In recent years, higher education has witnessed a significant shift toward digital environments, with e-exams becoming an essential tool for academic assessment due to their speed, efficiency, and reduced administrative burdens. However, the adoption of e-exams in Libyan educational institutions, particularly at the Faculty of Science in Gharyan, still faces challenges stemming from technical, organizational, and human factors. This study aimed to explore the main challenges hindering the implementation of e-exams and analyze the potential role of blockchain technology in enhancing security and reliability. A descriptive methodology was employed, with data collected through a questionnaire distributed to faculty members. Results revealed that the most significant barriers included weak ICT infrastructure, a lack of administrative and technical support, and the absence of reliable and user-friendly e-exam systems. Additional challenges included unclear policies, a lack of awareness of the benefits of e-exams, and concerns about cheating and technical glitches. On the other hand, individual factors, such as resistance to change and the difficulty of designing e-exams, showed a relatively smaller impact. The findings highlight the need for stronger infrastructure, ongoing training, and clear institutional policies as prerequisites for the successful adoption of e-exams. Furthermore, the integration of blockchain technology offers a promising solution by ensuring consistent exam records, secure student identity verification, and transparent assessment processes. The study concludes that developing more reliable and effective e-assessment systems in Libya requires collaboration between technical and Libyan educational institutions, with blockchain technology considered a strategic option for enhancing trust and security in higher education.

Abstract

Complete dentures remain an essential treatment option for edentulous patients, especially in regions where implant-supported prostheses are limited. Successful prosthodontic outcomes require systematic execution of clinical steps, including patient examination, impression taking, custom tray fabrication, jaw relation recording, try-in, and post-insertion management. This study aimed to assess the knowledge of complete denture fabrication among fourth-year dental students. A descriptive cross-sectional design was employed among 37 students (94.9% response rate) enrolled in 2024 at the Department of Prosthodontics, Faculty of Oral and Dental Medicine, Gharyan University. Data were collected using a validated, self-administered questionnaire covering patient assessment, primary and final impressions, custom tray design, jaw relation records, occlusal plane orientation, articulator use, try-in procedures, and post-insertion care, and analyzed descriptively.

All students (100%) reported performing histories, oral examinations, primary impressions with alginate, and centric relation registration. Knowledge gaps were observed in advanced procedures: 75.7% selected light-cure acrylic for custom trays, awareness of spacers, stops, and relief holes was limited, polyvinylsiloxane was the most frequently used final impression material (62.2%), yet 59.5% disinfected impressions improperly with water alone. None used a face bow or semi-adjustable articulators, relying solely on non-adjustable types. Students demonstrated solid foundational knowledge of basic procedures; however, deficiencies in custom tray design, post-palatal seal determination, impression disinfection, and articulator use indicate a need for enhanced clinical teaching and practical training to meet current prosthodontic standards.

Keywords. Complete Denture, Dental Students, Prosthodontics, Impression Technique.

This study integrates compliance evaluations and organic solvent risk assessments to enhance laboratory safety in Gharyan University chemistry laboratories. Six laboratories (Analytical, Biochemistry, Organic, Physical, Inorganic, and Research) were assessed for safety compliance across five elements: storage, PPE, usage areas, emergency management, and documentation. Concurrently, a systematic risk assessment was performed for seven common organic solvents (ether, chloroform, benzene, methanol, hexane, acetone, and ethanol), considering inherent hazard severity and laboratory-specific probability. Results showed moderate overall compliance (26.09%-38.26%), with the organic laboratory exhibiting the highest. "Emergency Management" was a universal weakness (20.00% compliance). Risk assessment revealed 62.5% of solvent-laboratory combinations posed very high risks, with ether, chloroform, and benzene consistently hazardous. A clear link emerged between laboratory compliance and risk profiles; higher compliance correlated with lower solvent risks. This research provides empirical evidence that improved safety compliance mitigates risks, offering a novel framework for integrated safety assessment in academic settings. Findings emphasize strengthening safety management, particularly in emergency preparedness and PPE use, to reduce risks from organic solvents in university chemistry laboratories. Keywords: organic solvents, risk assessment, compliance analysis, chemical hazards, safety management.

This study presents a quantitative descriptive analysis comparing the performance of human simultaneous interpreters (H-SI) and AI-driven interpreting systems (AI-SI) using deep learning architectures specifically Transformer-based models (e.g., mBART, Wav2Vec 2.0 + NMT pipelines) in real-time multilingual contexts. Drawing on a corpus of 1,248 interpreted segments extracted from TED Talks (English–Spanish, English–Mandarin), we evaluate output quality across six empirically derived metrics: lexical accuracy, syntactic fluency, temporal alignment, semantic fidelity, discourse cohesion, and error density. Data were collected from 30 professional human interpreters and three state-of-the-art AI systems (Google Translate Live, DeepL Pro, and a fine-tuned mBART-50 model). Statistical analyses (descriptive statistics, Mann-Whitney U tests, and Kruskal-Wallis H tests) reveal that while AI-SI outperforms H-SI in lexical accuracy (M = 92.4%, SD = 3.1) and temporal alignment (M = 0.87s lag, SD = 0.21), human interpreters demonstrate significantly superior performance in semantic fidelity (M = 86.7% as well as. 71.3%, p < .001) and discourse cohesion (M = 84.1% as well as. 63.9%, p < .001). Error density was significantly lower in human output (Mean Rank = 47.3 as well as. 29.1, p = .002). Findings suggest that current AI systems, despite algorithmic advancements, remain deficient in pragmatic and contextual adaptation core competencies rooted in human cognitive-linguistic processing. The study contributes a validated metric framework for evaluating AI interpreting performance and calls for hybrid human-AI paradigms in professional settings. Implications for interpreter training curricula and machine translation pedagogy are discussed

Keywords: Simultaneous Interpreting، Artificial Intelligence، Deep Learning، Quantitative Descriptive Research، Machine Translation، Human-Machine Comparison، Interpreter Performance، Transformer Model