SummaryBackground: Rotavirus infection is the most commoncause of watery viral diarrhea in children younger than 5 years of age; it is a major cause of childhood morbidity and mortality.Objective:The aim of the study is todetermine the clinical picture, age distribution of patients with rotavirus infection and their maternal educational background.Patients &methods: A total of 202 patients suffering from diarrhea were included in this study, over 6 months period( from 1stof March 2011to 30th of August 2011),in Children Welfare Teaching hospital. History and physical examinationwere carried out, anthropometrics measures were done and plotted on Centers for Disease Control& World Health Organization charts to determine the nut

In this paper, some commonly used hierarchical cluster techniques have been compared. A comparison was made between the agglomerative hierarchical clustering technique and the k-means technique, which includes the k-mean technique, the variant K-means technique, and the bisecting K-means, although the hierarchical cluster technique is considered to be one of the best clustering methods. It has a limited usage due to the time complexity. The results, which are calculated based on the analysis of the characteristics of the cluster algorithms and the nature of the data, showed that the bisecting K-means technique is the best compared to the rest of the other methods used.

In this research, we highlight the most important research related to the mixed ligand complexes of the drug trimethoprim (TMP), and for the past 7 years where this drug has been used as a chelating ligand and gives stability to the complexes with ions of metal elements where these complexes, prepared and diagnosed, and for some research the bacterial activity was studied against different types of bacteria.

Maxillofacial trauma in females is not widely reported. This study aimed to analyze the clinical characteristics and the patterns of maxillofacial injuries in females and to determine the differences in these patterns among different causes. This retrospective study analyzed several variables, including demographic, social, injury-related, and treatment-related variables, and compared these variables in relation to the main etiologies of maxillofacial trauma. The main etiologies of maxillofacial injuries involving females were assault, followed by road traffic accidents, and falls. There were significant differences in relation to the 3 etiologies in age groups (P= 0.002) and marital status (<

Debate is a teaching strategy in nursing education that enhances students' critical thinking. Although debate can be an effective teaching strategy, it is not without limitations. This article discusses the advantages and disadvantages of debate as a teaching strategy in nursing. Also, evaluating debates and choosing topics are highlighted. [ J Contin Educ Nurs . 2021;52(6):263–265.]

Одной из активно развивающихся отраслей лексикологии является неология, объект её изучения - новое слово или неологизм. В задачу неологии входит выявление новых слов и новых значений у уже существующих в языке слов, анализ причин и способов их появления, описание факторов, влияющих на появление нового в лексической системе языка, разработка языковой политики в отношении новых номинаций.  Лексикограф

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N