• 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.

Hereditary cancer syndromes (i.e. BRCA1/2-breast cancer) represent a small portion of around 6% of the population worldwide with genetic diseases, but their histopathological diagnosis is still difficult. In this paper, we present a clinical decision support system that combines an improved XGBoost classifier with SHapley Additive Explanations (SHAP) to provide high predictive accuracy and interpretable AI (XAI) in breast cancer diagnosis based on nuclear morphological features. The proposed approach employs a five-stage framework, namely: (1) Data collection from the Wisconsin Breast Cancer Diagnostic (WDBC) dataset, (2) missing value treatment by using multiple imputation by chained equations (MICE), (3) feature normalization, (4) biomarker selection using recursive feature elimination with cross validation (RFECV), and (5) class imbalance addressing by synthetic minority over-sampling technique (SMOTE). Hyperparameter tuning was done with Optuna using the Tree-structured Parzen Estimator (TPE) for 300 trials. The XGBoost model, evaluated on a test data consisting of 114 samples (20% of the total 569 samples), attained the highest accuracy of 96% and recall of 97% for class malignant, with a close to perfection ROC AUC of 0.99. SHAP results demonstrated that morphological characteristics (convex points and concavity, in particular) were the most important features, which is in line with histopathological understanding of nuclear alterations in hereditary cancer syndromes. Local waterfall plots ensured case-specific interpretability by breaking down single predictions into additive feature contributions. Although the WDBC dataset does not contain direct labels of genotype, the designed framework acts as a reproducible proof-of-concept for transparent, high-performance diagnosis of breast cancer with potential for application to hereditary syndromes, and veterinary histopathology incorporating appropriate species specific validation.

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.

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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.

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.