Abstract

Vitamin D deficiency is a widespread public health concern, necessitating a clear understanding of knowledge, at

titudes, and practices (KAP) among populations. Therefore, this study was conducted to assess Libyan citizens'

knowledge, attitude, and practice (KAP) regarding vitamin D in the western region of Libya. This study was carried out

between June and September of 2021. A questionnaire was distributed across the study area and the complete responses

were 314, with 62.7% were females and 37.3% were males. The scores were calculated as percentages and statistically

tested. Additionally, the impact of some socio-demographic factors (age, gender, education level, job, and salary) on

KAP towards vitamin D was investigated. The results revealed that nutritional knowledge of the participants (46.24%)

was much lower than their general knowledge (80.99%), and that the scores of attitude and practice were 74.79% and

60.06%; respectively. Furthermore, socio-demographic factors such as gender, age, education, occupation, and salary

were found to significantly influence practice scores. In conclusion, there's need for targeted public health interventions

and educational campaigns to promote accurate understanding and healthier practices related to vitamin D among

adults in Western Libya.

Keywords: Vitamin D, Knowledge, Attitude, Practice, KAP, Socio-demographic influences, Questionnaire

Cette étude examine l’acceptation de l’intelligence artificielle (IA) par les enseignants de français langue étrangère (FLE) dans les universités libyennes.

L’étude a été menée auprès de 30 enseignants issus de plusieurs universités libyennes. Les données ont été recueillies à l’aide d’un questionnaire en ligne, diffusé par l’intermédiaire des chefs des départements de langue française des universités libyennes. Le questionnaire comprend des informations sociodémographiques sur les participants (âge, diplôme académique, etc.), 27 questions fermées et 3 questions ouvertes.

Les données quantitatives ont été analysées à l’aide de statistiques descriptives, tandis que les réponses aux questions ouvertes ont fait l’objet d’une analyse thématique. Les résultats montrent que 70 % des participants ont une perception favorable et acceptent l’utilisation des outils d’intelligence artificielle dans l’enseignement du FLE. Toutefois, ils expriment des préoccupations concernant la fiabilité des contenus, le plagiat, la dépendance technologique et la protection des données. L’étude souligne la nécessité de mettre en place une formation institutionnelle et un cadre éthique afin de favoriser une intégration responsable de l’intelligence artificielle dans l’enseignement du français langue étrangère.

The present research examines the effect of nuclear energy consumption, financial globalization, nuclear energy technology research and development (R&D) budget on the environmental quality in the context of France. The load capacity factor (LCF) is employed as a novel proxy for environmental sustainability, explaining how human actions affect environmental sustainability and how nature compensates human induced damage. Using the novel approach of Augmented Autoregressive Distributed Lag, the findings of this study show that: (i) the load capacity curve (LCC) hypothesis is valid (ii) Nuclear energy consumption and nuclear energy technology R&D promote the environmental quality by increasing LCF. (iii) The financial globalization (overall, de jure, and de facto) has a positive influence on LCF, suggesting the positive role of financial globalization in boosting environmental sustainability in France. This study presents vital policy implications that can assist policymakers in France in achieving their environmental sustainability objectives.

This study aimed to evaluate the inhibitory and lethal effects of root extracts of the Wild Rue plant (Peganum harmala L.) against the pathogenic fungus Rhizoctonia solani under laboratory conditions. Two types of extracts (aqueous and alcoholic) were used at concentrations of (5%, 10%, 15%, and 20%). The results indicated that the effectiveness increased proportionally with the concentration. The inhibition rate of the aqueous extract reached 64.8% at a concentration of 20%, while the alcoholic extract was significantly superior, reaching 84% at the same concentration. Statistical analysis confirmed highly significant differences between concentrations (P < 0.001) as determined by one-way ANOVA, while two-way ANOVA showed extract type, concentration, and their interaction were significant factors, explaining approximately 85% of the total variance. The study concluded that the alcoholic extract is more effective than the aqueous one in inhibiting R. solani, making it a promising option for fungal control as a safe alternative to chemical pesticides.

Pneumonia remains a major global health burden, where timely recognition on chest X ray images is clinically important yet often challenged by subtle radiographic signs and variability in interpretation. This paper presents a controlled comparative evaluation of four convolutional neural network architectures, MobileNet, ResNet50, VGG, and InceptionV3, for binary classification of chest X ray images into diseased and normal cases. Experiments were conducted using a publicly available Kaggle dataset of 4,479 images under a unified preprocessing and evaluation protocol. Performance was assessed on a held out test set of 300 images, including 200 diseased and 100 normal cases, using accuracy and macro averaged precision, recall, and F1 score, supported by confusion matrix analysis. The results show that MobileNet achieved the highest test accuracy at 95.0 percent, while ResNet50 and VGG achieved 94.7 percent, and InceptionV3 achieved 92.0 percent. Confusion matrix inspection indicates that MobileNet produced the fewest false negatives for diseased cases in this setting, which is important for screening oriented use. Inference time measurements using batch size 1 at 180 × 180 input on CPU further highlight the efficiency advantage of lightweight architectures. Overall, these findings provide a reproducible benchmark to support architecture selection for computer assisted pneumonia screening and clinical triage

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