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

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.

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-This qualitative case study investigates the effectiveness of AI-driven translation technologies in mediating cultural understanding within English Language Teaching (ELT) contexts in Libyan higher education. Drawing on data from semi-structured interviews with 55 ELT instructors, 24 classroom observations across six universities, and document analysis of student translation outputs and institutional curricula, the study reveals a dualistic role of artificial intelligence in language pedagogy. While AI-powered tools such as Google Translate and Microsoft Translator significantly enhance lexical accessibility and reduce language anxiety among learners, their capacity to convey cultural nuance remains critically limited. Findings indicate that algorithmic biases, rooted in Anglo-American linguistic corpora, result in the systematic flattening of cultural meaning and misrepresentation of idioms, humor, politeness strategies, and socio-pragmatic cues, thereby reinforcing a form of digital linguistic imperialism. Students’ uncritical reliance on AI outputs has fostered a “copy-paste culture,” undermining opportunities for intercultural reflection and eroding pragmatic awareness. However, the study also identifies transformative potential when AI tools are pedagogically repurposed through “teaching against the machine” strategies. A subset of instructors successfully used AI-generated mistranslations as pedagogical moments to foster critical cultural awareness, metacognitive reflection, and intercultural dialogue. Despite this potential, widespread implementation is hindered by systemic barriers, including the absence of institutional policies, lack of professional development, curricular gaps, and infrastructural challenges. The research concludes that AI technologies are not inherently facilitative or obstructive to cultural understanding; rather, their effectiveness is contingent upon intentional pedagogical mediation, critical digital literacy, and context-sensitive integration. The study calls for curriculum reforms, ethical AI integration frameworks, and teacher training programs that

position AI as a scaffold, not a substitute for intercultural communicative competence in ELT contexts

KEYWORDS: AI-driven translation, cultural understanding,, English language teaching, higher education, Libya, intercultural competence, technology integration

driven translation technologies in mediating his qualitative case study investigates the effectiveness of AI

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Euphorbia glyptosperma Engelm is recorded for the first time as part of the flora of Libya. This widespread wild weed was collected during 2023–2024 from various locations, including regions in Sabha and Ariggiba (approximately 110 km southwest of Sabha city). To aid in its identification and facilitate future studies, a comprehensive description of the species, habitat information, a distribution map, and distribution data are provided. Additionally, a brief discussion highlights the most significant threats associated with this species. Keywords: Identification key, Ariggiba Region, Field work, Sabha taxonomy, Alena