An algorithm is proposed to recover out-of-service loads (OOSLs) in smart distribution
systems (SDSs) after exposure to cyber-attacks (CAs) resulting in interruptions of in-service
loads (INSLs). The proposed algorithm is implemented in three steps. The first step is
based on building the SDS in matrix form to be data input to the proposed algorithm. The
second step is concerned with classifying the SDS into three zones: the attacked zone, the
primary neighbor zone, and the secondary neighbor zone. The third step is performing five
maneuvering processes (MPs) to recover the OOSL without breaking the electric limitations
(ELs). The ELs are related to the maximum branch current, the node voltage, the load
priority, the radiality maintenance of the SDS, the minimum system total power loss, the
instruction sequence of the automatic-communication-switches (ACS), and the minimum
number of ACSs. The proposed algorithm was tested under a 70-bus SDS with four electric
supply feeders. The proposed algorithm achieved supply recovery for all OOSLs with
efficiency of 100% after the occurrence of a CA on a single or double ACS without breaking
the ELs. The proposed algorithm succeeded in achieving supply recovery for 97.6%, 97.1%,
and 96.4% of the OOSLs after the simultaneous occurrence of a CA on three, four, and five
ACSs, respectively, without breaking the ELs. The advantages of the proposed algorithm
are a lack of dependency on the system size, a short electric supply recovery time within
the range of 190–199 ms, a lack of dependency on distributed generation (DG), and the
achievement of self-healing in the SDS following a single and two simultaneous CAs, as well
as almost achieving self-healing under exposure to three, four, and five simultaneous CAs.

In this work, the impact of changing the flow rate of oxygen from 1 to 5 sccm in microstructure and optoelectronic properties of vanadium oxide (V_xO_y) thin films deposited by the reactive magnetron sputtering on glass substrates has been investigated. The films were analyzed for crystallinity using XRD, surface morphology using SEM, and the optoelectronic characteristics of the films using UV–Vis spectroscopy. The XRD spectra of the films exhibited good crystallinity and preferred orientation. In addition, structural parameters such as crystal size, stress, number of unit volumes of crystals, and displacement density were evaluated. Analysis of the XRD data indicated a phase transition from the monoclinic VO₂ phase to the orthorhombic V₂O₅ structure with increasing oxygen flow. A higher transmittance of the films was observed from the optical measurements which moved parallel to the increase in the oxygen flow …

This study conducted a rigorous evaluation of Environmental Mapping and Analysis Program (EnMap) data in geological applications, specifically focusing on lithological and hydrothermal alteration mapping. This research represents one of the earliest attempts to apply EnMap data for such purposes, and the first to integrate EnMap and airborne geophysical data for geological mapping over the entire Arabian Nubian Shield. To ensure a comprehensive appraisal, we selected a study area characterized by complex Precambrian rocks, including igneous, metamorphic, and sedimentary formations, alongside structural intricacies and hydrothermal activities. Our study utilized various image-processing techniques, including principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), Sequential Maximum Angle Convex Cone (SMACC) endmember analysis, and spectral resampling. These techniques successfully discriminated ophiolitic serpentinite, volcaniclastic metasediments (as part of the ophiolitic mélange matrix), metavolcanics, metagabbro-diorite, syn-orogenic granite, post-orogenic granite, Nubian sandstone, and Wadi deposits. Additionally, they revealed the prevalence of OH-bearing minerals and iron oxides as the primary hydrothermal alteration products within the study area. By correlating the findings with USGS spectral libraries and airborne geophysical data, we determined the efficacy of EnMap data in these applications. Our findings were further validated through multiscale observations, field investigations, petrographic analyses, and scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDX). In addition to endorsing the use of the UMAP algorithm and EnMap data for future applications, this study highlights key alteration zones that could serve as potential targets for future gold exploration, alongside insights into bauxite ore occurrences.

Groundwater serves as a vital resource for sustainable water supply, particularly in semi-arid regions where surface water availability is limited. This study explores groundwater potential zones in the East Desert, Qift–Qena, Egypt, using a multidisciplinary approach that integrates remote sensing (RS), geographic information systems (GIS), geostatistics, and field validation with water wells to develop a comprehensive groundwater potential mapping framework. Sentinel-2 imagery, ALOS PALSAR DEM, and SMAP datasets were utilized to derive critical thematic layers, including land use/land cover, vegetation indices, soil moisture, drainage density, slope, and elevation. The results of the groundwater potentiality map of the study area from RS reveal four distinct zones: low, moderate, high, and very high. The analysis indicates a notable spatial variability in groundwater potential, with “high” (34.1%) and “low” (33.8%) potential zones dominating the landscape, while “very high” potential areas (4.8%) are relatively scarce. The limited extent of “very high” potential zones, predominantly concentrated along the Nile River valley, underscores the river’s critical role as the primary source of groundwater recharge. Moderate potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and they account for 27.3%. These layers were combined with geostatistical analysis of data from 310 groundwater wells, which provided information on static water level (SWL) and total dissolved solids (TDS). GIS was employed to assign weights to the thematic layers based on their influence on groundwater recharge and facilitated the spatial integration and visualization of the results. Geostatistical interpolation methods ensured the reliable mapping of subsurface parameters. The assessment utilizing pre-existing well data revealed a significant concordance between the delineated potential zones and the actual availability of groundwater resources. The findings of this study could significantly improve groundwater management in semi-arid/arid zones, offering a strategic response to water scarcity challenges

The environmental difficulties from mining tailings arise mainly from legacy dump sites because these residues spread pollution through surrounding areas. Effective environmental management requires a comprehensive pre-assessment. An ERI, electrical resistivity imaging, system serves as the analytical tool to create models for leachate assessment prior to its measurement in abandoned mining tailing storage sites. A total of 16 2D ERI profiles produced both 2D and 3D models that monitored the El Mochito mine waste site in Honduras. Different geoelectric zones were identified in the electrical resistivity models of this site with high resistivity values ranging between 60 and 100 Ω m in the surface layer while the middle layer exhibited moderate resistivity between 30 and 60 Ω m and the lowest resistivity of 1–30 Ω m was observed in the active leaching zone that contained conductive materials and mineral-rich leachate. The 3D hydrogeological models provided clear visibility of leachate areas and flow paths. The leachate migration showed uniform movement towards the northern direction until it reached the southern region where concentrations decreased. Another level of spatial understanding and depth information on resistivity distribution was obtained from 3D ERI models. The complete assessment objectives of the research form the basis for future investigations while demonstrating the importance of integrating geochemical measurements. The study emphasizes the need for ERI to examine complicated mining tailings yet requests deeper scientific investigation to create effective environmental management techniques and remediation practices.

Link criticality analysis (LCA) in transportation networks plays a pivotal role in assessing the systemic impact of link failures on overall traffic performance. Traditional LCA approaches often rely on exhaustive link-removal simulations or graph-theoretic metrics, which become computationally prohibitive and behaviorally simplistic when addressing multiple link failures. This study proposes a novel, scalable framework that integrates stochastic user equilibrium traffic assignment, deep-learning-based flow estimation using stacked autoencoders (SAEs), and multi-method global sensitivity analysis (GSA) to evaluate network-wide link importance. The framework generates synthetic demand scenarios using Monte Carlo simulations, applies a stochastic assignment model to estimate flow distributions, and trains an SAE model to predict average user delay. The trained model then enables efficient GSA to quantify the influence of each link. The methodology is applied to a real-world case study in Egypt’s New Capital. The proposed framework demonstrates high predictive accuracy (mean standard error = 0.66, R² = 0.98) and computational efficiency, making it suitable for large-scale, data-sparse, or developing urban contexts. GSA results reveal critical links with both direct and nonlinear effects on delay, guiding planners toward strategic investments and resiliency planning. This integrated approach advances LCA by offering interpretable, scalable, and data-driven insights into transportation network vulnerabilities.