Introduction: Social distancing and wearing a face mask are highly recommended to mitigate the transmission of coronavirus disease 2019 (COVID-19). However, the success of these strategies relies on individuals’ adherence and public compliance. This study was conducted to assess the level of belief in social distancing and face mask practices in communities in low- and middle-income countries (LMICs) and to identify their possible determinants. Methods: A cross-sectional study was conducted in ten LMICs countries in Asia, Africa, and South America from February to May 2021. A questionnaire was used to assess the belief, practice, and their plausible determinants. Identification of the associated determinants was performed using a logistic regression model. Results: Our data revealed that only 62.6% and 66.9% of the participants had good beliefs in social distancing and good face mask practices, respectively. Residing in the Americas, having a healthcare-related job, knowing people in immediate social environment who are or have been infected and exposure to information of COVID-19 cases on social media or TV were factors significantly associated with good belief in social distancing. Residing country, gender, monthly household income, type of job and exposure to information of COVID-19 cases were significantly associated with face mask wearing practice. Conclusion: The proportion of participants having good beliefs in social distancing and good face mask practices is relatively low (<75%). Hence, sustained health campaigns regarding social distancing benefits and face mask-wearing practices during COVID-19 are critical in LMICs.
Objective: The purpose of this study was to evaluate the efficacy of combination therapy with oral acyclovir and antifungal drugs in addressing persistent dermatophytosis, particularly in patients with viral co-infections and impaired immune systems. Methods: A prospective study was performed on eight patients with persistent fungal infections who were all treated with a combination of antifungal drugs, oral acyclovir, topical steroids, antihistamines, and supportive care. Patient outcomes were monitored over six months. Recovery time and the efficacy of preventive courses were assessed. Results: All patients were fully recovered within six months. Symptoms subsided within 2-4 weeks, and full recovery occurred in 6-8 weeks. Preventive courses helped to avoid relapses. Combining antiviral and antifungal drugs proved valuable, especially in cases of treatment resistance or recurring infections. Conclusion: The study focuses on the potential benefits of combining oral acyclovir with antifungal medications for treating recalcitrant dermatophytosis. This method is a promising strategy for improving outcomes in difficult cases, particularly for patients with viral co-infections. Further research with controlled studies is necessary to confirm these findings.
The African Wild ass (Equus africanus) is considered an endangered species, particularly within its native range in the Horn of Africa and Nubian wild ass historically occupied northeastern Africa, including Sudan. This study presents the findings of a ground population survey carried out during the dry season (14th to 22nd of April 2025) across 23 sites representing various landscapes in the northern and middle areas of the Red Sea state, the coastal zone, coastal plains, and the Red Sea Hills. Our study recorded 652 individuals of which 415 are Nubian wild asses over an area of 184 km² with a population density of 4 individuals per km². The overall male-to-female ratio was found to be 1:2.1 for the Nubian wild ass and 1:2.11 for the African wild ass. These results highlight key demographic trends and spatial distribution patterns critical for targeted conservation efforts.
Machine learning (ML) is increasingly transforming geosciences by reshaping how geological data are generated, interpreted, and integrated across fields from seismology to global Earth system modeling. This review synthesizes the methodological and epistemic changes, arguing that ML constitutes a methodological revolution rather than a paradigm shift, as it supplements rather than supplants the mechanistic foundations of geoscientific explanation. We identify three structural limitations of purely data-driven models. These are: (1) systemic extrapolation failure in non-stationary regimes; (2) limited physics-based explainability, which restricts mechanistic inference; and (3) concerns regarding reproducibility, bias, and sustainability. To address this, we formalize the Data–Model Coevolution Paradigm, a framework advocating for an iterative evolution of ML architectures and physical theory through a feedback loop between representation learning and process-based understanding. By integrating ML with physical reasoning via hybrid modeling, differentiable simulation, and operator learning, this coevolutionary approach offers a roadmap toward trustworthy, physically consistent, and scientifically generative AI in geoscience.
Land use and land cover (LULC) changes strongly influence ecosystem service values (ESV), yet their dynamics in semi-arid biosphere reserves remain underexplored. This study quantifies LULC dynamics and their associated ESV changes in the Dinder Biosphere Reserve (DBR), a unique protected area in Sudan, from 1990 to 2034 using multi-temporal satellite imagery and scenario-based modeling. Findings show that dense tree cover declined by 48.20% (1990-2000), then partially recovered by 2014, but is projected to decrease again by 2034. Grasslands and sparse trees exhibited alternating expansion and contraction. While wetlands and water bodies initially declined, they are expected to expand in the future under a business-as-usual scenario. Total ESV declined by 26.67% (US$ 7.29-5.34 billion). Sensitivity analysis shows that water bodies (CS = 0.53) and dense trees (CS = 0.44) are leading contributors to the total ESV. These results underscore the need for adaptive, spatially targeted management to sustain ecosystem functions in semi-arid biosphere reserves.
