Search Results (3,617)

Rice is a staple grain crop central to China’s food security. As the core production region of single-cropping rice, the middle and lower reaches of the Yangtze River face escalating high daytime and nighttime temperatures and compound drought–heat stress amid global warming. The accurate assessment of heat hazards is therefore pivotal for regional yield stability and disaster mitigation. Based on meteorological, remote-sensing, and soil data, together with county-level rice yield statistics from 150 major producing counties spanning 1991 to 2024 (5009 county-year calibration units), we first constructed a composite heat damage index (CHI) by integrating daytime harmful accumulated temperature (Ha), nighttime harmful accumulated temperature (HNa), and the Vegetation Health Index (VHI). We then implemented a gradient boosting decision tree (GBDT) machine learning framework in which yield loss was imposed as a physical constraint. This framework was benchmarked against convolutional neural network (CNN), random forest (RF), and support vector machine (SVM) models, with the Shapley additive explanations (SHAP) method used for attribution analysis and an independent temporal partitioning strategy applied for model validation. The results indicate the following: (1) compared to the single daytime heat damage index, the CHI elevated the yield correlation coefficient from 0.52 to 0.63; (2) with yield constraint calibration, the model attained a balanced accuracy of 92.6% and 94.0% consistency with historical disaster records; (3) regional heat hazard presents a spatial pattern of “high in inland areas and low in coastal areas,” with the heading–flowering stage as the critical sensitive period; and (4) high nighttime temperature accounts for approximately 20% of the model’s relative importance, with higher discriminative sensitivity for high-grade hazards, while the amplifying effect of water deficit on heat stress maintains a stable relative importance of around 16%. In this study, the coupled optimization of traditional assessment paradigms and data-driven approaches is achieved, providing a methodological reference for refined growth stage–specific heat hazard assessment. Its cross-regional portability and independent predictive validity require further validation. Full article

Soil organic carbon (SOC) in mountain forest ecosystems exerts critical controls over regional carbon balance and climate feedback loops. This study collected 209 stratified topsoil (0–30 cm) samples across temperate mountain forests of Northeast China, conducted a boosted regression tree (BRT) modeling framework integrated with space-for-time substitution, and established 15 combined thermopluviometric sensitivity scenarios to simulate SOC shifts under diversified climate disturbances. Tenfold cross-validation yielded a model mean R² of 0.62, revealing mean annual temperature (MAT, RI = 35.31%) as the most influential predictor of SOC spatial variation, followed by elevation (ELE, RI = 19.11%), while single-season NDVI and soil particle fractions showed weak predictive capacity. Multi-scenario spatial simulation outputs demonstrated that simultaneous warming and aridification drastically reduce the coverage of high SOC zones, whereas increased precipitation can partially offset temperature-induced carbon mineralization losses. Terrain-mediated SOC spatial stratification remained stable across all climate backgrounds. This study quantifies the layered environmental association hierarchy of mountain SOC and generates spatially explicit modeled carbon sink projections under climate change. The terrain-dependent SOC response patterns provide operable differentiated carbon regulation guidance: humid low-lying convergence zones require long-term soil moisture conservation, while arid steep ridges need native mixed forest restoration to lift baseline carbon storage capacity, supporting precise watershed climate adaptation and targeted forest carbon sink management for temperate mountain regions. Full article

Operational risk management (ORM) in financial institutions has traditionally relied on quantitative loss event databases that capture what went wrong, but rarely why from a human factors perspective. This paper proposes a comprehensive Human Factors Framework for ORM that integrates the Human Factors Analysis and Classification System (HFACS) taxonomy with deep learning and large language models (LLMs) to extract, classify, and predict human-factor-driven operational risks from unstructured consumer complaint narratives. Using the U.S. Consumer Financial Protection Bureau (CFPB) Consumer Complaints Database (300,000 banking narratives), we (1) define and validate an eight-factor HFACS-Banking taxonomy through a Delphi study with five domain experts (Cohen’s κ = 0.86), (2) compare three extraction approaches—regex, supervised BERT, and Mistral-7B zero-shot LLM—achieving 0.81 average F1 with the LLM-distilled BERT relative to Mistral-generated reference labels, (3) propose a hybrid HF-BERT-BiLSTM-Attention architecture that fuses contextual text embeddings with structured features and HFACS factor probabilities for predicting a four-class company-response-based complaint severity proxy, and (4) provide explainability through SHAP feature attribution applied to the Random Forest baseline and attention-weight visualization of the proposed neural model. Under a temporal split comprising training data from 2014 to 2022, validation data from 2023H1, and held-out test data from 2023H2 to 2025, the proposed model achieves 91.42% accuracy and 89.78% macro F1 (95% CI from 1000-iteration paired bootstrap: [89.34, 90.21]), outperforming Random Forest (+9.84% F1, p < 0.001 Bonferroni-corrected), BiLSTM (+5.46%, p < 0.001), FinBERT (+3.21%, p = 0.003), and BERT-only (+3.92%, p = 0.002) baselines. Under the secondary random-split ablation analysis, removing the HFACS features and replacing additive attention with mean pooling reduced macro-F1 by 3.79 and 2.35 points, respectively. The findings demonstrate the retrospective feasibility of integrating theory-grounded human-factor representations with neural language models for complaint-outcome analysis; prospective institutional validation is required before operational use. Full article

Loss of soil organic resources after forest conversion is often assumed to constrain microbial functioning; however, resource status and functional potential may respond differently to land-use legacies. We compared soils under natural secondary forest, six-year poultry-integrated agroforestry, and 60-year paddy cultivation derived from the same regional forest type. Soil properties, microbial biomass, hydrolyzable organic N fractions, extracellular enzyme activities, nitrogen (N)-cycling genes, and potential N₂O emissions were measured using three independent field replicates per land use. To quantify the alignment between resources and microbial functioning, we separately standardized indicators of retained organic resources (soil organic carbon (SOC), total N, microbial biomass C, and four hydrolyzable organic N fractions) and microbial functional potential (four extracellular enzymes and five N-cycling genes). We then calculated their difference as a study-specific organic resource–microbial activation imbalance index (ORMAI), for which positive values indicate high functional potential relative to retained resources. Relative to the forest, SOC declined by 29.5% under agroforestry and 61.1% under paddy cultivation, while total N declined by 16.6% and 59.7%, respectively. Paddy soil also contained the lowest concentrations of all hydrolyzable organic N fractions, with reductions of 28.3–81.5% relative to the forest. Despite this depleted resource status, paddy soil exhibited the highest activities of all measured C-, N-, and phosphorus-acquiring enzymes and the greatest abundances of AOA-amoA, AOB-amoA, nirK, nirS, and nosZ. It also had a higher (nirK + nirS)/nosZ ratio than the other soils. Paddy soil produced 3.91 and 4.69 times as much cumulative N₂O as forest and agroforestry soils, respectively. The resulting ORMAI was strongly positive in paddy soil (2.34) but negative in the forest and agroforestry soils (approximately −1.17). The three systems therefore exhibited distinct configurations: high resource status with intermediate functional potential in the forest, partial resource retention with restrained functional activation under agroforestry, and depleted resource status coupled with high functional and emission potentials in paddy soil. These findings identify resource–function imbalance as an informative dimension of land-use legacy, revealing that soil organic-resource depletion can coincide with enhanced microbial functional and N₂O emission potentials rather than constraining them. These results highlight the importance of considering microbial functional potential alongside soil organic-resource status when assessing potential N₂O emission risks and developing land-management strategies following tropical forest conversion. Full article

The sustainable restoration of tropical dry forest (TDF)—one of the planet’s most threatened ecosystems—is constrained by low community integration and limited cultural relevance, particularly in Afro-descendant territories, where the erosion of oral tradition and forest degradation reinforce one another. This study proposes a quantitative framework to systematize the biocultural memory embedded in oral tradition as an input for socially grounded, sustainable TDF restoration. A corpus of 401 works from the Afro-descendant community of the Patía Valley (Cauca, Colombia) was coded in a multidimensional database of 10 categories, and three indices were computed: the Biocultural Density Index (IDBC), the Biocultural Vulnerability Index (IVB), and an adaptation of Winter’s flora framework (IVBw), in a total version and a version restricted to wild/native species. The framework identifies the works of greatest biocultural density (IDBC max. = 84) and separates cultivated/introduced species of high cultural value (Limón, Yuca, Caña de azúcar), ones relevant to food sovereignty, and from wild/native species (e.g., Caña brava, Cañafístula, Guayacán, Ceiba) that constitute the restorable core. Cultural practices are the hubs of the system, traditional medicine is the most at risk of loss, and food security concentrates 43% of the flora records. These replicable tools link Afro-descendant knowledge to measurable, monitorable restoration priorities, advancing sustainable land management, biodiversity conservation and cultural sustainability. Full article

Reliable forest biomass and carbon-stock assessment is essential for climate-change mitigation and sustainable forest management, particularly in semi-arid Mediterranean mountain forests affected by drought, wildfire and tree decline. Here, we estimated above-ground biomass (AGB), total biomass, carbon stock and CO₂-equivalent storage in the Ouled Yagoub Forest, northeastern Algeria, by combining field inventory data, species-specific allometric equations, Sentinel-2 vegetation indices and elevation. We established 60 circular plots of 0.1 ha in April 2026 and retained 54 stocked plots for biomass modelling. The field inventory included 661 trees: 467 Pinus halepensis and 194 Cedrus atlantica, including 122 healthy and 72 declining cedar individuals. Field estimates indicated an AGB of 338.07 Mg, equivalent to 62.61 Mg ha⁻¹ for stocked plots. Total biomass reached 436.11 Mg, while carbon stock and CO₂-equivalent storage reached 238.56 Mg C and 874.78 Mg CO₂eq, respectively. The NDVI-based model explained 78.3% of AGB variability, with Pearson’s r = 0.885, RMSE = 48.77 Mg ha⁻¹ and MAE = 39.14 Mg ha⁻¹. Cross-validation confirmed acceptable model stability, with LOOCV R² = 0.755 and RMSE = 51.86 Mg ha⁻¹, and 5-fold CV R² = 0.762 and RMSE = 51.07 Mg ha⁻¹. SAVI produced identical predictive performance because it was perfectly collinear with NDVI. Therefore, we selected the NDVI-only model as the most parsimonious model for spatial prediction. Declining Cedrus atlantica individuals showed lower estimated biomass and carbon storage than healthy cedar individuals, although this difference represents an observed pattern rather than a direct causal quantification of carbon loss due to dieback. The resulting maps provide field-calibrated exploratory spatial estimates rather than independently validated carbon maps. The research directly aligns with the principles of Sustainability, contributing to climate change mitigation and resilient forest management in semi-arid Mediterranean ecosystems. In addition, this work provides a stocked-plot carbon baseline and supports forest carbon monitoring, cedar conservation and restoration planning in the Aurès Mountains. Full article

Afforestation can contribute to climate-change mitigation when tree establishment is matched to ecological context and sustained by long-term management, but its net climatic effect is not uniformly cooling. Existing syntheses often emphasize carbon sequestration while treating biophysical, hydrological, disturbance, and socioeconomic evidence separately. Here, we provide a structured integrative review. We distinguish afforestation from reforestation, natural regeneration, forest restoration, and improved management. We explicitly assess evidence from global modeling, remote sensing, meta-analyses, long-term observations, and regional case studies. Global forests cover about 4.14 billion ha in 2025, while annual net forest loss remained about 4.12 million ha yr⁻¹ during 2015–2025. Global forests were a sink of about 3.5 ± 0.4 Pg C yr⁻¹ in the 2010s, but this existing-forest sink should not be interpreted as an afforestation-specific removal rate. Humid tropical and subtropical regions generally have the greatest potential for net climatic cooling. In contrast, afforestation at snow-covered high latitudes may cause substantial albedo-driven warming, while water-limited regions require careful species selection and conservative planting densities. Soil carbon gains are most consistent on former croplands and other low-carbon degraded lands, but responses on carbon-rich grasslands are highly variable. Long-term benefits further depend on disturbance resilience, permanence, land competition, financing, and credible monitoring. Additionally, we identify five priorities for the future: climate-smart adaptive silviculture, digital forestry with field-calibrated uncertainty, permanence and disturbance-risk accounting, sustainable forest bioeconomy, and integrated international governance and finance. Full article

Late blight in tomato caused by Phytophthora infestans can lead to severe economic losses. Early detection is essential for effective disease management. This study investigates the spectral characteristics of healthy and late blight-infected tomato leaves and plants using non-destructive hyperspectral imaging and proposes a cost-effective machine vision system for early disease detection. Hyperspectral images from seven batches of leaf sets and six batches of whole plant sets were taken hourly over a 96 h period under both controlled and artificially infected conditions. The hyperspectral data cubes were processed with an image analysis model that identified healthy vs. infected regions. Key wavelengths (77 from leaf datasets and 24 from plant datasets) were selected using recursive feature elimination and analysed with four machine learning classifiers: k-nearest neighbour, support vector machine, random forest, and artificial neural network. The models differentiated healthy and infected tissue with high accuracy (98–99%). The hyperspectral data were simplified into a multichannel image with most informative wavelengths, using a custom spectral index and binary decision rule. Experimental limitations were addressed, and a conceptual design of practical hardware was proposed: a monochrome camera combined with a multichannel light source and polariser mounted on mobile equipment. Although further trials will be needed, this proof-of-concept study and conceptual hardware design can be adapted in other crops facing similar disease challenges. Full article

Forests reduce water-driven soil erosion, yet their protective contribution has not been assessed consistently across the European Union (EU) in a framework that separates per-area service intensity from aggregate service flow. We quantified erosion regulation service (ERS) as RUSLE-based avoided sheet and rill erosion: the difference between structural soil-loss potential (C = P = 1) and loss under forest conditions. Spatially aligned European RUSLE factors were combined with the CORINE Land Cover 2018 forest mask and summarised by country, biogeographical region and elevation. Across 134.10 Mha, ERS totalled 6806.15 Mt yr⁻¹ (mean 50.75; median 12.36 t ha⁻¹ yr⁻¹), revealing strong spatial concentration. Slovenia had the highest mean intensity (257.45 t ha⁻¹ yr⁻¹) across 1.13 Mha of mapped forest, whereas Italy provided the largest national total (1437.05 Mt yr⁻¹) across 7.83 Mha of mapped forest. Alpine and Mediterranean forests supplied 64.0% of total ERS in the 27 EU Member States (EU27) while occupying 27.7% of mapped forest area. Mean intensity increased from 9.70 t ha⁻¹ yr⁻¹ below 200 m to 248.53 t ha⁻¹ yr⁻¹ at ≥2000 m, but total service peaked at 500–1000 m. This intensity–area trade-off distinguishes priority locations from major national contributions and provides a spatially consistent baseline for multifunctional forest management, soil protection and ecosystem restoration. ERS represents modelled avoided hillslope erosion, not sediment yield or a deforestation scenario. Full article

Accidents between vehicles and wildlife (WVCs) represent a complex management challenge, requiring integrated strategies that balance biodiversity conservation with public security and socio-ecological resilience. However, existing GIS hotspot analyses often identify spatial patterns without quantifying the structural landscape drivers that compel animal–road interactions. This study aims to identify “ecological traps” through an integrated landscape diagnostic framework combining Kernel Density Estimation (KDE) for statistical hotspot identification and landscape metrics (FRAGSTATS) for structural diagnosis, using the wild boar (Sus scrofa) as a focal species. An exploratory case analysis of high-collision locations was conducted, utilizing a high-quality dataset of 161 precisely georeferenced incidents recorded between 2015 and 2020 within the most critical municipalities of the Province of Avellino (Southern Italy). Results highlight two primary hotspots: the Guardia Lombardi-Conza corridor and the Avellino Nord-Pratola Serra axis. Quantitative analysis reveals that 39.1% of incidents occurred in non-irrigated arable lands and 19.9% in broad-leaved forests, with 52.8% of events situated within 500 m of river systems, which function as primary ecological movement corridors. Furthermore, fragmentation indices (Patch Density, Edge Density) were significantly higher in these focus areas, confirming that habitat isolation and the loss of core patches force animals to traverse infrastructure. These findings underscore the urgency of evidence-based spatial planning, offering a methodological framework with potential applicability for prioritizing mitigation actions, such as ecological corridors and intelligent signaling, to enhance the resilience of socio-ecological systems. This framework provides a scalable model for sustainable land management, ensuring that biodiversity conservation is integrated into long-term infrastructure governance. Full article

Periodic manual oversight is difficult to scale for large portfolios of funded research projects. Progress delays, budget irregularities, and superficial reporting often go undetected until final acceptance. Many conventional detection methods also generate false positives when contextually supported schedule adjustments resemble anomalous patterns. We present the Intelligent Project Monitoring System (IPMS), which couples a feature-decoupled multi-task LSTM-Attention network with an Adaptive Isolation Forest. The LSTM-Attention component models project workflows through finite state machines and predicts milestone deviations. The Adaptive Isolation Forest then flags records after a context gate screens cases meeting the study’s legacy legitimate-deviation criteria before final alerting. A multi-head attention module tracks how execution performance evolves over the project lifecycle, and the system includes a loss-ratio signal for candidate-shift review and feedback-gated controlled recalibration; its response was evaluated only under one researcher-designed synthetic global policy-change injection. On a real-world dataset from a provincial management platform, in which approximately 7% of legacy-labeled records carried an anomalous reference label, IPMS achieved an AUC of 0.924 and a false-positive rate of 4.2% against the available legacy binary reference labels, a 76.9% relative reduction in observed FPR compared with standard Isolation Forest. Milestone deviation prediction reached an MAE of 1.85 days, 34.2% lower than standard LSTM. Execution profiling achieved an MAE of 0.082. Removing the deviation filter alone degraded F1 by 12.3%, and removing multi-scale fusion increased the miss rate for long-duration stalls by 23%. Full article

Football match outcomes are influenced by a complex interplay of dynamic team strategies and stochastic match events, posing a significant challenge for predictive analytics. In this paper, we present a unified benchmarking study for football match outcome classification, using a dataset of 225,474 matches spanning 2018–2026 and a strictly chronological train–test split. Six classifiers spanning several modelling approaches, Logistic Regression, Generalized Additive Models, FastTree, Random Forest, Radial Basis Function networks and Multi-Layer Perceptron, are trained and evaluated under identical conditions. Bookmaker odds are converted into margin-free probabilities by the power method, with the exponent found by the Newton–Raphson method, and are included among the input features. In addition to the six classifiers, we also report a majority-class baseline and the de-vigged bookmaker prediction itself. Results are reported for both a binary one-vs-rest formulation and the original three-class (1, X, 2) formulation, with 95% bootstrap CIs for every metric and every model. Across the six classifiers, the differences are small: macro-averaged Precision ranges from 64.83% to 65.16% for Home Win and from 68.05% to 68.46% for Away Win, and macro-averaged Recall from 63.16% to 63.51% and from 59.10% to 59.72%, respectively, with substantially overlapping confidence intervals. In the three-class formulation, models trained without market data reach 48.0% accuracy against 43.4% for a trivial baseline and 51.9% for the bookmaker. Models trained with market data match the bookmaker but do not improve upon it on log-loss, Brier score or the ranked probability score. The only statistically distinguishable improvement of any kind is a 0.086 point accuracy advantage for the additive model, which is not accompanied by any improvement in the proper scoring rules and therefore does not indicate a practical advantage. Probability calibration and betting-signal generation are outside the scope of this study. Full article

The Loess Plateau in China is highly susceptible to frequent landslides and other geological disasters, which have led to substantial losses of natural and human resources and are frequently reported in the news media. Yuzhong County, located east of Lanzhou City, is a mountainous region with considerable development potential. On 7 August 2025, this area experienced a large-scale geological disaster characterized by a compound event involving both landslides and debris flows, resulting in nearly several hundred casualties. With the ongoing urban expansion of Yuzhong County in recent years, the prediction and prevention of geological disasters have become increasingly critical. This study employed three machine learning algorithms—Multiple Logistic Regression (LR), Random Forest (RF), and XGBoost (XG)—to assess landslide susceptibility in Yuzhong County. A total of 169 historical landslide points, supplemented by additional sites identified through field investigations, were compiled, along with 200 non-landslide locations. Multiple environmental factors were incorporated into the models to analyze landslide susceptibility across different areas. Because LR can effectively capture the generalized influence of precipitation variability, it was selected as the primary model for the final susceptibility mapping. To more accurately evaluate the impact of precipitation on landslide occurrence, average seasonal precipitation across the four seasons was used as a predictive factor. To refine the risk assessment at the township level, both raster-based and landslide-unit-based evaluation approaches were adopted. Overlay analyses were then performed by integrating urban infrastructure, population distribution, and predicted landslide hazard zones, while also accounting for the potential influence of extreme precipitation events. The results reveal that the mountainous areas in eastern Mapo Township, southern Xiaokangying Township, southern Xiaguanying Town, and the south-central part of Qingshuiyi Township are high-risk zones prone to group-occurrence landslide disasters. Full article

Urbanization and agricultural expansion have led to widespread habitat loss and fragmentation, profoundly altering avian community structure. To understand how different habitat types shape bird diversity and assembly rules, we investigated bird communities across four major habitats (farmland, forest, urban, and wetland) in Henan province. Our results showed that wetland habitats supported the highest species richness, while forest habitats exhibited the highest Shannon–Wiener diversity and evenness. Rarefaction curves indicated that wetland and forest communities harbored the greatest species diversity even with increased sampling effort, while farmland communities showed early saturation. Venn diagram analysis revealed that 30.0% of species were unique to wetlands, 25.0% to forests, 8.3% to urban areas, and only 5.0% to farmland, with limited species overlap across habitats. Rank-abundance distribution (RAD) models varied significantly by habitat: the lognormal distribution best described farmland communities, preemption fit forest and wetland communities, and the Mandelbrot distribution best fit urban communities. Indicator species analysis identified Spilopelia chinensis as a significant indicator for farmland habitats (Indicator Value = 0.938, p < 0.01), reflecting its strong association with open agricultural landscapes. NMDS ordination (Stress = 0.152) showed partial overlap in community composition across habitats, while redundancy analysis (RDA) revealed that longitude (15.3% variance explained), latitude, altitude, and temperature collectively shaped bird community structure. These findings highlight that habitat type is a key driver of avian diversity and community assembly, with forests and wetlands serving as critical refuges for diverse bird communities. Urban and farmland habitats, while supporting lower diversity, host specialized species adapted to human-altered conditions. Our study emphasizes the need for multi-habitat conservation strategies to maintain avian biodiversity in fragmented landscapes. Full article

Soil organic carbon (SOC) stocks are strongly influenced by land use, vegetation condition and climate, particularly in heterogeneous mountain catchments. This study quantified SOC stocks and simulated long-term SOC dynamics in the Çapakçur micro-catchment, eastern Türkiye, by integrating field assessment, geostatistical prediction, uncertainty analysis, inverse RothC calibration and scenario modelling. A total of 428 soil samples were collected from the 0–30 cm layer across forest, degraded forest, and pasture areas. SOC stocks were calculated from SOC concentration, bulk density and soil depth, and spatially predicted using ordinary kriging of log-transformed SOC stocks. RothC was calibrated for each land-use class to estimate the annual carbon inputs required to maintain observed SOC stocks, followed by 50-year restoration and climate-sensitivity simulations. SOC stocks ranged from 7.69 to 247.68 Mg C ha⁻¹, averaging 55.52 Mg C ha⁻¹. Forest had the highest mean SOC stock (78.5 Mg C ha⁻¹), followed by pasture (55.9) and degraded forest (50.2 Mg C ha⁻¹). Required annual carbon inputs were 5.17, 4.58 and 3.29 Mg C ha⁻¹ yr⁻¹, respectively. Increasing degraded forest carbon inputs to forest-equivalent levels increased SOC by 14.76 Mg C ha⁻¹ over 50 years, equivalent to 37.77 Gg C or 138.49 Gg CO₂eq at the catchment scale. A stronger restoration scenario increased this potential to 63.77 Gg C. Warming caused SOC losses, with +2 °C reducing catchment SOC by 56.78 Gg C. These findings demonstrate the potential of degraded forest restoration for SOC sequestration while highlighting the vulnerability of long-term SOC gains to climate warming. Full article