• Several types of steroids are a class of medications used worldwide to treat pain. Despite significant differences in their use across various age groups, particularly among young people, including university students, steroids offer a significant health benefit when used without a prescription and with guidance from a pharmacist. 425 new Libyan students from various colleges in Gharyan University (Medicine, Pharmacy, Science, Arts, etc.). The data were collected using a structured self-designed questionnaire consisting of 32 items. Descriptive (frequencies and percentages) and inferential statistics (Cronbach's alpha coefficient) were applied to assess internal consistency, and the Chi-squared test and ANOVA. The analysis revealed that 90.4% of students do not use non-steroidal anti-inflammatory drugs for pain, 94.1% do not use them for headaches, and 94.6% do not use them without a prescription. 6.1% reported experiencing stomach pain after use, and 1.6% indicated that they needed a larger dose to achieve the same effect. The Cronbach's alpha coefficient was 0.81, indicating good internal consistency. This study found significant differences in eight question items (29.6%) between genders and in three items between age groups. Thus, students at Gharyan University have a high level of awareness regarding the safe use of non-steroidal anti-inflammatory drugs and avoiding their use without a prescription.

• English plays an important role in university study, academic communication, access to information, technology, and future employment. For many first-year students, however, the transition from school English to the language demands of university study can be challenging. This volume develops the core grammatical foundations required for first-year university English. The sequence begins with basic sentence structure and the verb to be, then develops through the present simple, questions and negatives, there is/there are, countable and uncountable nouns, and the past simple in affirmative, negative, and question forms. Each lecture is organised around explicit learning outcomes and combines explanation, examples, guided practice, vocabulary, reading, speaking, writing, review, and homework. The aim is not only to teach grammatical forms but also to help students use those forms to communicate about their studies, routines, experiences, plans, and academic life. Teachers may use the book as a core classroom text or adapt individual lectures to suit student level, contact hours, and local curriculum requirements. Students are encouraged to complete the exercises actively, speak and write whenever possible, review recurring errors, and build confidence through regular practice. 

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This study reports on a collection of spiders from Misallatah Nature Reserve and National Park (MNRNP), north western Libya, including five species recorded for the first time in the country. 75 spider samples (13 adult males, 20 adult females, 42 juveniles), comprising 12 families (Agelenidae; Araneidae; Dysderidae; Gnaphosidae; Lycosidae; Palpimanidae; Pisauridae; Philodromidae; Pholcidae; Salticidae; Thomisidae; Zodariidae), 13 genera (Lycosoides Lucas, 1846; Argiope Audouin, 1826; Dysdera Latreille, 1804; Nomisia Dalmas, 1921; Hogna Simon, 1885; Palpimanus Dufour, 1820; Holocnemus Simon, 1873; Aelurillus Simon, 1885; Menemerus Simon, 1868; Plexippus C. L. Koch, 1846; Phlegra Simon, 1876; Bassaniodes Pocock, 1903; Zodarion Walckenaer, 1826) and 13 species (Argiope lobata Pallas, 1772; Dysdera crocata C. L. Koch, 1838; Nomisia recepta Pavesi, 1880; Palpimanus gibbulus Dufour, 1820; Holocnemus pluchei Scopoli, 1763; Plexippus paykulli Audouin, 1826; Bassaniodes lalandei Audouin, 1826; Zodarion pileolonotatum Denis, 1935; Lycosoides flavomaculata Lucas, 1846; Hogna effera O. Pickard-Cambridge, 1872; Aelurillus blandus Simon, 1871; Menemerus taeniatus L. Koch, 1867; Phlegra yaelae Prószyński, 1998) were collected; the last five species are first records in Libya.

This study reports on a collection of spiders from Misallatah Nature Reserve and National Park (MNRNP), north western Libya, including five species recorded for the first time in the country. 75 spider samples (13 adult males, 20 adult females, 42 juveniles), comprising 12 families (Agelenidae; Araneidae; Dysderidae; Gnaphosidae; Lycosidae; Palpimanidae; Pisauridae; Philodromidae; Pholcidae; Salticidae; Thomisidae; Zodariidae), 13 genera (Lycosoides Lucas, 1846; Argiope Audouin, 1826; Dysdera Latreille, 1804; Nomisia Dalmas, 1921; Hogna Simon, 1885; Palpimanus Dufour, 1820; Holocnemus Simon, 1873; Aelurillus Simon, 1885; Menemerus Simon, 1868; Plexippus C. L. Koch, 1846; Phlegra Simon, 1876; Bassaniodes Pocock, 1903; Zodarion Walckenaer, 1826) and 13 species (Argiope lobata Pallas, 1772; Dysdera crocata C. L. Koch, 1838; Nomisia recepta Pavesi, 1880; Palpimanus gibbulus Dufour, 1820; Holocnemus pluchei Scopoli, 1763; Plexippus paykulli Audouin, 1826; Bassaniodes lalandei Audouin, 1826; Zodarion pileolonotatum Denis, 1935; Lycosoides flavomaculata Lucas, 1846; Hogna effera O. Pickard-Cambridge, 1872; Aelurillus blandus Simon, 1871; Menemerus taeniatus L. Koch, 1867; Phlegra yaelae Prószyński, 1998) were collected; the last five species are first records in Libya.

The volume of Arabic text online keeps growing, and with it the need for systems that sort it into meaningful categories. A quieter problem often passes unnoticed: when long documents are split into shorter segments and pieces of the same document fall on both sides of the boundary between training and testing, the reported scores can look far better than the model deserves. This study treats that document level leakage as its central subject. Using a purpose built multidomain Arabic corpus of 1,000 segments drawn from 183 source documents across seven categories, we first measure how much leakage distorts the numbers. Under a naive random split, an optimized Linear SVM appears to reach 0.9800 accuracy; under a grouped split keyed to the document identifier, the same model reaches 0.8528. The gap of about 12.7 accuracy points is attributable to leakage, and across models the inflation ranges from roughly 11.9 to 30.4 points. Leakage also flattens the ranking, so a weak Naive Bayes looks almost as strong as the best model. Under the honest protocol, an ablation shows that character level TF IDF alone matches the combined word and character representation and slightly exceeds it on Macro F1, 0.7801 against 0.7713, while word features mainly refine precision. McNemar tests and a document clustered bootstrap show that the strongest linear models are statistically indistinguishable. A fastText baseline reaches 0.6497 accuracy, and two pretrained Arabic transformers, AraBERT and MARBERT, perform comparably to the linear model rather than surpassing it. A class level analysis exposes the General category as a structural confound whose removal raises accuracy to 0.9349. Overall, leakage aware evaluation paired with subword rich TF IDF and a Linear SVM forms a strong, honest, and practical baseline for Arabic text classification.

Abstract

This study investigates applying the artificial intelligence tools in translating culture-specific items in literary works. A heated debate has been initiated among scholars since the beginning of using AI tools in translation; the core of this debate was about the ability of AI translation tools to convey cultural nuances compared to human translators. Furthermore, techniques are usually adopted by AI translators in case of a lack of equivalence. To investigate these queries, fourteen excerpts containing cultural elements have been chosen from three Libyan novels by Ibrahim Al-Koni, The Bleeding of the Stones, Dust Gold and Anubis, are utilized as a case study to compare the human translator's work with AI translation tools, namely, ChatGPT and DeepL. The major results of this study are the performance of AI tools almost the same as human translators. Although AI translation tools outperformed human translators' work in some situations, they still require specialized training on culture-specific items related to specific communities. A collaboration between human and AI tools results in significant translation rather than separating or substituting each other. The mixing between machine and human leads to important progression in the field of translation.