Karary University

Air pollution remains a major public health and environmental challenge in China, driven by complex non-linear interactions among anthropogenic activities, meteorological conditions, and topographic features that go beyond simple linear relationships. This study presents a comprehensive spatio-temporal assessment of key air pollutants (CO, NO2, SO2, and PM2.5) and their relationships with Total Column Ozone (TCO) across China’s provinces from 2019 to 2023. Multi-source high-resolution satellite data from Sentinel-5P/TROPOMI, the China High PM2.5 dataset, MODIS, and ERA5-Land reanalysis were integrated. A tiered analytical framework was applied, combining linear Pearson correlations, non-linear Spearman rank correlations, and interpretable XGBoost machine learning with SHAP values. Results reveal a distinct seasonal “seesaw” pattern, with primary pollutants peaking during winter stagnation and TCO reaching maximum levels in late winter and spring. Non-linear analyses uncover critical threshold effects, including exponential increases in PM2.5 and SO2 when surface temperatures drop below 0 °C, very strong SO2-TCO coupling (ρ = 0.93), and significant pollutant trapping in low-elevation regions (CO-elevation ρ = −0.82). These findings support the development of precision environmental policies with dynamic, temperature-threshold-based emission controls and topography-specific strategies to effectively mitigate air pollution in China.

The investigation examined the protective potential of aqueous extract of Morinda lucida leaf (ML) on aluminium chloride (AlCl3)-mediated testicular damage in rats (male) and determined if its secondary metabolites could modulate proteins linked with erectile dysfunction. Rats in the treatment groups were administered 30mg/kg bw AlCl3, with doses of 125mg/kg bw and 250mg/kg bw of the plant extract. Phytocompounds identified in the plant were also screened for potential phosphodiesterase-5 and arginase inhibitor molecules. After 21 days of treatment, sperm count in the animal group treated with only AlCl3 decreased significantly, though with no effect on sperm motility. Administration of ML improved sperm morphology in the extract-treated group compared to the untreated group. Moreover, the leaf extract reversed the elevated myeloperoxidase activity, IL-6, TNF-α, and nitrite concentration observed in the untreated group to near control levels in the plant treatment groups. Molecular docking analysis showed that stigmasterol (-10.2kcal/mol) and oruwacin (-8.2kcal/mol) had the best respective binding energies against the phosphodiesterase-5 and arginase proteins. The 300ns dynamics simulation further revealed the compounds' good stability at the enzymes' active sites. Further experiments will be required to determine the potential alternative therapeutic interventions of the plant in preserving male fertility.

This study numerically analyzes the thermal-fluid performance of supercritical hydrogen in regenerative cooling channels with aspect ratios (AR) ranging from 1 to 8 for rocket engine combustion chambers. The study investigates the effects of channel geometry and inlet Reynolds number on heat transmission efficiency, flow behavior, and pressure drop. The SST k-ω turbulence model was validated and utilized in ANSYS FLUENT (2024 R1, (Ansys Inc., Canonsburg, PA, USA) CFD simulations to examine temperature distributions, turbulent kinetic energy, and velocity profiles. The results show that convective heat transfer is improved with higher Reynolds numbers, while pressure drops are increased; the best range for balanced performance is found to be between 35,000 and 45,000. The aspect ratio significantly influences thermal performance; increasing it from 1 to 8 reduces peak wall temperatures by 12–15% but exacerbates thermal stratification and pressure losses. An intermediate aspect ratio (AR = 2–4) was found to optimize both heat transfer enhancement and hydraulic performance. The study provides critical insights for optimizing cooling channel designs in high-performance rocket engines, addressing the trade-offs between thermal efficiency and flow dynamics under extreme operating conditions.

• Evaluates the thermodynamic performance of a hydrogen-enriched regenerative gas turbine. • Analyzes emissions across varying equivalence ratios and compression ratios. • Optimal conditions: Best at ∅=0.5, rₚ=25, 30% hydrogen with acceptable temperatures. • Demonstrates significant efficiency improvements in regenerative cycles. • Emissions impact: CO emissions rise over 100x at ∅=0.6, needing control. Gas turbine power plants are essential to global energy production. This study presents a comprehensive analysis of the effects of hydrogen enrichment and regenerative cycles on the performance and emissions of gas turbines. A comparison is made between the regenerative and simple cycles under varying equivalence ratios, pressure ratios, and hydrogen content levels. The analysis focuses on key parameters, including work output, fuel consumption, turbine inlet temperature (TIT), and emissions (CO, CO₂, NO, H₂O). The findings show that the regenerative cycle offers significant performance improvements, with work output increasing by 31.6% over the simple cycle. Hydrogen enrichment further boosts the regenerative cycle’s efficiency, reducing fuel consumption by up to 13.9% at 50% hydrogen. However, the regenerative cycle results in higher emissions, particularly CO and NO, with higher equivalence ratios intensifying this effect. Emissions can be reduced through optimization of the compression ratio, balancing efficiency gains with emission control. This study highlights the trade-offs involved in optimizing hydrogen-enriched regenerative gas turbines

Abstract Background Physiological changes during pregnancy significantly alter drug pharmacokinetics, yet research remains limited due to ethical and methodological challenges. A consolidated overview of the most influential studies is lacking. Objective This study aimed to perform a comprehensive bibliometric and thematic analysis of global research on pharmacokinetic changes during pregnancy from 1971 to 2025, identifying trends, influential contributors, emerging themes, and knowledge gaps. Methods A bibliometric analysis was conducted on October 8th, 2025 using data extracted from the Scopus database for publications between 1971 and 2025. Performance indicators were analyzed using descriptive statistics and correlation analyses. Bibliometrix (R), VOS viewer, and GraphPad Prism were used for bibliometric mapping, co-authorship networks, and thematic clustering. Results A total of 1,391 publications by 8,192 authors across 511 journals were identified, showing an annual growth rate of 6.18% and 18.89% international collaboration. The United States (13%), Italy (5%), and New Zealand (4%) were leading contributors. Clinical Pharmacokinetics was the top journal, while the "University of Washington, Seattle, united states" and "Wolters Kluwer health - Adis, Auckland, New Zealand”, were the most influential institutions. Core keywords included “pregnancy,” “pharmacokinetics,” “HIV,” “epilepsy,” and “therapeutic drug monitoring,” with emerging themes such as “metabolism” and “controlled study.” Thematic evolution revealed a shift from methodological foundations (1971–1988) to clinical applications (1989–2015) and mechanistic approaches (2016–2025). Conclusions Global research on pharmacokinetic changes during pregnancy has grown steadily, transitioning toward model-informed and mechanistic studies. However, the absence of mature motor themes indicates thematic fragmentation. Strengthening international collaboration, advancing physiologically based pharmacokinetic modeling, and expanding studies across diverse populations and drug classes remain priorities to optimize pharmacotherapy in pregnancy.

Breast Cancer is a rigorous cancerous disease affecting the majority of females and it is triggered through abnormalities in the growth of breast tissues. Doctors suggest early treatment and diagnosis for cancer, as it gives a pathway for several treatment opportunities and improved lifestyle. Manual clinical practices in detecting the severity of the disease can lead to many errors, and also the process is time-consuming. Early symptoms associated with breast cancer cannot lead to high diagnostic accuracy, as in the early stages, the symptoms are limited and similar for different kinds of breast cancer. Most late-stage breast cancer may not show symptoms at all, which require advanced clinical practices like mammography, radiography, ultrasounds, and so on. Recently, machine learning tools have been used with intelligent techniques for aiding medical diagnosis. Automated breast cancer detection can lead to high diagnostic accuracy, and also offer timely diagnosis. However, it is necessary to improve the potential of machine learners to effectively learn the complex features of mammograms, and increase the detection accuracy. A new breast cancer detection model is developed using deep learning techniques to improve the breast cancer diagnosis process by efficiently analyzing the mammogram images. The process begins by collecting mammogram images from benchmark sources. These images are processed using the developed Residual Densenet with Region Attention (RD-RA) model. The designed model is useful to enhance feature learning with the help of dense connections and residual learning. The use of a region attention mechanism is beneficial for focusing on the relevant areas of the mammogram for detecting abnormalities. Finally, the robustness of the model is evaluated by comparing its performance against other existing breast cancer detection methods to demonstrate its potential.