Search Results (320)

In this paper, we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude M_T using a count of small earthquakes in the magnitude range M_S to M_T in the area. Using the idea that the Gutenberg–Richter (GR) relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. “Local” is defined by the probability of a large earthquake occurring within a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the GR magnitude–frequency relation holds. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which improves as the time since the last major earthquake increases. The probability is conditioned on the number of small earthquakes n(t), with M ≥ M_S = 3.49, that have occurred since the last large earthquake. The probability is computed directly as the Positive Predictive Value (PPV) associated with the ROC curve. The method is compared with the UCERF3 forecasts for the UCERF3-defined geographic boxes centered on Los Angeles and San Francisco and serves as an indicative benchmark. The method is then applied to a 125 km radius circular area around Los Angeles, California, following the 17 January 1994 magnitude M6.7 Northridge earthquake, and short-term forecasts (1-year and 5-year) are computed. We further apply the method to six additional geographic regions with validation by comparison with an estimate of the time-independent conditional Poisson probability. These regions are Athens, Greece; Chengdu, China; Jakarta, Indonesia; Lima, Peru; Santiago, Chile; and Tangshan, China. Full article

High-skilled labor plays an important role in regional economic development, yet accurately identifying its influencing patterns remains challenging due to complex factor interactions, spatial spillover effects, and fuzzy boundaries among urban characteristics. Traditional regression-based approaches primarily focus on isolated linear effects, making it difficult to capture multi-factor combinatorial relationships underlying talent agglomeration. To address these limitations, this study proposes a spatially aware fuzzy association rule mining framework by integrating soft-gated spatial weighting and concept stability theory. Using data from the Sixth and Seventh National Population Censuses and the China City Statistical Yearbook, the framework is applied to the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Middle Reaches of the Yangtze River (MRYR) regions from 2010 to 2020. The results show that the associative patterns of high-skilled labor evolved substantially across regions. In the BTH region, dominant factors shifted from administrative hierarchy and environmental amenities to stronger interactions between economic growth and talent inflow. In the YRD region, economic dynamism gradually replaced static geographic advantages, while in the MRYR region, market-oriented drivers increasingly surpassed administrative-led resource concentration. Overall, the findings suggest a transition from single-factor dependence to multi-factor coupled patterns in China’s regional talent agglomeration. Full article

Background: The Vehicle Routing Problem (VRP) is foundational to logistics optimization, yet its alignment with the Triple Bottom Line (TBL) and UN Sustainable Development Goals (SDGs) remains fragmented. This study conducts a strategic bibliometric audit of 301 peer–reviewed publications (1992–2025) to quantify the evolutionary progression and thematic maturity of sustainable routing research. Methods: A four–stage scientometric framework was employed, utilizing Scopus–based data retrieval, longitudinal mapping, and Python 3.14–driven text mining to visualize keyword co–occurrence networks, author collaborations, and regional research clusters. Results: Findings reveal a pronounced “Sustainability Asymmetry,” where 51.5% of studies prioritize economic efficiency, while only 2.6% address the social pillar. Additionally, social sustainability remains an “isolated island” with minimal cross–citation to the research core. Geographic analysis identifies a heavy concentration in China, the USA, and Western Europe, uncovering a critical North–South—collaboration gap. Conclusions: The study proves that while environmental themes reached maturity between 2018 and 2022, social indicators exhibit a significant maturity lag. This quantified social deficit, centered on the neglect of SDG 3 and SDG 10, mandates a fundamental paradigm shift toward a geographically inclusive and socially conscious research agenda to ensure global logistical equity. Full article

The switch in land use of abandoned tailings can precondition their reuse as newly built parks. This study investigated the feasibility of reusing a remediated mercury (Hg) retorting site in Wanshan, Guizhou Province, China, as a functional urban park by assessing residual heavy metal risks and associated vegetation responses. Field investigations were conducted across 31 park sites distributed along an east–west geographical gradient from the former mining area to urban parks, using replicated plots to sample the surface soils and dominant plant species. The concentrations of arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn), and lead (Pb) in soil and plant tissues were quantified using inductively coupled plasma–mass spectrometry, and vegetation structure and diversity were evaluated using standard community indices. The results showed significant spatial variability in soil and plant metal concentrations, with higher levels generally observed near historically impacted areas of the mine. However, all soil metal concentrations were below the national safety thresholds. Plant tissues exhibit controlled metal accumulation within normal or regulated ranges, reflecting the effective screening of tolerant and hyperaccumulating species. Increasing heavy metal concentrations were associated with reduced vegetation coverage, height, and diversity along the gradient. Overall, the findings indicate that the reclaimed Hg retorting site almost met ecological safety requirements, but more data on deep soils, groundwater, and long-term observations are needed to draw more conclusive conclusions. Full article

Microplastics constitute an emerging contaminant of major concern in Latin America, where human exposure predominantly occurs through ingestion of drinking water and marine/estuarine food chains. This review synthesises available evidence on occurrence, exposure pathways, toxicological mechanisms, and regional public health risks, while examining regulatory and monitoring limitations that constrain effective risk management. Reported concentrations in drinking water show a wide range (1–1194 particles/L), dominated by PET, PP, and PS, with fibres and fragments as the main morphotypes. In commercial marine species, prevalence reaches 70–100%, with burdens up to 44 particles/g in oysters and ~90 particles/250 g in mussels. Estimated Daily Intake is 2–5 times higher in children (e.g., Chile: 13.03 vs. 5.59 particles/day in adults). Toxicological mechanisms include oxidative stress, chronic inflammation (NF-κB pathway), endocrine disruption, intestinal dysbiosis, systemic translocation, and placental transfer, exacerbated by vectorization of local co-contaminants (Hg from mining, Cd/Pb from agriculture). Risk indices indicate extreme danger in Brazil, Chile, and Ecuador, where data are available. Significant geographic and methodological gaps persist, with Brazil dominating research (~50–60%). Multicenter biomonitoring, harmonised surveillance networks, and SDG-aligned policies are urgently needed to reduce exposure burdens, protect vulnerable populations, and advance toward comprehensive regional risk assessment. Full article

Underwater target detection via side-scan sonar is vital for defense and economy but hindered by sparse targets, high data costs, and feature extraction difficulties due to textureless acoustic data and limited samples. To overcome these limitations, particularly for few-shot, small-object detection, we propose a Spatial Distribution Probability-Guided Detection Framework to aid Unmanned Underwater Vehicles (UUVs) in precise localization and clustering. The framework features a novel module that leverages a pre-trained Vision Foundation Model (DINOv3) to generate spatial distribution probability maps, guiding a Transformer-based network for accurate detection with scarce data. Additionally, it incorporates a Target Position Calculation Module and a DBSCAN-based post-processing module to determine global geographic coordinates and cluster discrete points, respectively. Experiments were conducted on both a Public Mine Detection Dataset and a self-collected dataset containing simulated mines and buoys. Ablation studies and comparison experiments demonstrated that the proposed guidance mechanism significantly improves detection performance. Furthermore, two comb-search missions verified that the system could accurately locate and cluster targets, distinguishing real targets from false detections (noise). These results confirm the framework’s efficacy in enabling high-precision perception and autonomous operations for complex underwater inspection tasks. Full article

Burden of diseases measured as disability-adjusted life years (DALYs) per 100,000 people can be mined from public domain data, when they are made available by population health surveillance systems. This can be analysed to allow insightful comparisons with the national average, and to understand differences in trends between the sexes, age groups, time periods, geographic regions, and sub-regions. In this illustrative case study, we have analysed the Scottish burden of disease database to understand what ailed the population of the Grampian region before the COVID-19 pandemic. We have identified that selected cancers, ischaemic heart disease, Alzheimer’s disease and other dementias are amongst the highest contributors to the burden; that drug use disorders and colorectal cancer are showing worsening trends and require health promotion and disease prevention measures from ages 15 and 25, respectively, especially in Aberdeen City; and that males are more vulnerable to atrial fibrillation and flutter, diabetes mellitus, oesophageal cancer, and self-harm, while females are more vulnerable to cerebrovascular and chronic obstructive pulmonary diseases. We demonstrate the usefulness of our analysis and methodology for the wider health system, allowing targeted medical research investments and coordinated response from public health and health service delivery. We also show the need for up-to-date surveillance data, forecasts, and evidence on the impact of interventions to be made available widely. Full article

Assessing the ecological and environmental quality of old industrial cities is crucial for understanding the spatial heterogeneity of ecological quality and its associated factors during regional transformation. Taking Tangshan, a typical old industrial city in China, as a case study, this study employed Landsat 8/9 remote sensing imagery and multi-source auxiliary data from 2015 to 2024 to calculate annual Remote Sensing Ecological Index (RSEI) values using a unified multi-year standardization and principal component analysis framework. Global and local Moran’s I analyses were conducted to examine spatial clustering patterns, and the Optimal-Parameter Geographical Detector (OPGD) was used to quantify the spatial correspondence between RSEI and selected natural and anthropogenic explanatory factors. The results indicate the following. (1) The mean RSEI in Tangshan fluctuated between 0.34 and 0.54 from 2015 to 2024, exhibiting significant interannual variability. (2) Higher RSEI values were primarily distributed in the northern mountainous and southern coastal ecological zones, while lower values were concentrated in the central and eastern industrial-mining zones. (3) The global Moran’s I was significantly positive in all years (0.702–0.778, p = 0.001), indicating the persistence of spatial clustering; the proportion of non-significant local spatial units decreased from 72.00% in 2015 to 69.46% in 2024. (4) Land use/land cover (LULC) exhibited the most consistently high explanatory power. Elevation (ELE), nighttime light (NTL), and built-up intensity (BUILT) also formed a leading group of spatially associated factors, although their relative ranking varied between the optimal-parameter results and the robustness analysis. Slope (SLOPE), annual precipitation (Pre), and annual mean temperature (Tmean) generally showed relatively lower explanatory power. Interaction detection showed that pairwise factor combinations generally had higher q values than individual factors, with LULC × ELE showing consistently high explanatory power in representative years. This study provides a scientific reference for ecological and environmental monitoring and differentiated management in old industrial cities. Full article

Aiming at the problems of low information management levels and low reuse rate of massive heterogeneous geological data of the Jiangxi Geological Bureau, a Jiangxi geological big data platform based on cloud service and big data technology was designed to realize the integration and sharing of Jiangxi geological big data. Firstly, the architecture of the Jiangxi geological big data platform is designed based on hierarchical thinking, including the infrastructure layer, data layer, platform service layer, application layer and user layer from the bottom up. Secondly, the key technologies for building a Jiangxi big data platform are described, including multi-layer service aggregation, geographic information service bus, geocoding service, Spark big data technology and elastic scaling technology. Finally, the main functions of the Jiangxi geological big data platform are introduced, including a platform portal website, a mobile portal system, a geological big data comprehensive analysis system and a geological 3D modeling system. The operation results of the platform show that the Jiangxi geological big data platform can effectively manage the massive heterogeneous geological data of the Jiangxi Geological Bureau and mine the value of the data. Full article

Despite the proliferation of mining and industrial activities within the Mano River Union member states, sediment quality assessment remains limited due to the lack of a comprehensive geochemical dataset. To narrow this knowledge gap, we evaluated heavy metal(loid) concentrations in stream sediments from Lofa County, Liberia. A total of 313 samples were collected and analyzed for eight metal(loid)s (Cu, Pb, Zn, Cr, Cd, Ni, As, and Hg). The contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), and potential ecological risk index (PERI) were calculated independently against two background values: the average shale and upper continental crust (UCC) values. The UCC background values proved more appropriate than average shale for Liberia’s geographic location and geological setting, providing results that align with the empirical data. The results show that zinc concentrations were consistently low across all sampling sites, indicating regional depletion of the micronutrient. Despite variations in the methodological approaches, assessment results from all four indices identified mercury and arsenic as the contaminants of primary concern. The varying degrees of metal(loid) enrichment and depletion necessitate further research in the study area. This study should guide policymakers in devising a sustainable plan for tackling site-based contamination and delivering on the United Nations Sustainable Development Goal 6.3. Full article

Although geopolymer and alkali-activated binders are promoted as low-carbon OPC alternatives, their resource-centric performance remains complex and geographically dependent. This review examines these systems from a resource-efficiency perspective and evaluates alkaline activator demand; precursor availability, including fly ash, slag, calcined clays, and mining residues; and embodied energy across mix designs and curing regimes. Recent mechanical and durability analyses, together with life cycle assessments, reveal important trade-offs in alkali-activated geopolymer systems. Customized precursors may unintentionally compromise their inherent resource efficiency, while the declining availability of industrial waste increasingly competes with alternative waste valorization processes. Developing one-part activator systems and implementing data- or machine-optimized mix designs capable of handling extremely highly variable waste streams will be necessary to achieve meaningful reductions in mineral consumption, energy demand, and emissions. The study reframes these binders as enablers of urban mining and industrial symbiosis. Policy changes toward resource-oriented governance, including performance-based standards, carbon-responsive procurement, and more transparent end-of-waste legislation, are also needed to promote a circular material economy. Strategic, large-scale deployment requires the integration of regional resource mapping with predictive performance modeling to navigate resource constraints in the construction sector. Full article

Background/Objectives: Sosnowsky’s hogweed Heracleum sosnowskyi, which originated in the Greater Caucasus region and spread rapidly across Central and Eastern Europe after being introduced as cattle fodder in the 1950s, is an example of an extremely dangerous invasive species listed by the European Union. This study aimed to estimate the genetic diversity of 6 native populations of Sosnowsky’s hogweed from the Caucasus region of Russia and Georgia, as well as 15 invasive populations from Lithuania and Poland, and to assess the adaptability of hogweed in new environments. Methods: Genetic analyses of plant material were conducted, including DNA extraction, ISSR genotyping, PCR product separation, and subsequent molecular data mining and analysis. Results: A pairwise Mantel test revealed a positive correlation between geographical distance and the genetic diversity of the hogweed populations. The presence of three distinct allele pools was confirmed in the populations under study, with genotypes from Poland dominated by the first allele pool, which had the largest number of polymorphic and private loci. Analysis of molecular variance by origin showed that 99% of the variation was within the analysed hogweed populations, with only 1% being between them. Native populations from Russia were genetically distinct from those in Poland and Lithuania. Some of the Georgian population shows genetic similarities to Russians, while the rest shows similarities to the secondary invasive Lithuanians. Conclusions: Introduced populations of H. sosnowskyi are characterised by considerable genetic variation, likely resulting from multiple introductions and subsequent evolutionary processes, which may facilitate local adaptation and invasiveness, although overall large-scale genetic differentiation remains low. Full article

In light of the accelerating process of global urbanization, the quality of cultural ecosystem services (CES) in urban parks has become a core metric for efforts to promote urban livability and sustainable cities. However, previous research has failed to consider the differential impacts of the external environment across various travel scenes. In this study, 32 parks in Chengdu serve as the empirical data, and public CES perception data are extracted from social media comments via text mining. Based on a unified 15 min time threshold, we delineate the service scope for four travel scenes and employ geographically weighted regression and piecewise regression models to analyze the spatial heterogeneity, driving mechanisms and threshold effects associated with the relationship between external environmental factors and park CES. The findings indicate that the external environment’s influence on CES exhibits a “scene-factor-scale” adaptation pattern. Walking scenes are influenced primarily by land-use and population factors; in contrast, cycling scenes rely on the availability of shared bicycle facilities, and public transport and driving scenes are driven by economic vitality and traffic-support factors, respectively. Five critical thresholds are identified, including a 40% impervious surface area. This research proposes scene-based optimization strategies and helps enhance the “external environment–travel behavior–spatial characteristics” coupling framework, thereby serving as a scientific reference for efforts to improve 15 min living circles. Full article

Socio-environmental conflicts in mining regions are often examined through political, economic, or social lenses, while the role of land-system transformation remains less integrated into quantitative analysis. This study examines the co-evolution of socio-environmental conflict and territorial change in Las Bambas (Apurímac, Peru) as a socio-territorial process. Annual conflict records from the Peruvian Ombudsman’s Office (2007–2024) were combined with annual land-cover data from MapBiomas. Yearly conflict influence zones were reconstructed from reported affected communities and geographic features using buffered spatial entities and concave hull polygons. Clustering methods (K-medoids, DBSCAN, and agglomerative hierarchical clustering) and FP-Growth association rule mining were applied to 23 unique conflicts consolidated from the original records and encoded with 10 root causes. The most intense conflict phases were accompanied by measurable landscape transformations, including the emergence of mining-related land cover from 2012 onward, sustained loss of high-Andean natural vegetation, expansion of agricultural mosaics, urban growth along the Apurímac–Cusco corridor, and hydrological alterations in wetlands and headwaters. Three conflict typologies were identified, with unfulfilled company commitments emerging as the most recurrent co-occurring grievance. The dynamic polygon approach offers a replicable framework for linking conflict records with land-system change in extractive regions. Full article

Understanding how Bitcoin mining is distributed across countries is important for evaluating both the sustainability and resilience of the network. In this study, we examine the evolution of total Bitcoin electricity consumption alongside the geographic distribution of Bitcoin mining. Data are provided by the Cambridge Centre for Alternative Finance (Licensed under CC BY–NC–SA 4.0): Annual data from the Cambridge Bitcoin Electricity Consumption Index (2010–2025) and a monthly panel of country-level Bitcoin hashrate shares for 105 countries (September 2019–January 2022). To assess the degree of decentralization in the global mining network, we employ entropy-based measures, inequality indices, and panel convergence tests. The results indicate that total electricity consumption grew exponentially during the early years of Bitcoin, but later transitioned to a more stable and approximately linear path. Country-level permutation entropy reveals highly volatile and dynamic mining trajectories. The Theil index shows that cross-sectional inequality declines over time, while increasing symbolic entropy reflects a progressively more even cross-country distribution of mining activity. Further evidence from $\sigma$-convergence supports a statistically significant reduction in cross-country dispersion of mining shares. Dynamic panel fixed-effects estimates reveal mean-reverting behavior in relative country shares, consistent with stochastic convergence. Finally, Phillips–Sul analysis points to heterogeneous early transition paths but ultimately supports convergence toward a single global club. The gradual geographical decentralization occurs alongside persistent core–periphery asymmetries in long-run mining shares. Overall, our findings suggest that Bitcoin mining behaves as a globally integrated industry in which computational capacity reallocates rapidly across countries in response to economic and regulatory conditions. Full article