The rising integration of renewable distributed generators (RDGs) and electric vehicle charging stations (EVCSs) in modern distribution networks introduces significant technical, economic, and environmental challenges, particularly under faulted operating conditions. The converter-interfaced characteristics of these sources often reduce system fault current contribution and inertia, leading to degraded voltage and frequency stability. This study proposes an enhanced resilience framework for a faulted Indian 28-bus radial distribution system (RDS) integrated with RDGs and EVCSs through the optimal allocation of Distributed Flexible AC Transmission System (DFACTS) devices ‒ Distribution Static Synchronous Compensator (DSTATCOM), Unified Power Quality Conditioner (UPQC), Distributed Static Series Synchronous Compensator (DSSSC), Distributed Static VAR Compensator (DSVC), and Distributed Thyristor Controlled Series Capacitor (DTCSC). A bio-inspired Black Widow Optimisation (BWO) algorithm is utilised and evaluated against Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Marine Predators Algorithm (MPA) for multi-objective optimisation. The combined objective is to get the best environmental performance while minimising technical and economic impacts. BWO consistently beats the other algorithms, with the lowest average total objective (0.487), maximum (0.505), and minimum (0.485) values. It also has a standard deviation of 0.012, an average convergence of 120 iterations, and a CPU time of 8.5 s. Compared to GA, these changes mean that optimisation works up to 16.2% better and calculations are up to 22% faster. The simulation results confirm the concept that the proposed BWO-based model performs well in improving voltage stability, reducing active power loss, improving frequency response, and reducing CO 2 emissions. This suggests that it is robust and can be used to improve supply systems that are rich in renewable resources and prone to breakdowns.
Explainable artificial intelligence (XAI) is an emergent requirement for the responsible use of AI applications in sensitive application domains due to technological advancements, regulatory pressures and ethical obligations, and alignment with the United Nations Sustainable Development Goals (SDGs). In this paper, we provide a survey of literature from 2023 to 2025, synthesizing the latest advances in approaches, performance metrics, applications and policies, with explicit emphasis on how XAI advances SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), SDG 10 (Reduced Inequalities), and SDG 16 (Peace, Justice and Strong Institutions). We propose an enhanced classification framework that distinguishes between intrinsic versus post-hoc approaches, local versus global explainability, and emerging paradigms (multimodal, LLM-oriented, and interactive explanations) that extend classical taxonomic dimensions as orthogonal descriptors. We also present technical versus human-centred metrics, emphasizing fidelity, robustness, complexity, impact on decisions and evaluability, as well as shedding light on human-centric aspects. The survey investigates key challenges in scalability, security, privacy, ethical trade-offs, and their domain-specific applications in healthcare, cybersecurity, finance, and law. To bridge the research-practice gap, we present a practical “XAI-by-design” engineering template. Although XAI techniques are now mature, challenges related to reproducible research, interactive explanation, and dynamic regulatory adaptation remain significant obstacles. We conclude with concrete research advice to explore future research opportunities.
Traditional landslide susceptibility assessment methods, such as the Slope Stability Susceptibility Evaluation Parameter (SSEP), are often constrained by subjective judgments. This study aims to develop and validate a novel integrated approach that combines the SSEP framework with Geographic Information System (GIS)-based bi-variate statistical analysis to enhance objectivity and reliability in landslide susceptibility mapping. The study was conducted in the landslide-prone Shoko district of Southwestern Ethiopia. Ten causative factors—slope angle, relative relief, land use/land cover, rainfall, seismic activity, soil type, rock type, structural discontinuities, groundwater traces, and human development activities—were analyzed using field surveys, SPOT5 satellite imagery, and ASTER DEM data. A detailed landslide inventory map documenting 14 historical events was created through fieldwork and remote sensing analysis (satellite imagery and Google Earth). Bi-variate statistics were applied to calculate hazard indexes, which were then used to objectively calibrate and refine the conventional SSEP ratings. The integrated methodology produced a landslide susceptibility map classifying the area into four distinct zones: low, moderate, high, and very high susceptibility. Validation against the known inventory demonstrated the model's effectiveness, with 78.57
Malaria remains a major public health concern in Ethiopia, particularly in endemic settings like Arba Minch Town, where environmental and behavioural factors contribute to persistent malaria epidemic. Despite ongoing control efforts, malaria remains a substantial morbidity, mortality, and socio-economic disruption. The objective of the study was to examine epidemiological investigation of a possible malaria outbreak and risk factors in Arba Minch Town, Gamo Zone, Southern Ethiopia, 2023. A descriptive epidemiological analysis was followed by a 1:1 unmatched community-based case–control study conducted from October 28, 2022, to December 28, 2023. Ninety-four rapid diagnostic test-confirmed cases and 94 neighbourhood controls were selected. Data were collected through structured questionnaires. Data were collected and entered into Epi Info 7.2.6, and then exported to the Statistical Package for the Social Sciences (SPSS) version 26 for statistical analysis. Bivariable logistic regression was conducted, and candidate variables (p-value ≤ 0.25) were selected for multivariable logistic regression analysis. The degree of the association was assessed by computing the adjusted odds ratio (AOR) with a 95
BackgroundInconsistent condom use represents the most proximal behavioral risk factor for acquisition and transmission of sexually transmitted infections, including human immunodeficiency virus. However, certain situations hinder female sex workers from practicing consistent condom use. This study aimed to assess the pooled estimate of inconsistent condom use among female sex workers and identify factors associated with it.MethodsThis study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 2020 reporting checklist. Electronic databases (PubMed, Cochrane Library, Epistemonikos, Hinari, and Science Direct), Google Scholar, and other university repositories were searched until March 20, 2024, based on the eligibility criteria. Three independent reviewers screened the titles, abstracts, and full texts. Two independent reviewers extracted the data. The Joanna Briggs Institute quality appraisal checklist was used. The Higgin's I² test was used to quantify heterogeneity. Pooled analysis was conducted using a random-effects model. Sensitivity and subgroup analyses were done. Publication bias was assessed using Egger's regression test and funnel plot. The pooled prevalence and statistical association were declared at a p-value < 0.05 with the 95% CI.ResultsA total of 24 studies involving 23,496 female sex workers with a median age of 27.3 years were included. The overall pooled prevalence of inconsistent condom use among FSW in Africa was estimated at 46.73% (95% CI: 37.60, 55.86), I² = 99.59%, and p = 0.00. Condom availability (AOR = 0.68; 95% CI: 0.50, 0.92), depression (AOR = 1.51; 95% CI: 1.00, 2.30), no education (AOR = 1.87; 95% CI: 1.19, 2.93), two or more nonpaying clients (AOR = 2.90; 95% CI: 1.51, 5.54), having >9 current client number (AOR = 0.46; 95% CI: 0.29, 0.74), violence (AOR = 1.74; 95% CI: 1.33, 2.27), and police harassment (AOR = 2.28; 95% CI: 1.03, 5.05) were significant factors.Conclusion and recommendationInconsistent condom use was high in Africa. Factors including availability of condoms, depression, and education, having two or more nonpaying clients, client numbers, violence, and police harassment were significant factors. Strategies like improving peer education, providing mental health support, empowering women, and improving female sex workers educational status, ensuring condom availability, and strengthening supply for easily accessible condoms can decrease inconsistent condom use and protect FSWs from STI including HIV.
