Free-space optical communication (FSO) is an innovative technology that uses light rays to transmit data over the air, providing a wireless alternative to traditional cables. However, the performance of this system is greatly affected by severe weather conditions such as fog, rain, humidity, and atmospheric turbulence, which degrade the quality of communication and pose challenges that require precise solutions. This paper aims to analyze the impact of these environmental factors on the performance of the FSO system using three main criteria: the bit error rate (BER) to evaluate the accuracy of data transmission, the signal-to-noise ratio (SNR) to assess the quality of communication, and the total attenuation induced by each atmospheric condition to quantify its effect on signal degradation. The On-Off Keying (OOK) modulation technique was adopted due to its simplicity and widespread use in optical communication systems. Using MATLAB, simulation models representing different weather scenarios were employed to analyze the performance of the FSO system under varying environmental conditions. The effects of atmospheric changes on system behavior are studied and evaluated to gain a deeper understanding of its operation and limitations. Also, in this paper a further enhancement is introduced to the system by integrating Multiple-Input Multiple-Output (MIMO) technology to improve its performance under challenging conditions as a solution.

The approach to delivering education in Libya has undergone a significant transformation in recent years. Various tools now support the educational process, shifting it from rote learning to creativity, interaction, and skill development. This study aims to examine the feasibility of implementing e-learning in Libyan universities. A statistical analysis was conducted on approximately 400 samples to identify patterns. The findings highlight the importance of e-learning in enhancing the quality of education by outlining its benefits and limitations. Moreover, it was found that employees consider e-learning an effective alternative to traditional education. Despite this consensus, concerns remain regarding the availability of sufficient infrastructure. 

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.

This study presents a strategic synthetic approach to functionalize the traditionally unreactive C3 position of the benzothiophene core. A series of 3-chloro-N-(aryl)benzothiophene-2-carboxamides (2a–d) were synthesized in good to excellent yields (53–83%) via amidation of 3-chloro-2-chlorocarbonylbenzothiophene[b]thiophene (1).Subsequent oxidation with hydrogen peroxide in acetic acid afforded the corresponding sulfone derivatives (3a–d), a transformation that reduced aromaticity and activated the C3 position. This activation enabled anucleophilic aromatic substitution, where chlorine in sulfone 3b was replaced by ethylamine, isopropylamine, and diethylamine, yielding novel 3-aminobenzothiophene-1,1-dioxides (4a–c) in moderate yields (50–64%). All new compounds were fully characterized by IR, ¹H NMR, and ¹³C NMR spectroscopy. The synthesized derivatives were evaluated in vitro for antimicrobial activity against Gram-positive bacteria (Staphylococcus aureus, Bacillus subtilis), Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa), and fungal strains (Candida albicans, Crysosporium pannical, Aspergillus niger, Rhizopus oryzae). While overall activities were slight to moderate compared with standard drugs, compounds 3a and 3b displayed a promising broad-spectrum profile. These findings establish an efficient and versatile methodology for accessing C3-functionalized benzothiophenes, providing a valuable foundation for the design of new lead structures against resistant microbes.

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