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Item type:Item, Arabic Synonyms Extraction using Singular Value Decomposition and Self-Organizing Maps(2026-06) Tala Sarabtah; Dr. Dia AbuZeinaArabic is a morphologically rich language where many words share the same root. This causes a well-documented problem when using automatic synonym extraction, where standard methods based on embeddings and cosine similarity -- the Baseline -- consistently return root-related word forms instead of true functional synonyms. This thesis addresses this problem. This thesis proposes a three-stage pipeline combining a pre-trained Arabic language model (BERT), SVD, and the SOM. BERT generates contextual word representations; SVD reduces the embedding dimensionality of any given word to suppress noise; and the SOM reorganises the space so that functionally equivalent words cluster together rather than root-related ones. The SVD-SOM pipeline was trained on approximately 3,800 Arabic articles covering 157 target words from variety of domains including education, economics, law, and media. A key limitation of the SVD-SOM pipeline is the absence of a standard Arabic synonym evaluation benchmark, which makes performance measurement inherently difficult. Evaluation was therefore conducted by comparing the SVD-SOM pipeline output against the Microsoft Word Arabic Thesaurus and several established Arabic online dictionaries (Almaany, Al-Maajim, Arabic Wiktionary), and the system was shown to retrieve true functional synonyms directly and reliably. To make this comparison quantitative, a lexically-grounded weighted synonym evaluation (the WSS@k and wDCG@k measures, defined in Chapter 2) scores each retrieved candidate on a 0--1 closeness scale and rewards ranking true synonyms near the top of the list. The results are clear. For the word تعليم (Education), the Baseline ranked terms such as أستاذ (Professor) and طلابي (Student-related) within its top-15 output -- neither of which is a functional synonym. The proposed SVD-SOM pipeline ranked تدريس (Teaching) and تثقيف (Enlightenment) in the top two positions, exactly matching the Microsoft Word Arabic Thesaurus. This pattern was reproduced consistently across the case studies presented in Chapter 5. A quantitative cross-validation using an architecturally independent Sentence-BERT model across all 2,512 experimental scenarios showed a modest but consistent improvement of the SVD-SOM pipeline over the Baseline. Future work should prioritise building a publicly available Arabic synonym benchmark and extending the SVD-SOM pipeline to include Arabic dialects and downstream applications such as information retrieval.Item type:Item, Nonlinear Seismic Response of Strengthened Reinforced Concrete Frames: A Comparative Finite Element Investigation Using CFRP, RC Jacketing, and UHPC(2026-06) Dunya Firas Kasem Abu Mayyaleh; Dr. Belal AlmassriThis study investigates the seismic performance of reinforced concrete (RC) frames strengthened using different retrofitting techniques under cyclic loading conditions through advanced finite element simulation. A three-dimensional nonlinear finite element model was developed in ABAQUS and calibrated using the experimental results of a previously tested RC frame reported in the literature. Concrete behavior was simulated using the Concrete Damage Plasticity (CDP) model, while reinforcing steel was modeled using an elastoplastic constitutive relationship. The numerical model achieved a high level of agreement with the experimental response, with an accuracy of approximately 95.12% in predicting the peak lateral load, as well as accurately capturing stiffness degradation, crack propagation, and hysteretic behavior. Following validation, three strengthening techniques were investigated, including CFRP strengthening, RC jacketing, and UHPC jacketing. The results demonstrated that all strengthening methods enhanced the seismic behavior of the frame; however, UHPC strengthening exhibited the best overall performance, achieving an increase of approximately 88.5% in load-carrying capacity, 44.73% in ductility, and significant improvement in stiffness and energy dissipation capacity. The findings confirm the effectiveness of advanced strengthening techniques, particularly UHPC jacketing, in improving the seismic resistance and structural performance of deficient RC frames. Keywords: Reinforced Concrete Frames, Seismic Strengthening, Cyclic Loading, Nonlinear Finite Element Analysis, ABAQUS, CFRP, RC Jacketing, UHPCItem type:Item, Features of Videographics Produced by (AJ+) Arabic and Distributed via Facebook(2026) هيا مروان امريش; د. حافظ عاكسرهThis study aimed to reveal the features of videographics produced by the )AJ+( Arabic platform and distributed via Facebook by analyzing its technical and structural characteristics alongside audience engagement patterns, addressing the central question: "What are the features of videographics produced by )AJ+( Arabic for its digital platform on Facebook?" Employing a mixed-methods approach with quantitative and qualitative content analysis tools, the study examined a sample of 21 videographic clips selected over seven consecutive months (three clips monthly representing the beginning, middle, and end of each month) from the )AJ+( Arabic Facebook page. Theoretically anchored in the Diffusion of Innovations Theory—to explain the adoption of videographics as an emerging digital format—and Semiotic Theory—to analyze the semantic and persuasive dimensions of visual symbols—the study revealed that )AJ+( Arabic heavily relies on modern graphic elements and intensive visual processing in short-form videos to boost engagement, effectively leveraging color schemes, iconography, and visual storytelling to simplify complex information for digital audiences. Consequently, the study recommends that Arab media institutions refine their digital visual production strategies and calls for further academic research in this understudied domain, underscoring its own contribution to bridging a significant knowledge gap in Arab digital journalism, video production, and visual narrative studies.Item type:Item, The impact of Project Management Knowledge Areas (PMKA's) according to PMBOK on the quality of infrastructure projects in the municipalities of the Southern Hebron Governorate(2026-06) Shaymaa Nihad Sweitti; Dr. Marwan JaloudThis study aimed to examine the impact of project management knowledge areas and its relationship with the quality of infrastructure projects implemented by municipalities of Southern Hebron Governorate. A quantitative explanatory cross-sectional design was used, and a census survey approach, targeting the entire population of 103 of municipal engineers and heads of project departments. A total of 95 valid questionnaires were returned and included in the statistical analysis. Descriptive and inferential statistics (PLS-SEM) through Smart-PLS4 to analyze data. The findings revealed that project management knowledge areas positively affected the quality of infrastructure projects, where project procurement management had the strongest positive effect, followed by project resources management. However, project schedule management showed a positive but statistically non-significant effect on the quality of infrastructure projects. The study recommended increasing awareness among project management staff by conducting training programs and workshops on project management best practices to ensure quality, ensuring full coordination among municipal departments, and clearly allocating responsibilities and powers to minimize issues that hinder effective project management implementation.Item type:Item, Enhancing IoMT Security using Multi-Layer Authentication: ZTM and ML-Based Case Study(2025-11) Basma Mohammad Abu Sabha; Dr. Radwan TahboubThe growing use of the Internet of Medical Things (IoMT) in ambulances enables real-time transmission of patients’ vital signs to hospitals. However, this exchange is increasingly exposed to cyber threats, especially Distributed Denial of Service (DDoS) and data injection or spoofed attacks that can delay critical decisions and compromise patient safety. This loMT system is subject to various kinds of attacks across multiple layers. While many existing solutions focus on securing medical data through encryption or blockchain in a mobile medical environment, they do not focus on detecting anomalies during transmission or analyzing network and data content behavior in real-time. This thesis proposes a real-time security framework for Emergency Medical Services (EMS) that continuously monitors and analyzes both levels, network and data content, based on the principle of “never trust, always verify.” The aim is to detect specific threats and take immediate action in real time before the data reach the hospital. The model integrates Machine Learning (ML), a Signature Intrusion Detection System (SIDS), the Zero Trust Model (ZTM), and Two-Factor Authentication (2FA) to simulate the application at two levels: (1) DDoS attacks at the network level with a general description and less simulation; (2) data injection attacks at the content level with focusing and discussion in detail at this level. Once an attack is detected, the system applies an isolation mechanism that either isolates the compromised IoMT device from its network at the ambulance in case data injection comes from that device, or disconnects the ambulance’s network interface in case of DDoS at the network level, preventing further propagation of malicious traffic. ML classifiers such as Decision Tree (DT), K-Nearest Neighbors (KNN), and Random Forest (RF) are trained in the cloud represented in our work by the hospital and then tested at the edge node (ambulance), which is the closest point to the data collection to ensure real-time decision-making and optimize resource usage under the constraints of mobile medical environments. The simulation results demonstrate that the DT model achieved the best performance for multiclass classification at the data-content level (attack, medical issue, normal), with an overall accuracy of 98.096% and an Matthews’ Correlation Coefficient (MCC) of 97.172%. This compared with other models, including RF with an accuracy of 98.01% and MCC of 97.055%, and KNN with an accuracy of 97.72% and MCC of 96.56%.