-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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The collection of comprehensive data is crucial for monitoring antimicrobial resistance (AMR) patterns and implementing targeted interventions. However, there is a lack of data regarding the AMR status of pathogens in Libya, hinders the efforts to address the problem effectively. This retrospective study evaluates the prevalence of Escherichia coli AMR isolates to gentamycin, chloramphenicol, cefoxitin (or) ceftriaxone, meropenem, moxifloxacin (Mox), amoxicillin/clavulanate (AUG) or ampicillin-sulbactam (UNZ) among the visitors to the main pathology laboratories in Gharyan city. Reports from 107 AMR tests demonstrate that E. coli was mostly isolated from Urinary Tract Infections (UTIs, 85%) followed by vaginal infections (8.4%) and wound infections (6.5%). More than half of the isolates were multi-drug resistant which is a clear indicator of the need for continuous monitoring. The overall pattern demonstrates a high resistance rate to cephalosporins, and combinations of penicillins with beta-lactamase inhibitors (UNZ or AUG), followed by high resistance rates to Mox. In contrast, E. coli isolates showed high sensitivity to meropenem followed by gentamicin and chloramphenicol. However, the isolation site seems to affect the resistance patterns, most of the wound isolates were resistant to gentamycin. In contrast, all vaginal isolates were resistant to UNZ or AUG, yet both types showed high susceptibility to meropenem. 

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 

Abstract:

This research deals with estimating the likely switch of own car and private vehicles modes

users to public transport modes in the main cities as Tripoli Capital city of Libya due to

reduced travel time of all trips influenced by travel costs through the introducing the public

transport modes such as public buses and increasing cost of car parking. The data on other

factors (variables) that could potentially reason a model switch of from own cars and private

transport to public transport were collected through a survey (questionnaire) with a specified

preference approach. Mode-switch models to describe the switch of behaviour of own car

operators to public transport are developed. A binary logit regression model was used to

assessment the transport displacement model for work and study trips. Mode-switch

likelihood curves have also, been developed to serve as a user-friendly tool to analyse the

likely model-switch for a variety of variables. One way to achieve this objective is by

establishing reserved public transport lanes on main urban Tripoli city roads. The providing of

private road lanes will reduce the level of road traffic congestion and this may effect in a

switch of all or some private transport mode users to public transport modes.

Key words: Own car, Public transport modes, logit model, Travel time, Travel cost, parking

cost and switch probabilities.

Background/Objectives: Clostridioides difficile is a “One Health” pathogen and a cause of antibiotics-associated diarrhea and pseudomembranous colitis. Mobile genetic elements (MGEs) have been documented in the genomes of clinical C. difficile strains; however, the presence of MGEs in environmental strains remains poorly characterized. Thus, the present study was conducted with the objective of identifying the prevalence of MGEs, including mobilizable transposons (MTns), conjugative transposons (CTns), plasmids, and insertion sequences, in whole genome sequences (WGSs) of environmental C. difficile isolates. Methods: The analysis of MGEs was conducted using 166 WGSs obtained from C. difficile strains isolated from various environmental sources contaminated with feces. The MGEs were identified using bioinformatic tools. Results: A total of 48.2% (80/166) of the studied genomes were identified to harbor nine transposons, including Tn916, Tn6194-like, Tn5397, Tn6215, Tn4001, Tn6073, Tn6110, Tn6107, or Tn5801-like. The majority of MTns and CTns could be found within C. difficile sequence types ST11, ST3, and ST35. The results demonstrated close genetic relatedness among the studied genomes, the array of antimicrobial resistance (AMR) genes, such as tetM, ermB, and aac(6′)-aph(2″), and the presence of CTns. Furthermore, the analysis revealed that 24.7% (41/166) of the genome sequences of isolates were associated with various predominant plasmid groups, including pCD6, pCD-ECE4-6, pCD-WTSI1-4, pCDBI1, and pCd1_3, which belonged to 16 different sequence types. Furthermore, several plasmids were identified as harboring the prophage phiCDHM19. Conclusions: The results of the current study suggest that the identified plasmids are abundant and may encode functions that are relevant to C. difficile physiology. The genomes of C. difficile strains examined contain closely related CTns, suggesting that horizontal transfer of AMR is important in this species or other bacterial species. Further research is required to ascertain the effect of these genetic elements and their transferability on the biology of C. difficile.