Middle East College

Abstract: Beams commonly fail in shear due to diagonal tension, shear compression, or web shear, typically caused by insufficient reinforcement, overload, or material weaknesses. One solution is the use of fiber-reinforced polymer (FRP) wrapping, which enhances durability and strength. FRP, a composite of fibers and resin, exhibits superior mechanical properties such as impact resistance, stiffness, and corrosion resistance. This study investigates the effectiveness of Polypropylene Fiber Reinforced Polymer (PFRP) in strengthening reinforced concrete (RC) beams. Mechanical behaviour was assessed through compression, split tensile, and flexural tests. RC beams wrapped with PFRP were also analyzed in terms of shear strength using experimental and simulation methods. The results indicate higher strength in PFRP-wrapped beams compared to unwrapped ones.

The paper aims to quantify the intersectoral linkages that facilitate economic diversification in the Omani economy. The input-output analysis is used to assess the effects of inter-sectoral transactions for policy changes. The analysis is employed in development planning, impact assessment, and measuring external shocks. The short-run projected demand is forecasted through hypothetical positive and negative changes in final demand in each sector to predict changes in final demand. Income multipliers are derived by transposing the third-degree power series generation of the Leontief inverse matrix. The results suggest that the non-mining and quarrying sectors are strongly linked, with significant forward and backward linkages in determining value addition, output growth, and employment levels. The wholesale retail, fishing, public services, defense, health, and education sectors are creating employment opportunities. However, the employment and income multipliers indicate strong links with non-mining and services sectors. Policymakers should consider focusing on industries with strong connections. The robust linkages in the non-mining and quarrying sectors suggest that interventions in these sectors could help diversify the economy. The Omani economy requires an in-depth understanding of structural patterns, sectoral-level interventions, and consistency in policy measures.

This review examines the rapidly expanding landscape of Generative Artificial Intelligence (GenAI) in healthcare, focusing on how models such as GANs, VAEs, diffusion models, and large language models are being explored across medical imaging, clinical documentation, synthetic data generation, drug discovery, and decision-support workflows. Despite GenAI's growing influence, persistent challenges, including limited annotated datasets, concerns over model generalizability, privacy risks, and the opacity of generative architectures, underscore the need for careful evaluation and governance. Accordingly, this study aims to map current applications, assess methodological and ethical constraints, and identify future research opportunities. Using a structured search across ScienceDirect, Scopus, and other sources, the study follows a structured narrative review complemented by quantitative descriptive analysis of the literature. The review also adopts PRISMA-guided screening and standardized data extraction, the review synthesizes evidence from 110 studies published up to October 2025. The findings indicate that the literature frequently reports improvements in imaging quality, data augmentation, molecular modeling workflows, and clinical documentation through generative approaches, particularly in technically constrained settings; however, evidence of clinically validated impact remains uneven across domains. While issues of bias, hallucination, and limited interpretability persist as significant obstacles, imaging-focused applications appear comparatively more mature than decision-support and patient-level modeling tasks. Across domains, diffusion models are commonly associated with higher visual fidelity in biomedical image generation, whereas LLMs demonstrate promise in narrative-oriented tasks but require stronger factual grounding and external verification mechanisms. Overall, the evidence suggests that GenAI’s potential in healthcare is highly context-dependent and contingent upon robust validation frameworks, transparent governance, and human-in-the-loop oversight. The review concludes that responsible integration of GenAI—guided by ethical, legal, and clinical safeguards, will be essential for ensuring safe, equitable, and sustainable adoption in healthcare research, delivery, and policy.

Construction waste management and inventory are lacking in the Jordanian construction sector. With the dramatic population growth, the construction sector plays a significant role in developing the Jordanian economy. This article aims to develop a model to quantify construction and demolition (C&D) waste based on either project hyper-categorization or waste type, and to measure the degree of awareness and readiness of local contractors to benefit from C&D waste. Therefore, it classified projects further into construction and infrastructure projects. The construction project is also classified as new construction, renovation, or rehabilitation. These classifications help improve the management of construction waste. Moreover, a survey was conducted among construction stakeholders to increase awareness and assess their perceptions and readiness to adopt C&D waste management activities in their current or future projects. Finally, a strengths, weaknesses, opportunities, and threats (SWOT) analysis was conducted to heighten major stakeholders' awareness about the challenges of C&D and to develop an effective C&D waste management plan. The results of this research can help both decision-makers and stakeholders in the construction sector properly classify their waste, reduce and manage generated C&D waste, and lower overall costs. Finally, the SWOT analysis identifies hidden opportunities to promote and develop a future C&D waste management at all levels. Meanwhile, revenue from recycling C&D waste is expected to be about US$4.2/m2 of construction.

This review paper provides clear insight towards the issue of the innovative teacher and the innovative learner. Major research questions include (a) What is the role of education in innovation? (b) What are the opportunities that are presented for both the instructor and the learner to be innovative? (c) What are the challenges faced by the instructor and the learner in education for them to be innovative? (d) What is the nexus between African educational policies and innovation practices? (e) What is required to increase the link between education and innovation in Africa? To address demands of the questions, cross-sectional literature review was carried out through desktop literature search to gather already existing data. Published and grey literature with aspects related to Africa will be highly considered, although global literature was be included for comparison. Inclusion andexclusion of documents was determined by key words and abstract content. Literature show that educational innovations equip the instructors and learners for challenges faced by society and instructors are considered as key drivers of didactic innovations. Educational innovations expose instructors to new teaching methods and gives emphasis on lifelong learning. Learners have the opportunity to interact with the instructor and among themselves as well as the flexibility to learn at their own pace providing them flexibility. Nevertheless, literature identifies challenges that include poverty, digital literacy and internet connectivity, cultural issues, political factors, socio-economic factors and brain drain. The study recommends investment in research, promotion of practical activities, incorporating technologies with user friendly applications, creating supportive environments, and policy formulation and implementation.

Abstract Sustainability has become a critical focus in global supply chain strategies, yet small and medium enterprises (SMEs), especially in South Africa, face significant barriers in adopting green supply chain management (GSCM) practices. This study aims to investigate the drivers, barriers, and sustainability impacts of GSCM adoption among South African SMEs, with a focus on entrepreneurial contexts. Using a systematic literature review (SLR) guided by PRISMA, 45 peer-reviewed studies published between 2010 and 2025 were analysed. The study is theoretically grounded in the triple bottom line (TBL) framework and the diffusion of innovation (DOI) theory. Results indicate that GSCM adoption can significantly improve environmental, economic, and social sustainability. However, its implementation among South African SMEs is constrained by financial limitations, lack of awareness, technological gaps, and weak supply chain collaboration. Based on these findings, a theoretical framework was developed to explain the relationship between GSCM drivers, adoption barriers, and sustainability outcomes. The proposed framework is conceptual and was not empirically tested in this study. Therefore, future research should apply quantitative or mixed method approaches to validate and refine the model. This research contributes to sustainable entrepreneurship literature by offering a structured foundation for understanding GSCM integration in resource-constrained SME environments.

Vitamin D3, also known as cholecalciferol, is crucial for overall health and is linked to various aspects of well-being, including immune function and the prevention of chronic illnesses. Recent studies have highlighted the association between low levels of Vitamin D3 and an increased risk of cardiovascular disease (CVD), diabetes, and obesity, making it a significant public health biomarker. Given the high prevalence of these chronic diseases worldwide, monitoring of Vitamin D3 levels has become increasingly important, especially in regions where low sun exposure and poor dietary habits contribute to widespread deficiency. This deficiency increases the risk of developing chronic disease. This work proposes a point-of-care system using a three-electrode biosensor based on the Internet of Things (IoT) to rapidly and accurately measure Vitamin D3 levels. The system utilizes an immobilized working electrode surface with an enzyme that modulates the current flow between the working electrode and auxiliary electrode in response to the biomarker. This change in electrical activity enables the precise detection of Vitamin D3 within the physiological range.

Word embeddings are essential to natural language processing tasks because they contain a single word’s syntactic and semantic information. Word embeddings have been developed widely for numerous spoken languages across the globe like English. The research community needs to pay more attention to the Urdu language despite its significant number of speakers, which amounts to approximately 231.3 million individuals. Urdu is a complex language because word boundaries in Urdu are unspecified, as it does not employ delimiters between words. The compound word, a multiword expression, is a more complex word consisting of many strings or independent base words. Traditionally, compound words are identified during the word segmentation using bigram or trigram approaches. The challenge with these techniques is that they do not produce meaningful words. This study uses morphological rule-based compound words in Urdu text documents. For text representation, a self-trained morphological rule-based compound word embedding (Cword2vec) based on the word2vec model is proposed for Urdu text sentiment analysis. The performance of self-trained morphological rule-based compound word embedding was then evaluated using four well-known deep learning models, i.e., long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural networks (CNN), and convolutional LSTM (C-LSTM) for sentiment analysis. We also compare the performance of morphological rule-based compound words with traditional compound word identification techniques such as bigrams and trigrams. Regardless of the classification model, word embedding using our proposed morphological rule-based compound words outperformed in terms of precision, recall, F1 score, and accuracy than bigrams and trigrams.

Abstract Hydrogen storage plays a critical role in advancing hydrogen as a sustainable energy source. The materials used in hydrogen storage tanks must adhere to stringent standards, especially in hot climates where elevated temperatures can accelerate material degradation and affect storage performance. This review provides an overview of the key technologies used for hydrogen production, including steam reforming, electrolysis, thermolysis, and bio-photolysis. Additionally, the review delves into hydrogen liquefaction systems and discusses key performance indicators for their analysis. Cost estimation calculations have been conducted to assess both CapEx and OpEx for the MENA region, including the UAE. These costs have been calculated using Aspen HYSYS simulation software to evaluate both financial and performance aspects. The analysis was carried out for a production scale of 100 kg/hr of hydrogen, utilizing both nitrogen and mixed refrigerant (MR) as cold streams for liquefying hydrogen for transportation.