the city of Rostov-on-Don (1 km, higher duct Aksai) was 38.57 i

Background: Atrial fibrillation (AF) stands as the most prevalent cardiac arrhythmia, with associated risks of stroke and systemic thromboembolism. While vitamin K antagonists, specifically warfarin, have historically been the mainstay for stroke prevention in AF, they come with inherent limitations.

Aim: This review seeks to offer a comprehensive analysis of the efficacy, safety, and clinical advantages of novel oral anticoagulants (NOACs) compared to traditional warfarin in AF management.

Method: A meticulous examination of pivotal clinical trials, meta-analyses, and recent research publications was conducted.

   In this paper, we investigate the basic characteristics of "magnetron sputtering plasma" using the target V₂O₅. The "magnetron sputtering plasma" is produced using "radio frequency (RF)" power supply and Argon gas. The intensity of the light emission from atoms and radicals in the plasma measured by using "optical emission spectrophotometer", and the appeared peaks in all patterns match the standard lines from NIST database and employed are to estimate the plasma parameters, of computes electron temperature and the electrons density. The characteristics of V₂O₅ sputtering plasma at multiple discharge provisos are studied at the "radio frequency" (RF) power ranging from 75 - 150 Wat

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

This study aims at examining and confirming the patterns of phenetic relationships and the levels of variations within and among the species of Lotus L., 1753 in Egypt by using morphometric analysis techniques. We have evaluated 24 morphological characters from about 300 herbarium specimens representing 19 species of Lotus that are currently recognized. Based on numerical analyses of macromorphological characters (cluster analysis, principal coordinate analysis and principal component analysis), 19 species of Lotus were recognized from Egypt. These species were clustered in six species-specific groups: (I) Lotus halophilus Boiss. & Spruner, L. angustissimus L., L. glinoides Delile and L. schimperi Steud. ex Boiss., (II) Lotus glaber

In this paper, the effect size measures was discussed, which are useful in many estimation processes for direct effect and its relation with indirect and total effects. In addition, an algorithm to calculate the suggested measure of effect size was suggested that represent the ratio of direct effect to the effect of the estimated parameter using the Regression equation of the dependent variable on the mediator variable without using the independent variable in the model. Where this an algorithm clear the possibility to use this regression equation in Mediation Analysis, where usually used the Mediator and independent variable together when the dependent variable regresses on them. Also this an algorithm to show how effect of the

In current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL^-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi

The study aims at diagnosing the importance of environmental analysis (external, industrial and internal) in the organization's strategy for the development of public organizations. The theoretical problem derived from the existence of a knowledge gap for studies and research that dealt with the variables of the study. Three public organizations were selected as the study society (the Civil Defense Directorate, the Directorate of Nationality, Passports and Residency, the General Traffic Directorate). The sample of the study was a sample of (215) managers (managers, department managers, Analysis of the data and hypothesis testing using the appropriate statistical tools, Pearson, the simple and multiple regression coefficient to te

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al