Sudan University of Science and Technology (abbreviated SUST) is one of the largest public universities in Sudan, with ten campuses in Khartoum state. The main campus is located in the so-called Al Mugran area of Khartoum, the confluence of the White Nile and the Blue Nile.
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.
This review highlights the application of first principles in studying hydrogen interactions within HEAs. It explores key aspects, including electronic, mechanical stability, thermodynamics, and diffusion pathways. Most research uses SQS to simulate chemical disorder, which is paired with GGA-PBE exchange-correlation functionals and PAW pseudopotentials to ensure accurate energetic and structural predictions. Electronically, DFT descriptors such as DOS/PDOS, COHP/ICOHP, bond order, and Mulliken charges have demonstrated that hydrogen stabilization is controlled by localized metal-hydrogen bonding, charge transfer, and element-specific d-1s hybridization, all of which influence interstitial site preference and hydride stability. Mechanically, DFT-derived elastic moduli (B, G, E), Pugh's ratio, and dislocation energy factors show that hydrogen can strengthen or soften HEAs, depending on concentration, phase, and local lattice distortion, influence powder hydride decrepitation and cycle durability. Thermodynamically, hydrogen binding energy emerges as the critical descriptor, requiring an ideal intermediate binding strength for reversible absorption and desorption. DFT exhibits heterogeneous hydrogen transport regulated by competing lattice distortion (trapping) and lattice expansion (enhanced pathways), resulting in diffusion composition and phase dependence. DFT's strengths include unmatched atomistic insight, but it is limited by exchange-correlation approximations, neglect of short-range order, incomplete entropy/zero-point effects, static 0 K models, and high computing cost. Future advancement will be made by combining DFT and machine learning for high-throughput screening in rational HEA design.
The accurate and efficient prediction of crack propagation in dielectric materials is a critical challenge in structural health monitoring and the design of smart systems. This work presents a hybrid modeling framework that combines an electromechanical phase-field fracture model with deep learning-based surrogate modeling to predict fracture evolution in dielectric nanocomposite plates. The underlying finite element simulations capture the coupling between mechanical deformation and electrical field perturbations caused by cracks, using a variational phase-field formulation. High-fidelity simulation outputs-namely, phase-field damage variables and electric potential fields-are utilized to train convolutional neural networks (CNNs) with ResNet-U-Net architectures. Crucially, the framework is designed to predict the final crack path directly from the geometric configuration (random defect patterns). We systematically compare the effectiveness of using either phase-field variables or electric potential fields as the primary physical signatures to guide the training process. The results reveal that models informed by electric potential fields offer superior segmentation accuracy, faster convergence, and enhanced generalization, owing to the smoother gradient distribution and global spatial coverage of the electrical response. Ultimately, the trained surrogate model enables the instantaneous, geometry-driven prediction of crack paths, bypassing the need for computationally intensive field calculations during inference. This demonstrates that leveraging electrical physics as a "training guide" significantly improves the reliability of real-time fracture assessment in smart materials.
Self-sensing conductive composites can reveal deformation and damage through measurable changes in electrical resistance, which makes them attractive for embedded diagnostics and learning-enabled structural health monitoring. This paper presents a physically consistent multiphysics Deep Energy Method (DEM) for brittle fracture in piezoresistive materials. The mechanical part is modeled by small-strain linear elasticity coupled to a fourth-order AT2-type phase-field fracture functional with tensile/compressive energy split and history-field irreversibility. To avoid artificial energetic mixing of mechanical and electrical quantities, the electrical problem is treated as a one-way coupled sensing subproblem: after solving the mechanics–fracture problem, the electric potential is obtained from a steady conduction problem whose conductivity depends on strain through a linearized piezoresistive law and on damage through a crack-induced conductivity degradation. The resulting formulation predicts crack evolution together with its resistance signature without assigning the electrical field an artificial crack-driving role. DEM is used to minimize the variational subproblems over admissible neural trial spaces with exact imposition of essential boundary conditions. A lean verification suite is used to validate the electrical building blocks and the fracture engine separately, followed by a numerical study of a tensile plate with stress concentrators and electrodes. In that study, the framework captures a nontrivial sensing regime in which appreciable damage growth leaves the global resistance nearly unchanged, followed by a sharp resistance increase once dominant conductive ligaments are disrupted and current paths reorganize strongly.
Ready-to-use therapeutic foods (RUTFs) have become a pivotal intervention for community-based management of malnutrition due to their nutrient density and long shelf life. However, reliance on imported ingredients such as peanut butter, limits sustainability and accessibility of RUTFs in many regions. This study assessed the nutritional composition and shelf stability of RUTFs developed from indigenous sources (soybean and quinoa). The three RUTFs formulations were evaluated for peroxide value (PV), acid value (AV), thiobarbituric acid (TBA), water activity (aw), color change (ΔE), total plate count (TPC), total mold count (TMC), and sensory acceptability over storage period of 90 days, using two-way ANOVA. The RUTF formulations showed enhanced protein, fat and energy densities with minimal increases in PV (1.03 to 1.69 meq O2/kg), TBA (0.02 to 0.08 mg MDA/kg), and with decreased aw (p < 0.05). TPC and TMC remained within WHO limits and ΔE ranged from 2.5 to 3.1, confirming the nutritional efficacy, safety, and acceptability of RUTF in treating malnutrition. In conclusion, RUTF formulations possessed improved nutritional composition, oxidative stability, microbiological safety, and sensory acceptability throughout the storage period. Future clinical trials are recommended to confirm the therapeutic efficacy and compliance of these locally produced RUTF formulations in malnourished populations.
