This research studies the use of code-switching in English classes at secondary schools in Derj. Code-switching means using Arabic with English during the lesson. Many English teachers face problems when students do not understand explanations given only in English. For this reason, some teachers use Arabic to help students understand the lesson better. The study focuses on teachers’ opinions about using code-switching in the classroom. A questionnaire was used to collect data from 32 English teachers. Simple statistical methods were used to check the reliability of the questionnaire and to analyze the results. The findings show that most teachers accept the use of code-switching, but they do not support using it all the time. Teachers mostly use Arabic when explaining difficult vocabulary, grammar rules, or classroom instructions. They also believe that limited use of Arabic makes students feel more comfortable and willing to participate. However, teachers strongly agree that using Arabic too much can reduce students’ chances to practice English. For this reason, they prefer to keep English as the main language in class and use Arabic only when it is really necessary

The phenomenon of big data has become a defining characteristic of the technological age. Consequently, new challenges arise, including the types of data and the scale of the information available; traditional relational databases frequently prove inadequate in addressing these demands, resulting in the proliferation of NoSQL databases. This research presents a comprehensive review of NoSQL-based systems and their methodologies for managing extensive data. Furthermore, this study examines the primary categories of NoSQL databases, their optimal attributes, and their strategies for addressing the contemporary "5Vs" of Big Data: volume, velocity, variety, value, and veracity. Additionally, the research highlights the opportunities presented by NoSQL technologies in emerging fields, such as the Internet of Things (IoT), artificial intelligence, and machine learning, alongside the associated risks, including trade-offs in consistency, security challenges, and the lack of standardization. By integrating classification, comparison, and analysis, this review offers a unified perspective that can aid researchers and practitioners in the effective selection and implementation of NoSQL solutions within Big Data contexts.

Abstract:

This study examines the challenges of integrating theoretical preparation with practical training in Colleges of

Education, focusing on the Faculty of Education in Tiji, University of Zintan. Using a descriptive-analytical

method, data were collected from 20 faculty members and 33 student teachers through structured questionnaires.

Results showed that both groups face difficulties in linking theory and practice, with faculty members reporting

higher role conflict (M = 3.70) than students (M = 3.39), a statistically significant difference (p = 0.013). The

dominance of theoretical content, limited administrative support, and short practicum periods contribute to this

gap. The study recommends curriculum redesign, better supervision, and investment in modern educational

laboratories to strengthen the connection between academic and professional preparation.

Keywords: Faculties of Education, Faculty Members, Student teachers, Practicum (Practical Training), Role

Conflict of Education

Tabular data is a common format for storing information in rows and columns, representing data entries and their associated features. Existing deep learning models designed for tabular data often rely on uni-dimensional feature selection mechanisms, which fail to adequately capture contextual dependencies of feature importance. This can result in missed crucial interactions, bias towards high-impact features, and limited generalization in attention generalization. To overcome these challenges, this study introduces a novel Iterative Feature Exclusion module that enhances the estimation of feature importance. The proposed module iteratively excludes a single feature from the input data and computes attention scores that represent the impact of each feature on the prediction. By aggregating the attention scores across iterations, the module generates a refined representation of feature importance that captures both the global and local feature interactions. The effectiveness of the proposed module was evaluated on four public datasets, and the results consistently demonstrated its superior performance over state-of-the-art methods and baseline models in both feature ranking and classification tasks.

The code is publicly available at https://github.com/abaraka2020/Iterative-Feature-Exclusion-Ranking-Module and https://github.com/mohalim/IFENet

Background: Gastrointestinal stromal tumors (GISTs) are the major mesenchymal tumors of the gastrointestinal 

tract (GIT), which progress due to the mutation in TKI and PDGFR-α receptors. It shows asymptomatic conditions 

however, become malignant due its large size and produce abdominal pain. Several surgical managements have 

been applied to diagnose patients with GIST, but these increase the risk of tumor recurrent rate. Therefore, 

adjuvant therapies contribute to preventing this challenge with several side effects and post-operative 

complications. A 42-year-old male patient from Libya with acute abdomen and GIST in small bowel (Jejunum) 

was diagnosed with complete excision besides lymph node dissection..Aim: To investigate numerous case reports 

through literature analysis regarding GIST along with a case presentation of the patient. Method: This research 

employed a literature-based study design. Previous case reports were screened from various online research 

platforms, including the National Center for Biotechnology Information (NCBI) and PubMed. A total of 37 cases 

were included through the search phrase "Case report on Gastrointestinal stromal tumor” .. Results: The findings 

revealed patient mean age (56.35) with most of the small intestine GIST (6.42 cm) site of origin, antibodies 

expression, and maximum mitotic cell counts.. Conclusion: The cases reported a risk of recurrence among GIST 

patients due to high mitotic counts and tumor size. Future researchers should address immune markers to reduce 

tumor recurrence among GIST patients. Stakeholders should implement proper treatment procedures with 

effective diagnostic methods. 

Keywords: Gastro-Intestinal Stromal Tumors (GISTs), Perforation, Imatinib, Acute abdomen, Tyrosine Kinase 

Inhibitor (TKI), Small Bowe