The article is devoted to football lexicon in Arabic and Russian. The charr acteristics that determine the coincidence of lexical units in the Arabic and Russian languages are revealed. Football related words and phrases exist in different languages and in most cases they are the same. With the develop ment of football in the world and thanks to the media, new words and terms have become firmly established in the language. In Arabic, Italian and Span ish words are often used, but there are original Arabic phrases, most often consisting of two words - sports journalists and commentators actively use them. The word »mercato« was borrowed from the Italian language; in addi tion, in the Arabic language there is a phrase »Bab al-inti

This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Reg

Asthma is a chronic respiratory disease highly prevalent worldwide. Recent studies have suggested a role for microbiome-associated gut–lung axis in asthma development. In the current study, we investigated if Resveratrol (RES), a plant-based polyphenol, can attenuate ovalbumin (OVA)-induced murine allergic asthma, and if so, the role of microbiome in the gut–lung axis in this process. We found that RES attenuated allergic asthma with significant improvements in pulmonary functions in OVA-exposed mice when tested using plethysmography for frequency (F), mean volume (MV), specific airway resistance (sRaw), and delay time(dT). RES treatment also suppressed inflammatory cytokines in the lungs. RES modulated lung microbiota and cause

Some experiments need to know the extent of their usefulness to continue providing them or not. This is done through the fuzzy regression discontinuous model, where the Epanechnikov Kernel and Triangular Kernel were used to estimate the model by generating data from the Monte Carlo experiment and comparing the results obtained. It was found that the. Epanechnikov Kernel has a least mean squared error.

Static loads exposing to mechanical components can cause cracks, which are lead to form stress concentration regions causing the failure of structure. Generally, from 80% to 90% of structure failure is due to initiation of the cracks. Therefore, it is necessary to repair the crack and reduce its effect on the structure where the effect of the crack is modelled as an additional flexibility to the structure. In the last few years, piezoelectric materials have been considered as one of the most favourable repairing techniques. The piezoelectric material converts the applied voltage on it to a bending moment to counter the bending moment caused by the external load on the beam at the crack location. In this study, the design of the piez

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,