University of Khartoum

Egypt has recently been certified as malaria-free. To sustain this status, the Egyptian Ministry of Health has implemented a policy of centralised malaria treatment at specialised hospitals, provided free of charge to all populations regardless of migrant status. This study examines migrants' malaria care-seeking experiences, the factors influencing their choices, and strategies to improve access. A qualitative study was conducted in Cairo using semi-structured interviews, inviting adult migrants from malaria-endemic countries and with prior malaria experience. Participants were recruited through purposive and convenience sampling. Data were collected in Arabic, transcribed, translated, and analysed thematically. We recruited 21 individuals, all Sudanese; 90.5% were female. Five participants (24%) developed malaria symptoms immediately upon arrival in Egypt; others developed symptoms within six months. Three themes emerged from the analysis: "Malaria-specific healthcare practices," "Determinants of healthcare-seeking behaviour," and "Recommendations." Although participants recognised the severity of malaria and the need for treatment, 19 (90.5%) initially relied on traditional remedies or self-medication without malaria testing. Factors influencing healthcare-seeking behaviour included, at the patient level, a lack of trust in healthcare professionals, reluctance to disclose a malaria diagnosis due to stigma, fears of quarantine, concerns about adverse drug effects, and exposure to negative rumours and misconceptions about treatment. Factors within the healthcare system encompassed malaria services' affordability, accessibility, and perceived quality. Sudanese migrants' malaria care-seeking behaviours are influenced by multiple personal and systemic factors. Improving access requires targeted health education to address misconceptions, raise awareness, and build trust in the healthcare system.

Sentinel-5P (S5P) plays a central role in global atmospheric and environmental monitoring, yet its coarse spatial resolution limits the analysis of localised emission sources and sharp concentration gradients. Super-resolution (SR) methods have been proposed to address this limitation, but most existing approaches rely on paired low and high-resolution data that are unavailable for S5P, restricting their applicability in real-world settings. In this work, we present a self-supervised hyperspectral SR framework specifically designed for S5P that enables training without high-resolution ground truth. The proposed framework integrates the S5P degradation operator and band-dependent noise characteristics derived from sensor signal-to-noise ratio metadata within a self-supervised learning strategy. Convolutional Neural Network (CNN) architectures tailored to S5P's spectral characteristics based on Depthwise Separable Convolutions (DSC) are introduced to efficiently enhance spatial detail while preserving spectral fidelity. The framework is evaluated across all S5P spectral bands under two settings: (i) reference experiments where supervised and self-supervised learning can be directly compared using synthetic ground truth, and (ii) fully self-supervised settings where high-resolution reference data are unavailable, and assessment relies on physics-based consistency metrics. Results show that the proposed self-supervised models achieve performance comparable to supervised counterparts and produce sharper spatial structures than standard bicubic interpolation. Additional validation using coincident EMIT hyperspectral observations demonstrates that the super-resolved outputs exhibit physically meaningful spatial enhancement, particularly along coastline regions. These findings highlight the potential of the proposed self-supervised framework to improve the effective spatial resolution of atmospheric satellite observations, enabling practical deployment in scenarios where high-resolution reference data are inherently unavailable.

Coastal cities in fragile and conflict-affected states face unprecedented challenges in maintaining infrastructure and protecting ecosystems. In Sudan, Port Sudan has recently emerged as the temporary administrative capital, experiencing rapid urban pressure alongside heightened climate vulnerability. This research evaluates the integration of Nature-based Coastal Solutions (NBCS), such as coral reef and mangrove preservation, into the city’s urban recovery framework. Utilizing GIS and satellite-based geoscience monitoring, the study assesses the current state of coastal assets and their protective capacity. A major barrier to implementing these solutions is the financing gap and high perceived risk in fragile economies. This study explores innovative financial frameworks, specifically the role of Development Finance Institutions (DFIs) in providing 'patient capital' and de-risking investments for sustainable coastal infrastructure. By combining interdisciplinary financial modeling with environmental assessment, the research proposes a strategic roadmap for financing resilient coastal protection. The findings demonstrate that NBCS can significantly reduce infrastructure restoration costs while serving as a vital catalyst for long-term economic stability and post-conflict recovery.Final results, including a detailed comparative cost-benefit analysis and quantified financial projections, will be presented at the conference. This will provide a rigorous evidence-based framework for integrating Nature-based Solutions into Port Sudan’s post-conflict urban recovery.

The East African Rift System (EARS) is an active continental rift that experiences frequent earthquakes, yet seismic hazard assessment across the region remains difficult. Challenges stem from sparse monitoring networks, the absence of standardized guidelines, and numerous active faults that remain unmapped or poorly characterized. In addition, regional earthquake catalogs are incomplete and often depend on limited data, introducing considerable uncertainty into seismic hazard estimates. Despite these issues, conventional least-squares regression methods are still commonly used for magnitude conversion, even though they are sensitive to outliers, rely on assumptions that are made but rarely validated, and possess several limitations. These limitations constrain the generation of reliable homogenized earthquake catalogs essential for seismicity, seismotectonic, and hazard assessments.This study proposes a robust statistical framework for deriving regional magnitude conversion relationships using the Restricted Maximum Likelihood (REML) estimation method. REML is particularly advantageous for data-scarce regions such as EARS, as it explicitly accounts for measurement uncertainties, non-constant variance, and the prevalence of outliers common in mixed-magnitude earthquake catalogs. The methodology incorporates rigorous statistical tests, including Box–Cox transformations for date normality, residual diagnostics, and variance stability evaluations.To demonstrate its usefulness, the proposed framework is applied to catalogs from three regions along the EARS: (1) the Main Ethiopian Rift (Eastern Branch), (2) Sudan (a tectonically stable region), and (3) Malawi (Western Branch). The resulting magnitude conversion relationships exhibit significantly reduced uncertainty and provide confidence bounds, thereby enhancing the reliability of homogenized earthquake catalogs. The proposed approach strengthens the consistency of earthquake datasets across East Africa and offers a valuable tool for improving seismic hazard and risk assessments in similar data- limited regions worldwide.

The gross chemical composition from four populations of Clarias gariepinus, obtained from different geographical sites in the Sudan was determined.The proximate analysis of the specimens of the four populations showed significant differences (p<0.05) in protein, moisture and ash contents but not in fat contents (p>0.05).Khor Abu Gassaba and Tordat Al Rahd populations had the highest calorific contents (185.81 and 178.64kcal/100g) respectively.Analysis of variance of the chemical composition from the four sites revealed highly significant difference (p<0.01) in protein, moisture and fat, and insignificant difference (p>0.05) in ash.The calculated caloric values were high in all populations, but the fat: protein ratio was low which is indicative of high digestibility.The correlation coefficient between body weight and chemical composition percentage of C. gariepinus, from the four locations ranged from weak to high, and from positive to negative correlations.It is concluded that C. gariepinus contain adequate quantity of animal protein, fat and ash that would support and promote human health.