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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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.

Abstract Understanding the distribution of ABO and Rh blood types in a population helps genetic research and studies on disease susceptibility associated with blood types. It also helps blood banks efficiently manage donor blood supplies, providing the availability of compatible blood types for transfusions and emergencies. This study was conducted to analyze the distribution of ABO and Rh-D blood groups in Gharyan city, Libya. The statistics suggest that Blood group O (48.73%) is the most frequent, followed by A (36.36%), B (11.61%), and AB (3.28%). A lesser number of the population is Rh-D negative (20.20%), while the majority is Rh-D positive (79.79%). These results could help blood banks manage blood transfusion programs in handling local blood transfusion services. Keywords. ABO Blood Groups, Rh-d Factor, Blood Group Distribution, Gharyan, Libya.

This study presents a taxonomic note on Fagonia isotricha Murb.

(Zygophyllaceae), a species recorded in Libya and currently treated as a taxonomic

synonym of Zygophyllum mayanum. Global taxonomic databases, such as those

curated by the Royal Botanic Gardens, Kew and World Flora Online, recognize this

taxon as part of the Libyan flora under its currently accepted nomenclature.

This study provides field-based and morphological documentation of populations

observed in the Ariggiba region (southern Libya), and discusses their taxonomic

placement within the Fagonia–Zygophyllum complex. Herbarium specimens were

collected, examined, and deposited, supporting the occurrence of this taxon in the

Libyan flora.