Search Results (10,020)

Wetlands are a critical component of the global biogeochemical cycle and have great potential for carbon sequestration under the changing climate. However, previous studies have mainly focused on the dynamics of soil organic carbon while paying little attention to the vegetation carbon storage in wetlands. Poyang Lake is the largest freshwater lake in China, where intra-annual and inter-annual variations in water levels significantly affect the vegetation carbon storage in the floodplain wetland. Therefore, we assessed the seasonal distribution and carbon storage of six typical plant communities (Arundinella hirta, Carex cinerascens, Miscanthus lutarioriparius, Persicaria hydropiper, Phalaris arundinacea, and Phragmites australis) in Poyang Lake wetlands from 2019 to 2024 based on field surveys, the literature, and remote sensing data. Then, we used 16 preseason meteorological and hydrological variables for two growing seasons to investigate the impacts of environmental factors on vegetation carbon storage based on four correlation and regression methods (including Pearson and partial correlation, ridge, and elastic net regression). The results show that the C. cinerascens community was the most dominant contributor to vegetation carbon storage, occupying 12.68% to 44.22% of the Poyang Lake wetland area. The vegetation carbon storage in the Poyang Lake wetland was significantly (p < 0.01) higher in spring (87.75 × 10⁴ t to 239.10 × 10⁴ t) than in autumn (77.32 × 10⁴ t to 154.78 × 10⁴ t). Water body area emerged as a key explanatory factor, as it directly constrains the spatial extent available for vegetation colonization and growth by alternating inundation and exposure. In addition, an earlier start or end to floods could both enhance vegetation carbon storage in spring or autumn. However, preseason precipitation and temperature are negative to carbon storage in spring but exhibited opposite effects in autumn. These results assessed the seasonal dynamics of dominant vegetation communities and helped understand the response of the wetland carbon cycle under the changing climate. Full article

The economic importance of Carlin-type gold deposits is complicated by the concealed nature of stratiform gold-bearing zones and their occurrence at depths of several tens of meters or more below the present-day surface. This necessitates the use of a wide range of technologies and unconventional, including cost-effective and environmentally friendly, exploration methods to delineate potentially prospective areas. This study explores the possibilities of applying remote sensing methods to organize prospecting and exploration activities for targeting Carlin-type deposits in a more efficient and cost-effective way. The location of Carlin-type gold deposits within areas of orogenic and post-orogenic magmatism, mantle plumes, and linear crustal structures—as demonstrated by previous research in the Nevada and South China metallogenic provinces—may serve as a basis for developing a conceptual model of their distribution. To this end, we developed the GeoNEM (Geodynamic Numeric Environmental Modeling) software in Python, which enables the analysis of the formation of fold and fault structures, melt emplacement and contamination, as well as the duration and rate of geodynamic processes. GeoNEM is based on the computational geodynamics “marker-in-cell” (MIC) method, which treats geological media as extremely high-viscosity fluids. Locations of the brittle deformations of the crust, the formation of which was simulated numerically, can be detected through lineament analysis of remote sensing images. The spatial distribution of such structures—lineaments—serves as a predictive criterion for assessing the prospectivity of territories for Carlin-type gold deposits. It has been demonstrated that remote sensing provides a modern level of efficiency, cost-effectiveness, and comprehensiveness in approaching the exploration and assessment of new Carlin-type gold deposits. This is particularly important in the context of rational resource utilization and cost reduction. Full article

The issue of formulating scientifically sound standards for mercury (Hg) content in Arctic soils is becoming increasingly pertinent in view of the rising human impact and climate change, which serve to augment the mobility of Hg compounds and their involvement in biogeochemical processes. In the absence of uniform criteria for regulating Hg concentrations, it is particularly important to determine its geochemical baseline values and the factors that determine the spatial and vertical distribution of the element in the soil profile. The study conducted a comprehensive investigation of Hg content and patterns of its distribution in various types of tundra soils in the European North-East of Russia. The mass fraction of total Hg was determined by atomic absorption spectrometry, and the spatial features of accumulation were analysed using geoinformation technologies. The distribution of Hg in the soils of the tundra zone was found to be distinctly mosaic in nature, determined by the combined influence of organic matter, granulometric composition, and hydrothermal conditions. It has been established that the complex influence of the physicochemical properties of soils determines the spatial heterogeneity of Hg distribution in the soils of the tundra zone. The most effective Hg accumulators are peat and gley horizons enriched with organic matter and physical clay fraction, while in Podzols, vertical migration of Hg is observed in the presence of a leaching water regime. In order to standardise geochemical baseline Hg values, a 95% upper confidence limit (UCL_95%) is proposed. This approach enables the consideration of natural background fluctuations and the exclusion of extreme values. The results obtained provide a scientific basis for the establishment of standards for Hg content in background soils of the Arctic. Full article

The Food Bank of Bogotá is a non-profit organization whose primary mission is to provide food aid to economically vulnerable people and others. One of its key operations is the distribution of food to over 600 beneficiaries. In this research, we present the design and implementation of a computer application that calculates the delivery schedule of the Food Bank vehicles. Firstly, the beneficiaries of the Food Bank are clustered into four delivery zones, and their orders are assigned to specific weeks of the month. Next, a variant of the Capacitated Periodic Vehicle Routing Problem (CPVRP) is solved with an open-source tool. Lastly, routes are assigned to days of the week depending on the traffic conditions. The numerical results showed significant improvements in terms of total time reduction with respect to the business-as-usual practice. This tool is essentially for the monthly planning of the distribution of routes. These routes eventually will need adjustments because of changes in the beneficiaries’ demand, traffic conditions, fleet availability, and so forth. At the time of writing, the model is being integrated with another application that records and tracks the orders in the Food Bank. The users of this application would handle the daily operation and will make manual adjustments if needed. Finally, we discuss the main limitations of the application, which lie primarily in the need to educate both the Food Bank staff and the beneficiaries’ management, who are accustomed to last-minute orders, very tight time windows, and reactive delivery schedules that are highly inefficient. Full article

The rapid growth in Internet-based services has increased the demand for user data to enable personalized and adaptive digital experiences. These services typically require users to disclose various types of personal information, which are organized into user profiles and used to tailor content, recommendations, and accessibility settings. However, achieving an effective balance between personalization accuracy and user data protection remains a persistent and complex challenge. Excessive data disclosure raises the risk of re-identification and privacy breaches, while excessive anonymization can significantly diminish personalization and overall service quality. In this paper, we address this trade-off by proposing a context-aware learning-based data generalization framework that preserves user privacy while maintaining the functional usefulness of personal data. We first conduct a systematic classification of user data commonly collected into five main categories: demographic, location, accessibility, preference, and behavior data. To generalize these data categories dynamically and adaptively, we use a Multi-Layer Perceptron (MLP) model that learns patterns across heterogeneous data types. Unlike traditional rule-based generalization techniques, the MLP-based approach captures nonlinear relationships, adapts to heterogeneous data distributions, and scales efficiently with large datasets. The proposed MLP-based generalization method reduces the granularity of personal data, preserving privacy without significantly compromising information usefulness. Experimental results show that the proposed method reduces the risk of re-identification to approximately 35%, compared to non-anonymized data, where the re-identification risk is about 80–90%. These findings highlight the potential of learning-based data generalization as a strategy for privacy-preserving personalization in modern Internet services. They also show how the proposed generalization method can be applied in practice to transform user data while maintaining both utility and confidentiality. Full article

The Micangshan lead–zinc deposits, located in the northern margin of the Sichuan Basin, are classified as the Mississippi Valley-type (MVT) deposits. This study investigates the genetic linkage between Pb–Zn mineralization and paleo-oil reservoirs in the region, which is distinct from separate investigations on lead–zinc deposits or paleo-oil reservoirs. Through mineralogy, isotope, and fluid inclusion analyses, it is revealed that the direction of ore-forming fluid migration and the ore-forming process are closely related to the thermal cracking of paleo-oil reservoirs. The deposits show a characteristic clustered distribution along the southern part of the Micangshan area, with high-grade mineralization concentrated in the Nanmushu and Kongxigou Pb–Zn deposits. Rb–Sr isotopic dating indicates that mineralization occurred during the Late Cambrian to Early Ordovician (Nanmushu deposit 486.7 ± 3.1 Ma; Kongxigou deposit 472 ± 6.1 Ma), coinciding with the formation of the first-stage paleo-oil reservoirs. The study concludes that the MVT Pb–Zn mineralization in the Micangshan area is genetically linked to the first-stage paleo-oil reservoirs’ hydrocarbon generation and migration events. The organic-rich hydrothermal fluids facilitated the migration and precipitation of Pb–Zn minerals. Full article

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation. Full article

Soil organic carbon (SOC) is a core component of farmland fertility, and its content is significantly influenced by tillage practices. To clarify the effects of alternate tillage on soil organic carbon sequestration and soil aggregate stability, a tillage experiment was initiated in 2017. The study focused on the distribution of soil aggregates across different particle sizes and their organic carbon contents under four tillage treatments: (1) rotary tillage for two consecutive years after initial deep plowing (RT_DP); (2) no-tillage for two consecutive years after initial deep plowing (NT_DP); (3) continuous rotary tillage (RT); and (4) continuous no-tillage (NT). Compared with continuous rotary tillage (RT), RT_DP increased the crop yield by 14.78%, NT decreased the yield by 10.59%, and NT_DP increased the yield by 3.40%. In the topsoil, soil organic carbon (SOC) content increased by 21.57% under RT_DP, 24.47% under NT, and 21.57% under NT_DP. In the subsoil, SOC content increased by 36.91% under RT_DP, 24.80% under NT, and 42.52% under NT_DP. Compared with the RT treatment, practices such as RT_DP increased the SOC content and the proportion of macroaggregates. No significant differences were observed among all treatments in the topsoil. However, in the subsoil, RT_DP significantly increased the SOC content (by 36.91%), SOC content within >0.25 mm aggregates (by 35.75%), and the proportion of >0.25 mm aggregates (by 1.28%), relative to RT. Compared with NT, NT_DP also increased these three indices by 14.2%, 13.38%, and 0.32%, respectively. In the topsoil, the NT_DP treatment resulted in higher mean weight diameter (MWD) stability than the other treatments. In the subsoil, the NT treatment showed the highest MWD and geometric mean diameter (GMD) values, while both RT_DP and NT_DP had significantly higher MWD and GMD than RT. In the deeper soil layer, the NT treatment exhibited the highest aggregate stability. Further analysis indicated that the positive effects of alternate tillage (NT_DP and RT_DP) on aggregate distribution, aggregate stability, and subsoil SOC sequestration were mainly due to improvements in the soil’s nutrient availability, bulk density, porosity, and water content. The optimization of these soil properties further enhanced soil enzyme activity and ultimately promoted the stabilization and accumulation of SOC. In conclusion, incorporating deep plowing into rotational tillage can effectively promote SOC accumulation, especially in the subsoil of maize farmland, and enhance the physical protection of SOC. This study provides a practical tillage strategy for increasing the maize yield and enhancing soil organic carbon sequestration. Full article

As a key component of urban green spaces, which provide sustainability-relevant ecosystem services such as carbon sequestration, soils support plant growth and represents an important carbon pool in urban ecosystems. However, surface soil organic carbon (SSOC) in urban green spaces can be highly heterogeneous due to the combined influences of natural conditions and human activities. To quantify national-scale patterns and major correlates of SSOC in China’s urban green spaces, we compiled published surface (0–20 cm) SSOC observations from 154 field studies and synthesized SSOC density and stocks across 224 Chinese cities, providing a nationally comparable assessment at the city scale. Measurements were harmonized to a consistent depth, and a random forest gap-filling approach was used to extend estimates for data-poor cities. The mean SSOC density and total SSOC stock of urban green spaces were 3.22 kg C m⁻² and 57.87 × 10⁹ kg C, respectively, and SSOC density showed no obvious latitudinal gradient across the 224 cities. Variable importance from the random forest analysis indicated that soil physicochemical properties (e.g., bulk density, total nitrogen, and texture) were the strongest predictors of SSOC density, whereas climatic and topographic variables showed comparatively lower importance. This pattern may suggest that anthropogenic modification and management dampen macro climatic signals such as temperature and precipitation at the national scale. Full article

Accurate quantification of aircraft emissions and their uncertainties is essential for well-informed policy-making, air quality management, and the development of sustainable airport strategies. This study addresses uncertainties in aircraft emission estimates implemented for local air pollutants with hourly resolution at six European airports. Publicly available flight-tracking data were used to determine aircraft movements and types, but they typically lack detailed information on aircraft engine models, thus contributing to uncertainties in emission factors. Times-in-mode for take-off, climb-out, and approach modes followed International Civil Aviation Organization (ICAO) recommendations, while taxi times, known to vary between airports, were modeled using statistical distributions derived from Eurocontrol, and the contribution of taxi time to overall uncertainty in emission estimates was investigated. Monte Carlo simulation combined with Sobol sensitivity analysis identified the relative contribution of each uncertainty source. On average, the results indicate an uncertainty of 23% for CO, 34% for HC, 7% for NO_x, and 21% for PM across the airports analyzed. Overall, the proposed methodology introduces a novel framework utilizing publicly available, hourly resolved flight-tracking data with robust uncertainty analysis to estimate airport-level emissions with enhanced reliability, providing crucial information for local air quality assessment and policy development. Full article

Autoantibodies have long been regarded as passive reflections of immune dysregulation in connective tissue diseases (CTDs). Recent advances in systems immunology and molecular pathology have fundamentally redefined them as active molecular fingerprints that delineate distinct disease endophenotypes with predictive power for clinical trajectories and therapeutic responses. Rather than mere epiphenomena, autoantibodies encode precise information about dominant immune pathways, organ tropism, and pathogenic mechanisms. This review synthesizes emerging evidence that autoantibody repertoires—defined by specificity, structural properties, and functional characteristics—stratify patients beyond traditional clinical taxonomy into discrete pathobiological subsets. Specific signatures such as anti-MDA5 in rapidly progressive interstitial lung disease, anti-RNA polymerase III in scleroderma renal crisis, and anti-Ro52/TRIM21 in systemic overlap syndromes illustrate how serological profiles predict outcomes with remarkable precision. Mechanistically, autoantibody pathogenicity is modulated by immunoglobulin isotype distribution, Fc glycosylation patterns, and tissue-specific receptor expression—variables that determine whether an antibody functions as a biomarker or pathogenic effector. The structural heterogeneity of autoantibodies, shaped by cytokine microenvironments and B-cell subset imprinting, creates a dynamic continuum between pro-inflammatory and regulatory states. The integration of serological, transcriptomic, and imaging data establishes a precision medicine framework: autoantibodies function simultaneously as disease classifiers and therapeutic guides. This endophenotype-driven approach is already influencing trial design and patient stratification in systemic lupus erythematosus, systemic sclerosis, and inflammatory myopathies, and is reshaping both clinical practice and scientific taxonomy in CTDs. Recognizing autoantibodies as endophenotypic determinants aligns disease classification with pathogenic mechanism and supports the transition towards immunologically informed therapeutic strategies. Full article

This study evaluated powdered activated carbon (PAC) pre-coating as a pretreatment strategy to enhance dissolved organic matter (DOM) removal and control fouling during microfiltration of surface water. Two PAC types (one is coal-based and the other is wood-based), divided into three different particle size ranges (22–44, 44–63, 63–88 μm) using sieves and coating weights ranging from 0.6 to 1.2 and 2.4 mg/cm², were systematically compared. Coating PAC improved the quality of water after filtration and stabilized filtration flux, with smaller PAC particle size ranges exhibiting higher DOM removal efficiencies, achieving maximum removals of approximately 30–35% for DOC and over 50% for UV260 at the highest coating weight, whereas uncoated membranes showed negligible DOM removal. The resulting PAC layer on the membrane increased filtration resistance. Fluorescence EEM and Mw distribution results showed that aromatic and high molecular weight DOM was preferentially adsorbed by PAC before reaching the membrane surface; therefore, their contribution to membrane fouling could be reduced. SEM observations showed differences in the images of deposits formed on the PAC layer. These results indicate that the PAC layer acted as a protective interception zone that reduced direct contact between DOM and the membrane surface, thereby contributing to improved flux stability. The coating effect varied with the weight, type and size range of PAC, highlighting the importance of PAC selection. The findings of this study could contribute to more efficient and sustainable urban water supply system operation and management through water quality improvement and process configuration. Full article

Horse flies (Tabanidae) negatively affect livestock by reducing productivity, compromising animal welfare, and serving as mechanical vectors of pathogens. However, the spatial processes shaping their community organization in southern Brazil’s Coastal Plain of Rio Grande do Sul (CPRS) remain poorly understood. To address this, we conducted standardized Malaise-trap surveys and combined them with historical–contemporary comparisons to examine distance–decay patterns in community composition. We evaluated both abundance-based (Bray–Curtis) and presence–absence (Jaccard) dissimilarities using candidate models. Across sites, Tabanus triangulum emerged as the dominant species. Dissimilarity in community structure increased monotonically with geographic distance, with no evidence of abrupt thresholds. The square-root model provided the best fit for abundance-based data, whereas a linear model best described presence–absence patterns, reflecting dispersal limitation and environmental filtering across a heterogeneous coastal landscape. Sites within riparian forests and conservation units displayed higher diversity, emphasizing the ecological role of protected habitats and the importance of maintaining connected corridors. Collectively, these findings establish a process-based framework for surveillance and landscape management strategies to mitigate vector, host contact. Future directions include integrating remote sensing and host distribution, applying predictive validation across temporal scales. Full article

The moldic pore-vuggy reservoirs of the Ma5₄-Ma5₁ sub-member in the Majiagou Formation, central Ordos Basin, are key targets for deep natural gas exploration, yet the alteration mechanisms and controlling factors of burial-stage pressure-released water karstification remain unclear. Herein, an integrated methodology encompassing core observation, thin-section analysis, and geochemical testing was adopted to systematically clarify the development characteristics and multi-factor coupling control mechanisms of this karst process. Results show that burial-stage pressure-released water karst is dominated by overprinting on pre-existing syndepositional and supergene pore networks, forming complex reservoir spaces via synergistic selective dissolution. The development of preferential dissolution zones is jointly controlled by differential compaction of the weathering crust, permeability heterogeneity of the overlying strata and weathered crust, and diagenetic fluid properties. After the supergene diagenetic stage, differential tectonic deformation and burial compaction induced overpressure in pore fluids, which drove acidic pressure-released water to migrate along high-permeability pathways such as the “sandstone windows” overlying the Ordovician weathering crust. These fluids preferentially dissolved high-permeability moldic pore-vuggy dolomites in paleo-karst platforms and steep slope zones, whereas tight micritic dolomites served as effective barriers. The acidic environment sustained by organic acids and H₂S in pressure-released water promoted carbonate dissolution, and carbon-oxygen isotopes as well as pyrite δ³⁴S values verify that the fluids were derived from mudstone compaction. This study reveals that the distribution of high-quality reservoirs is jointly determined by the synergistic preservation of moldic pore-vuggy systems in paleo-karst platforms and steep slopes and directional alteration of pressure-released water along preferential pathways, providing crucial geological guidance for the evaluation of deep carbonate reservoirs. Full article

As distributed generators (DGs) and flexible adjustable loads (FALs) further penetrate distribution networks (DNs), to reduce regulation complexity compared with traditional centralized control frameworks, DGs and FALs in DNs should be packed in several clusters to enable their dispatch to become standard in the industry. To mitigate the negative influence of DGs’ and FALs’ spatiotemporal distribution and uncertain output characteristics on dispatch, this paper proposes an intelligent dynamic cluster partitioning strategy for DNs, from which the DN’s resources and loads can be intelligently aggregated, organized, and regulated in a dynamic and optimal way with relatively high implementation efficiency. An environmental model based on the Markov decision process (MDP) technique is first developed for DN cluster partitioning, in which a continuous state space, a discrete action space, and a dispatching performance-oriented reward are designed. Then, a novel random forest Q-learning network (RF-QN) is developed to implement dynamic cluster partitioning by interacting with the proposed environmental model, from which the generalization and robust capability to estimate the Q-function can be improved by taking advantage of combining deep learning and decision trees. Finally, a modified IEEE-33-node system is adopted to verify the effectiveness of the proposed intelligent dynamic cluster partitioning and regulation strategy; the results also indicate that the proposed RF-QN is superior to the traditional deep Q-learning (DQN) model in terms of renewable energy accommodation rate, training efficiency, and portioning and regulation performance. Full article