Urban regeneration and gentrification are complex, interconnected processes that significantly shape cities. However, these phenomena in the Middle East and North Africa (MENA) region are often understudied and typically viewed through a Western lens. This systematic review of literature from 2010 to 2024 addresses this gap by synthesizing a comprehensive framework for understanding urban regeneration-led gentrification in MENA countries. The review delves into key themes: Gentrification contexts, the regeneration process, gentrification accelerators, and the aftermath of gentrification. It explores the diverse motives behind urban regeneration, identifies key stakeholders, and analyzes catalysts of gentrification. Findings reveal that informal areas and deteriorated heritage sites in major cities are most susceptible to gentrification. The study also highlights the critical issue of insufficient community participation and proposes a participation evaluation framework. The unique socioeconomic and political factors driving gentrification in the MENA region underscore the necessity of context-specific approaches, facilitating the identification of regional similarities and differences. Conclusively, the review asserts that gentrification is a cyclic process, necessitating core interventions through enhanced regeneration strategies or displacement plans to mitigate its effects.

This research quantitatively examines how technology-mediated formative assessment techniques affect student learning outcomes in middle school education. The research investigates the correlation between instructors’ technology use, attitudes, and student performance in several academic disciplines using surveys and evaluations conducted with teachers and students. Results show strong positive connections between how often technology is used, the specific digital tools used, how effective technology-mediated formative assessment is judged to be, and the results of student learning. On the other hand, obstacles to implementation were shown to have a negative relationship with student accomplishment. The research emphasizes that technology-mediated formative assessment is more successful in some subjects, emphasizing the necessity to customize teaching methods for each subject’s requirements. The study revealed a positive correlation between student learning outcomes and the frequency of technology use, the types of digital tools used, and the perceived effectiveness of technology-mediated formative assessment. These results suggest ways to improve the use of technology and formative assessment in middle school instruction.

In the present work, a series of butyl methacrylate/1-hexene copolymers were synthesized, and their efficiency as viscosity index improvers, pour point depressants, and shear stabilizers of lube oil was investigated. The effect of 1-hexene molar ratio, type, and concentration of Lewis acids on the incorporation of 1-hexene into the copolymer backbone was investigated. The successful synthesis of the copolymers was confirmed through FTIR and 1H NMR spectroscopy. Results obtained from quantitative 1H NMR and GPC revealed that an increase in the molar ratio of 1-hexene to butyl methacrylate, along with concentration of Lewis acids led to an increase in 1-hexene incorporation and a reduction in Mn and Ð. Similar trends were observed when the Lewis acid changed from AlCl₃ to organometallic acids. The maximum 1-hexene incorporation (26.4%) was achieved for sample BHY3, with a [1-hexene/BMA] ratio of 4 mol% and a [Yb(OTf)₃/BMA] ratio of 2.5 mol%. Evaluation of the synthesized copolymers as lube oil additives demonstrated that the viscosity index was more significantly influenced by samples with higher molecular weight. Sample BHA13 represents maximum VI of 137. The copolymer containing Yb(OTf)₃ as a catalyst exhibited superior efficiency as a pour point depressant. Furthermore, sample BHY3 showed the lowest shear stability index (6.4).

Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.