Search Results (2,112)

Camels are increasingly recognized as an important epizootological and epidemiological link in natural plague foci, contributing to the transmission of Yersinia pestis from wildlife to humans. In the Republic of Kazakhstan, where natural plague foci occupy up to 40% of the territory, the rapid growth of camel populations may significantly enhance epidemiological risks. The aim of this study was to perform a comprehensive assessment of the role of camels in the epizootology and epidemiology of plague based on retrospective data (1907–2003) and contemporary monitoring (2000–2025), including spatial analysis and risk zoning. A total of 64 cases of camel plague and 43 epizootic foci were identified during the historical period. More than 400 human cases, including fatal outcomes, associated with infected camels were documented, with direct contact during slaughter and meat processing accounting for 94.7% of infections. Spatial analysis and epidemiological zoning revealed a heterogeneous risk distribution, with western and southern regions representing the highest-risk areas. Serological investigation (n = 2726) showed 75.6% seropositivity, likely reflecting substantial population immunity largely associated with vaccination. Despite increasing camel population size (from 227.7 to 304.0 thousand heads in 2020–2025), vaccination coverage varied between 32.0% and 51.0%, reflecting risk-based preventive strategies. The absence of recent camel plague cases supports the effectiveness of integrated control measures, including vaccination, surveillance, and the establishment of protective zones. These findings suggest that camels remain an important component of plague transmission systems and should be systematically integrated into surveillance programs within a One Health framework. Full article

Tailings are solid waste generated during mining and mineral processing. Their tremendous accumulation not only encroaches on arable land but also pollutes the environment. Currently, tailings are considered a viable alternative to natural fine aggregates in concrete because of their suitable physicochemical properties. However, existing studies remain highly fragmented and often report inconsistent conclusions owing to the considerable variability in tailings mineralogy, particle morphology, and physicochemical characteristics. To date, a comprehensive synthesis linking these intrinsic properties to the fresh, mechanical, durable, microstructural, environmental, and economic performance of tailings concrete remains lacking. Therefore, this review provides a systematic and critical assessment of tailings as aggregate in concrete and proposes an integrated framework connecting tailings characteristics, microstructural evolution, engineering performance, and sustainability outcomes. It systematically examines the physico-mechanical properties, durability, microstructure, hydration characteristics, environmental impact, and economic benefits of the resulting tailings concrete. The results showed that although tailings varied considerably in particle size, chemical composition, and mineralogy, they typically exhibited a rough surface texture and high water absorption. Furthermore, partial substitution of fine aggregates with tailings was found to improve the physical–mechanical properties and durability. However, to prevent performance decline, the substitution ratio should not exceed 50%. These benefits originated primarily from the filling effect and optimized particle packing, which increased matrix density. Microstructural analyses indicated that moderate tailings contents refined the pore structure, strengthened the interfacial transition zone (ITZ), and promoted hydration. In contrast, excessive substitution ratios weakened bonding and increased porosity. From an environmental perspective, the use of tailings generally reduced carbon emissions (by up to ~28%) and production costs (by up to ~50%) by lowering natural resource consumption and enabling large-scale waste valorization. Overall, tailings represent a sustainable aggregate alternative, provided that substitution levels are carefully controlled to balance workability, performance, and durability. Full article

Landslide susceptibility assessment is important for disaster prevention and sustainable land-use planning in mountainous regions. However, conventional discretization methods often overlook threshold effects in conditioning factors, and many machine learning models still have limited interpretability. This study develops an integrated framework that combines ChiMerge discretization, Weight of Evidence (WOE) transformation, and tree-based ensemble learning to map landslide susceptibility in Jiuzhaigou County, Sichuan Province, China. A landslide inventory of 164 points was compiled from field investigations and hazard records, and fourteen topographic, geological, and environmental conditioning factors were derived from multi-source spatial datasets. Continuous factors were discretized using ChiMerge, a supervised chi-square-based discretization method that identifies statistically meaningful thresholds according to the distributions of landslide and non-landslide samples. WOE values were then calculated to quantify the association between each factor class and landslide occurrence. Three WOE-based ensemble models, WOE-CatBoost, WOE-LightGBM, and WOE-RF, were constructed and compared. All models showed high predictive performance (AUC > 0.90), with WOE-CatBoost performing best (AUC = 0.9432). Its high and very high susceptibility zones covered 28.59% of the study area but contained 85.96% of observed landslides. High-risk areas were mainly concentrated in steep valleys, fractured lithological zones, erosion belts, and areas affected by engineering activities, such as road construction, slope cutting, tourism infrastructure development, and settlement expansion. The proposed framework improves prediction accuracy and interpretability and provides spatial support for landslide prevention and sustainable land-use management. Full article

Background: Polymicrobial skin and soft tissue infections (SSTIs) are frequently complicated by methicillin-resistant Staphylococcus aureus (MRSA) and co-colonizing Gram-negative pathogens like Pseudomonas aeruginosa (P. aeruginosa). Mupirocin, the clinical gold standard, is limited by rising resistance and an intrinsic “mupirocin gap” against P. aeruginosa. This study evaluates a novel Nigella sativa (NS) seed oil topical formulation as an alternative. Methods: A 4-tier preclinical platform assessed the NS formulation against MRSA, methicillin-sensitive S. aureus (MSSA), Streptococcus pyogenes, and P. aeruginosa. The pipeline included: (1) in vitro agar diffusion, (2) a gauze biofilm prevention model, (3) an ex vivo porcine ear skin model challenging epidermal lipid barriers, and (4) an in vivo Galleria mellonella model evaluating trans-cuticular systemic protection. Results: The NS formulation produced extensive diffusion zones, completely inhibiting S. pyogenes and outperforming controls against MSSA and P. aeruginosa. In the gauze model, NS achieved complete eradication of MSSA and S. pyogenes, while significantly suppressing MRSA and P. aeruginosa biofilms (p < 0.001). In the ex vivo porcine model, NS yielded >1.5 to >2.5 log reductions across all pathogens at 24 h (p < 0.001). Furthermore, in the in vivo G. mellonella model, topical NS significantly reduced the systemic bioburden of MSSA, S. pyogenes, and P. aeruginosa (p < 0.001), though MRSA reduction lacked statistical significance. Conclusions: The novel NS formulation demonstrates potent broad-spectrum antimicrobial activity. By effectively bridging the “mupirocin gap” against P. aeruginosa and demonstrating significant efficacy against MRSA in in vitro and ex vivo environments, it represents a promising plant-based pre-clinical candidate that strongly warrants future evaluation in live mammalian wound healing models. Full article

The magmatic Ni-Co-Cu mineralization in the Iken deposit in the central part of the Kun-Manie complex, Amur Oblast, Russia, hosted by an olivine-bearing websterite, is of a low-sulfide type. The fine-grained disseminations of base metal sulfides (BMS), dominantly pyrrhotite, pentlandite (a major source of Ni of industrial importance), and chalcopyrite, are followed by a scarce Pd-Pt-Ag mineralization. Elevated contents of Al in orthopyroxene (mean 2.78 wt.% Al₂O₃) along with Al–Na enrichment in clinopyroxene (diopside; mean 5.10 wt.% Al₂O₃) are associated with highly aluminous compositions of low-chromium members of the spinel–hercynite series. High levels of TiO₂ in kaersutite and titanian phlogopite also reflect a pronounced degree of fractionation of the ore-forming melt. Minor portions of sulfide melt are distributed evenly as a result of immiscibility at advanced stages of orthopyroxene crystallization, after the formation of olivine. Differentiated grains of droplet-like BMS largely settled in situ close to grain boundaries of orthopyroxene or occupied interstitial spaces of pyroxenes and olivine in association with spinel–hercynite and fluorapatite. A combination of late saturation in S with relatively quick cooling rates of the hypabyssal body prevented the effective settlement and accumulation of sulfide droplets in the ore zone. The well-developed lamellae of troilite (Fe₅₀S₅₀) exsolved from the host pyrrhotite Fe₄₈S₅₂ during subsolidus cooling, as a consequence of a low-temperature reaction triggered by a sudden drop in fO₂. An influx of mantle-derived fluid bearing CO₂, CO, and CH₄ with the rising magma could be the primary cause of the fO₂ reduction. Also, graphite-bearing metasedimentary rocks could have been assimilated. Tiny grains of minerals of noble metals (moncheite and merenskyite with essential amounts of melonite component, sperrylite, hessite, alloy Au_63.2Ag_36.8, and argentopentlandite) deposited late in a fluid-enriched medium under submagmatic conditions. Full article

Groundwater level mapping is used in order to detect aquifer locations, flow paths, recharge zones, and contamination/pollution of groundwater. This is crucial for water management, environmental studies and resource planning focusing on landfills. This study involves the collection of monitoring well data and the mapping of the groundwater table at the Mavrorachi landfill site using Geographic Information Systems (GISs). The monitoring period spans from 2008 (startup) to 2025 (the current full year) for the 11 monitoring boreholes. Interpolation methods in GISs enabled us to map the groundwater level, while spatial analysis tools modeled the potential groundwater flow. The above process proved to be a valuable tool for modeling groundwater resources. The monitoring of groundwater level is essential to prevent the impact of leachate generated from landfill. Full article

Optimal coordination of directional overcurrent relays (DOCRs) in medium-to-large power systems constitutes a computationally demanding, mixed-integer, nonlinear optimisation problem whose complexity escalates rapidly with system size, making the simultaneous minimisation of relay operating time and computational cost a critical open challenge. This study presents a two-level hierarchical framework in which Type-2 Fuzzy C-Means (T2FCM) clustering partitions 226 fault scenarios into subproblems at the upper level, while the Hybrid Fractional Entropy Evolution (HFEE) algorithm independently optimises relay settings for each cluster at the lower level. HFEE integrates fractional-order velocity updates—derived from the Grünwald–Letnikov formulation—with a Shannon entropy diversity-control mechanism to prevent premature convergence. T2FCM captures inherent fault-current uncertainty through interval-valued type-2 fuzzy memberships, yielding more robust cluster assignments near protection-zone boundaries than crisp partitioning methods. The framework is validated on the extended IEEE 30-bus system. An ablation study demonstrates that standalone HFEE achieves a 29.19% improvement in $T_{\mathrm{op}}$ over the prior best-reported result; however, a comprehensive parameter sweep over cluster counts $K\in \{2,\dots ,8\}$ and fractional orders $\alpha \in \{0.1,\dots ,0.9\}$ across 50 independent runs per configuration shows that the proposed clustering-decomposed method achieves 3.68–66.67% lower wall-clock computation time while maintaining zero CTI violations across all active relay pairs. The communicationless, entirely offline framework demonstrates scalability for simultaneous sub-transmission and distribution protection coordination and offers a practically deployable strategy for modern power networks. Full article

Heavy metal(loid) pollution in watersheds surrounding mining areas originates from multiple and complex sources, posing persistent threats to terrestrial–aquatic ecosystems and human dietary safety. This study systematically investigated the pollution characteristics, spatial distribution, ecological risks and human health hazards of seven typical heavy metal(loid)s (As, Pb, Cr, Cd, Cu, Zn, and Ni) in the integrated water–soil–vegetable continuum of a mining-affected watershed in Southwest China. Field sampling was carried out in three functional zones with different mining disturbance intensities, and inductively coupled plasma mass spectrometry (ICP-MS) was used to detect heavy metal(loid) concentrations in all samples. Multiple pollution evaluation indices and the USEPA human health risk assessment model were adopted for comprehensive quantitative analysis. The results showed that 44.0% of surface water samples exceeded national permissible limits, with high-pollution areas concentrated in intensive mining zones, presenting moderate overall aquatic heavy metal(loid) pollution. Although the average concentrations of seven heavy metal(loid)s in riparian soils complied with Chinese agricultural soil screening standards, localized significant enrichment was observed for As (1.98 times), Cd (4.62 times), Cu (1.81 times), and Zn (2.72 times) compared with regional background values, causing mild comprehensive soil pollution. Farmland soils exhibited prominent Cu and Zn accumulation, and leafy vegetables in the study area suffered severe Pb and Cd pollution, with potential dietary exposure risks. Health risk assessment indicated that children face higher non-carcinogenic and carcinogenic risks than adults via soil hand-to-mouth exposure; dietary intake of vegetables leads to moderate carcinogenic risks for children caused by As and Ni exposure. Overall, this study clarifies the migration and enrichment rules of heavy metal(loid)s in the water–soil–vegetable system of mining watersheds, confirms the prominent ecological and human health risks of Cd, As and Pb in the study area, and provides targeted basic data for regional heavy metal(loid) pollution prevention and food safety management. Full article

Numerous space missions are advancing our understanding of the origin and evolution of planetary bodies and the potential for the emergence of life throughout the Solar System and beyond. Investigations across the inner Solar System have revealed contrasting planetary environments: Venus offers insights into runaway greenhouse processes, while Mars remains a primary target for studying climate evolution, atmospheric loss, past water activity, and extinct life, with sample return missions planned in the next decade. Beyond the traditional habitable zone, attention has shifted to the icy moons of Jupiter and Saturn. Data from space missions have identified subsurface oceans and possibly active geology on moons such as Europa, Ganymede, Titan, and Enceladus, highlighting their astrobiological potential. Among others, Europa’s ocean, possibly interacting with a silicate mantle and sustained by tidal heating, Enceladus plumes and Titan’s complex organic chemistry make these worlds compelling targets. Current and upcoming missions will further explore these environments and refine our understanding of habitability. This work also emphasizes the importance of planetary protection to prevent biological contamination, particularly for sample return missions. Continued exploration, supported by international collaboration and technological innovation, will be essential to address engineering challenges and to expand our knowledge of potentially habitable environments across the Solar System. Full article

Background: Healthcare-associated infections (HAIs) remain a critical patient safety challenge. Hand hygiene is considered the most effective preventive measure, yet traditional monitoring captures only compliance, not technique quality. This prospective before–after study evaluated whether real-time visual feedback via the Semmelweis UV-fluorescence system is associated with improved hand hygiene quality, measured by ATP bioluminescence and microbiological culture. Methods: Three clinical departments (the Intensive Care Unit, Hematology, and Gynecology) at a Romanian tertiary hospital were purposively selected. Seventy-one healthcare workers (HCWs) were enrolled. The 12-week study comprised Phase 1 (baseline, weeks 1–4), Phase 2 (active intervention with Semmelweis feedback, weeks 5–8), a one-week washout (week 9), and Phase 3 (sustainability assessment, weeks 10–12). Paired ATP-CFU samples were collected weekly. Within-group comparisons used Kruskal–Wallis H tests with post hoc Dunn’s tests and Bonferroni correction. Secondary outcomes included Semmelweis global and zone-specific coverage and the correlation between subject-level Semmelweis coverage and ATP bioluminescence (Spearman’s rho). Results: A total of 781 paired ATP-CFU samples and 497 Semmelweis evaluations were analyzed. Mean ATP declined from 195.9 RLU at baseline to 148.2 RLU in Phase 2 (−24.4%) and 154.8 RLU in Phase 3 (−21.0%; Kruskal–Wallis H = 102.73, p < 0.001). CFU/mL declined from 84.8 to 66.2 (−21.9%) and 70.7 (−16.6%; H = 22.48, p < 0.001). Post hoc comparisons confirmed significant Phase 1 versus Phase 2 and Phase 1 versus Phase 3 differences for both markers (all p < 0.01), while Phase 2 versus Phase 3 was non-significant, indicating stabilization at an improved level. Subject-level Semmelweis coverage correlated negatively with ATP (rho = −0.665, 95% CI −0.778 to −0.510, p < 0.001), supporting construct validity at the operator level. Semmelweis global coverage was 93.1% (Phase 2) and 90.6% (Phase 3); interdigital spaces showed the highest inadequacy rate (73.9% protocol-based, 92.5% targeted). Conclusions: Real-time visual feedback via UV-fluorescence imaging was associated with significant and sustained improvements in hand hygiene quality beyond baseline. ATP, CFU, and Semmelweis assessments captured complementary, non-redundant dimensions, supporting multimodal evaluation. Interdigital spaces and fingertips remained persistent failure points requiring targeted educational reinforcement. Full article

This study applies the GALDIT index within a GIS environment to assess groundwater vulnerability to seawater intrusion (SWI) in the coastal aquifer of Katerini, Greece. In addition, a single-parameter sensitivity analysis (SPSA) is performed to evaluate the relative influence of the GALDIT parameters and to produce a modified vulnerability map based on SPSA-derived weights. The results indicate that, in both cases, most of the study area exhibits low and moderate vulnerability, with high vulnerability limited to small areas along the coastal zone. Minor differences were observed between the two approaches, with the modified GALDIT map showing a slightly larger proportion of moderate-vulnerability areas. The SPSA further revealed that L is the most influential parameter in the GALDIT index, whereas G and I have the least influence. Overall, the study findings may serve as a valuable basis for prioritizing groundwater monitoring and management in the study area, and for guiding targeted measures to help prevent SWI. Full article

Mapping wildfire patterns in Wildland–Urban Interface (WUI) areas is a fundamental tool for fire management and prevention, particularly in regions where urban expansion occurs in close proximity to natural vegetation. This mapping approach makes it possible to identify critical zones and to support more effective interventions adapted to the specific conditions of each territory. This work analyzed wildfires in the state of Tocantins, Brazil, using detailed geospatial data and advanced analysis techniques and statistics to characterize the dynamics of burned areas. Data used for the project were retrieved from MapBiomas and the Geoprocessing Laboratory of the Public Ministry of Tocantins (LABGEO), applying logistic regression models to explore the relationship between the distance of WUIs and the frequency of wildfires. The methodology covered the spatial distribution of fires and the different dynamics observed by type and size of burned area, allowing for a more detailed analysis. The results indicated significant variations in the proportion of burned areas inside and outside the WUIs, suggesting that proximity to these interfaces plays a critical role in the occurrence pattern of fires. Notably, Palmas, the state capital, stood out as one of the municipalities with the highest concentration of impacts in WUI areas, highlighting the relevance of these zones in environmental risk management. The study emphasizes the importance of adopting regional approaches that consider local specificities in the management and prevention of wildfires. The integration of geospatial data with robust statistical methodologies can guide more effective management strategies, assisting in the planning of public policies adapted to the socio-environmental dynamics of Tocantins. Full article

Landslides constitute a globally pervasive and highly destructive natural hazard. Although artificial intelligence (AI)-driven landslide susceptibility mapping has emerged as an effective tool for delineating high-risk zones, its predictive performance is frequently constrained by inherent data noise and insufficient characterization of landslide triggering factors, restricting the credibility of the mapping results. In this study, to remedy this limitation, we adopt fractal analysis to extract latent inherent information from topographic features. Specifically, the box-counting method and multifractal analysis are applied to excavate the intrinsic nonlinear characteristics embedded in eight topographic factors, and an improved K-means algorithm is utilized to perform feature selection and construct a dedicated fractal feature dataset, which is fed to advanced AI models. Our results indicate that the information dimension ($D_1$) of the slope gradient, the correlation dimension ($D_2$) of aspect, land relief, the $D_2$ of roughness, the $D_2$ of plan curvature, the multifractal spectrum width ($∆\mathsf{\alpha }$) of profile curvature, the $D_2$ of elevation, and the surface cutting depth were the most effective features, demonstrating superior performance in capturing landslide targets. Comparative performance evaluations reveal that AI models trained on fractal features demonstrate substantially superior predictive capabilities compared to AI models trained on raw features. This superiority is consistently evidenced across key evaluation metrics, including overall accuracy, kappa coefficient, F1-score, and predictive efficiency, demonstrating that the integration of fractal characteristics significantly augments model robustness and predictive efficacy. To mitigate the ‘black-box’ problem of AI modeling, Shapley additive explanations were employed to quantify individual feature contributions and elucidate the underlying predictive mechanisms. Our findings indicate that the integration of fractal analysis yields highly discriminative and robust feature representations, thereby expanding the representational capacity of the models and improving predictive accuracy. Furthermore, a joint assessment of spatial uncertainty and susceptibility maps demonstrates that these models exhibit low predictive variance and high spatial stability when delineating high-susceptibility zones. Notably, models utilizing fractal-derived features achieve superior spatial capture efficiency. The resultant topographic features characterized by fractal representation and selected via the improved K-means algorithm can significantly improve the predictive performance of trained AI models in landslide susceptibility mapping tasks, offering a scientific and viable technical approach for future landslide prediction and prevention. Full article

Under climate change, the increasing typhoon intensity poses a severe threat to fishery harbor safety through storm surges and extreme waves. Traditional empirical management approaches fail to capture the complex wave-surge coupling inside harbors, leading to risk blind spots in berth allocation. This study enhances the fishery harbor disaster resilience by employing high-resolution coupled wave-storm surge modeling, taking Xinying Central Fishing Harbor (Hainan, China) during Super Typhoon Yagi (September 2024) as a case study. A Holland typhoon model integrated with ERA5 reanalysis data was used to reconstruct the wind field, which subsequently drove a one-way coupled MIKE 21 FM–SW model to simulate regional tides and deep-water waves. A Boussinesq wave model was then applied to resolve nearshore shallow-water wave transformations inside the harbor. Model validation showed strong agreement with observations: correlation coefficients of 0.97 for tides in Xinying station and 0.95, 0.97, 0.93 for significant wave heights in three buoys around Hainan island, with root-mean-square errors of 0.19 m and 0.67, 0.69, 0.31 m, respectively. The Boussinesq wave simulations revealed detailed spatial distributions of wave heights inside the harbor during the typhoon. Based on these simulations, a dynamic berth zoning strategy was developed, mapping safety zones for different vessel sizes according to wave-height tolerance (e.g., ≤0.6 m for medium-sized trawlers). This framework can provide potential support for decision-making regarding fishing vessel refuge during typhoons, maximizing safe capacity while minimizing capsizing risks. Overall, this study demonstrates a feasible pathway from advanced numerical modeling to practical engineering management, supporting a transition from experience-based to data- and model-driven disaster prevention for coastal fishery harbors. Full article

The efficient conversion of methane into hydrogen-rich syngas is essential for sustainable energy; however, integrating methane partial oxidation (POM) with the water–gas shift (WGS) reaction remains a significant challenge due to thermal and kinetic mismatches. This research presents a spatially decoupled dual-bed reactor configuration, utilizing Ni/GDC and Cu/GDC catalysts, to achieve synergistic hydrogen production. Unlike conventional physically mixed systems, which suffer from thermal hotspots and the unintended promotion of the endothermic Reverse Water–Gas Shift (RWGS) reaction, the dual-bed architecture effectively segregates the reaction zones. Advanced characterization, including O₂-TPO and Raman spectroscopy, reveals that the GDC support acts as a critical oxygen buffer via the Mars-van Krevelen mechanism, modulating the dynamic redox state of the active metal sites to prevent deep oxidation and carbonaceous deactivation. Furthermore, macroscopic performance and carbon–oxygen mass balance analyses confirm that this rational architectural design facilitates a seamless integration of POM and WGS pathways, resulting in significantly maximized H₂ yield. From a broader engineering perspective, this dual-bed strategy offers a practical, low-complexity alternative to intensive integrated technologies such as sorption-enhanced reforming (SER) or chemical looping, providing a robust and scalable framework for durable, high-efficiency hydrogen production. Full article