Feature reduction is widely used in machine learning based phishing URL detection to lower model complexity while retaining detection performance. However, conclusions about how much reduction is acceptable may depend on how training and testing data are partitioned. This study examines whether conclusions about feature reduction remain stable when evaluation changes from random stratified to domain disjoint cross validation. Experiments were conducted on URL-Phish Version 2 using Mutual Information ranking within each training fold and four predefined feature budgets, from 22 to 5 features. Logistic Regression and Random Forest were evaluated under both protocols, with PR AUC as the primary metric. PR AUC decreased as the feature budget was reduced for both classifiers under both evaluation protocols, so the qualitative conclusion about feature reduction remained consistent within this experimental setting. However, the size of the protocol gap varied across classifiers and feature budgets: it was clearest for Random Forest under the smallest feature budget, whereas the Logistic Regression gaps were less clearly separated from fold-level variability. Domain disjoint evaluation also produced greater fold-level variability. The results show that claims about compact phishing URL representations should be interpreted together with the evaluation protocol used. Future work should examine whetherthis pattern persists across additional datasets, classifiers, and domain grouping rules.

Missing data are common in clinical prediction studies, yet their handling may affect not only predictive performance but also which model is judged best. This study examined the stability of classifier selection when missing data handling was changed under controlled, paired evaluation conditions. Using SUPPORT2 data from 9,105 patients, Logistic Regression, Random Forest, and XGBoost were evaluated with median, K nearest neighbor, and iterative imputation. The analysis retained natural missingness, added nested missing completely at random (MCAR) perturbations of 5%, 10%, 20%, and 30%, and included a separate 20% missing at random (MAR) condition. Repeated stratified five fold cross validation used the same patient partitions, model seeds, and artificial missingness masks across corresponding comparisons. Ranking stability was assessed through condition level winner changes, paired rank reversals, and agreement across receiver operating characteristic area under the curve (ROC AUC), Average Precision, and Brier Score. The condition level winner remained stable under natural and mild additional missingness, but became dependent on imputation at higher missingness. Winner changes occurred in 6 of 12 imputation comparisons and 9 of 15 missingness comparisons, while pairwise rank reversals occurred in about 44% of matched repeat comparisons. All three metrics selected the same winner in 8 of 18 conditions. Because competing winners were separated by small ROC AUC margins, the results indicate sensitivity of model selection rather than large performance advantages. Reporting ranking stability alongside conventional performance estimates may therefore provide a more cautious basis for comparative clinical prediction studies.

This study proposes a new robust blind video watermarking technique for copyright protection that combines the Fourier transform, entropy analysis, Hessenberg decomposition, and lifting wavelet transform (LWT). The host video is divided into frames, the RGB channel of each frame is then isolated, and the 2-level LWT is applied to the R channel. The FFT coefficients are divided into 4x4 non-overlapping blocks after the FFT is applied to the high-frequency sub-band of the 2-level LWT. The watermark is then hidden in the first row and first column element of the upper Hessenberg matrix after each selected block has been subjected to Hessenberg decomposition. The proposed technique is extremely invisible and resilient to attacks, as demonstrated by numerous simulated experiments. Comparisons with similar scheme show that the proposed watermarking scheme outperforms the others in terms of invisibility and robustness

The rapid growth of Internet of Things (IoT) applications has increased the number of connected devices operating in shared wireless environments. IoT systems rely on sensors, actuators, embedded electronics, software, and internet connectivity to collect, exchange, and process data across sectors such as smart homes, healthcare, transportation, industrial systems, energy systems, and public infrastructure. However, the dense deployment of wireless IoT devices can create significant Electromagnetic Compatibility (EMC) challenges, especially when multiple transmitters and receivers operate in proximity or within the same or adjacent frequency bands. These conditions may lead to Electromagnetic Interference (EMI), which can degrade communication performance, reduce device reliability, and affect safety in critical environments. This study uses a regulatory review and conceptual framework development, drawing on literature synthesis, regulatory requirement extraction, analytical classification, regulatory mapping, and risk-based EMC categorisation. It identifies key EMC and EMI challenges in IoT applications, particularly in dense wireless and low-power communication environments. It analyses immunity requirements in critical sectors such as vehicular networks, healthcare, industry, and public infrastructure. It also develops a regulatory framework that defines stakeholder roles and classifies IoT connection methods, application areas, and device structures to support safer deployment and reduce interference risks.

• 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.

abstract

The present study aimed to assess the knowledge of diabetic patients regarding diabetes-related systemic and oral

complications and to evaluate their attitudes toward maintaining good oral health and their oral hygiene practices,

including regular dental visits. A cross-sectional study was conducted at Gharyan Educational Hospital, Gharyan,

Libya, between November 2025 and January 2026. A total of 134 diabetic patients attending scheduled outpatient

clinics were enrolled. The study found that 60.5% of participants were aware that diabetes increases the risk of

oral disease, with a high awareness of xerostomia (91%). However, knowledge of specific complications was lower;

only 46.2% identified dental caries and 42.6% identified oral fungal infections as related risks. Regarding systemic

health, patients showed higher awareness of diabetic foot and eye complications than of nerve or kidney

complications. While 73.9% of participants reported brushing their teeth, only 41.8% did so twice daily.

Furthermore, 44.8% visited the dentist less than once a year, and 41% could not remember their last visit. The

primary sources of information were diabetologists (52.9%) and various media (50%), with 98.5% of participants

expressing a desire for more education on this topic. Diabetic patients in Gharyan demonstrated a moderate level

of knowledge regarding diabetes-related oral health. While attitudes toward oral hygiene are generally positive,

there are significant gaps in knowledge regarding specific oral complications. The findings suggest an urgent need

to integrate oral health education into routine diabetic care and to strengthen collaboration between physicians and

dentists to improve overall patient outcomes.

Keywords. Diabetes mellitus, Oral Health, Knowledge, Attitude, Practice, Libya.

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.