Search Results (2,831)

With the intensification of global climate change, frequent extreme temperature events pose increasing challenges to agricultural production. The aim of this study is to characterize the spatiotemporal evolution of county-level agricultural total factor productivity (ATFP) under extreme temperature events, reveal key driving factors and crop-specific heterogeneity, and predict potential high-risk areas, which is crucial for providing scientific basis for risk management and adaptive policy formulation in globally climate-sensitive agricultural regions. This paper selects Jiangsu Province as a typical case study, uses the DEA-Malmquist model to measure agricultural total factor productivity (ATFP), systematically analyzes the spatiotemporal dynamic evolution characteristics of ATFP at the county scale, and selects the random forest and XGBoost ensemble models with optimal accuracy through model comparison for prediction, assessing the evolution trends of ATFP under different climate scenarios. The results showed that: (1) From 1993 to 2022, the average ATFP increased from 0.7460 to 1.1063 in the province, though development showed uneven distribution across counties, exhibiting a “high in the south, low in the north” gradient pattern. (2) Mechanization, agricultural film and land inputs are the core elements driving the overall ATFP increase but there are obvious crop differences: mechanization has a more prominent role in promoting the productivity of wheat and maize, while labor inputs have a greater impact on the ATFP of rice. (3) The negative impacts of extreme climate events on agricultural production will be significantly amplified under high-emission scenarios, while moderate climate change may have a promotional effect on certain crops in some regions. Full article

Exposure to chemical irritants in laboratory and medical environments poses significant health risks to workers, particularly in relation to asthma-like symptoms. Routine cleaning practices, which often involve the use of strong chemical agents to maintain hygienic settings, have been shown to contribute to respiratory issues. Laboratories, where chemicals such as hydrochloric acid and ammonia are frequently used, represent an underexplored context in the study of occupational asthma. While much of the research on chemical exposure has focused on industrial and high-risk occupations or large cohort populations, less attention has been given to the risks in laboratory and medical environments, particularly for professional cleaning staff. Given the growing reliance on cleaning agents to maintain sterile and safe workspaces in scientific research and healthcare facilities, this gap is concerning. This study developed an exploratory simulation tool, using a simulated cohort based on key demographic and exposure patterns from foundational research, to assess the impact of chemical exposure from cleaning products in laboratory environments. Four supervised machine learning models were applied to evaluate the relationship between chemical exposures and asthma-like symptoms: (1) Decision Trees, (2) Random Forest, (3) Gradient Boosting, and (4) XGBoost. High exposures to hydrochloric acid and ammonia were found to be significantly associated with asthma-like symptoms, and workplace type also played a critical role in determining asthma risk. This research provides a data-driven framework for assessing and predicting asthma-like symptoms in professional cleaning workers exposed to cleaning agents and highlights the potential for integrating predictive modeling into occupational health and safety monitoring. Future work should explore dose–response relationships and the temporal dynamics of chemical exposure to further refine these models and improve understanding of long-term health risks. Full article

The Three Gorges Reservoir Area (TGRA) faces mounting pressures from urbanization and hydrological regulation, threatening the sustainability of its ecosystem services (ESs). The InVEST model, coupled with optimal parameter geographical detector (OPGD) and geographically and temporally weighted regression (GTWR), was employed to assess spatiotemporal changes, trade-offs/synergies, and driving mechanisms of four ESs, water yield (WY), habitat quality (HQ), carbon storage (CS), and soil conservation (SC), from 2000 to 2020. Results revealed that WY and SC increased significantly by 24.54% and 5.75%, respectively, while HQ declined by 3.02% and CS remained relatively stable, with high-value ES zones mainly concentrated in the eastern and northern forest-dominated areas. Regarding interactions, strong synergies existed among HQ, CS, and SC, whereas WY exhibited persistent trade-offs with other services, particularly in the central agricultural-urban transitional zone. Furthermore, landscape diversity increased linearly, driven by forest expansion and urban growth. Mechanistically, land use type (LUT) dominated the spatial distribution of WY, HQ, and CS, while slope primarily controlled SC patterns, with all driver interactions demonstrating enhanced effects. By coupling OPGD with GTWR, this study uniquely elucidates the spatiotemporal instability of ES trade-offs/synergies and the spatial heterogeneity of their driving mechanisms, providing a novel scientific basis for implementing spatially differentiated management strategies in large-scale reservoir-impacted regions. Full article

Accurate forecasting of meteorological parameters is essential for the reliable operation and performance optimization of photovoltaic (PV) power plants. Among these parameters, ambient temperature and global horizontal irradiance (GHI) have the most direct impact on PV output. This study investigates the integration of machine-learning-based (ML) weather forecasts into PV energy modeling and quantifies how forecast accuracy propagates into PV generation estimation errors. Three commonly used ML algorithms—Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF)—were developed and compared. Antalya (Turkey), representing a Mediterranean climate zone, was selected as the case study location. High-resolution meteorological data from 2018–2023 were used to train and evaluate the forecasting models for prediction horizons from 1 to 10 days. Model performance was assessed using root mean square error (RMSE) and the coefficient of determination (R²). The results indicate that RF provides the highest accuracy for temperature prediction, while ANN demonstrates superior performance for GHI forecasting. The generated forecasts were incorporated into a PV power output simulation using the PVLib library. The analysis reveals that inaccuracies in GHI forecasts have the largest impact on PV energy estimation, whereas temperature forecast errors contribute significantly less. Overall, the study demonstrates the practical benefits of integrating ML-based meteorological forecasting with PV performance modeling and provides guidance on selecting suitable forecasting techniques for renewable energy system planning and optimization. Full article

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LST_max) and its diurnal range (LST_{max_min}), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments. Full article

During the safety planning stage, accurate crash prediction tools are critical for prioritizing countermeasures and allocating resources effectively. Traditional statistical approaches, while long applied in this field, often depend on distributional assumptions that may introduce bias and limit model accuracy. To address these issues, studies have started exploring Machine Learning (ML)-based techniques for crash prediction, particularly for higher functional class roads. However, the application of ML models on two-lane highways remains relatively limited. This study aims to develop an approach to integrate traffic, geometric, and critically, speed-based factors in crash prediction using Random Forest (RF) and SHapley Additive exPlanations (SHAP) techniques. Comparative analysis shows that the RF model improves crash prediction accuracy by up to 25% over the traditional Zero-Inflated Negative Binomial model. SHAP analysis identified AADT, segment length, and average speed as the three most influential predictors of crash frequency, with speed emerging as a key operational factor alongside traditional exposure measures. The strong influence of speed in the RF–SHAP results depicts its critical role in the safety performance of two-lane highways and highlights the value of incorporating detailed operating characteristics into crash prediction models. Overall, the proposed RF–SHAP framework advances roadway safety assessment by offering both predictive accuracy and interpretability, allowing agencies to identify high-impact factors, prioritize countermeasures, and direct resources more efficiently. In doing so, the approach supports sustainable safety management by enabling evidence-based investments, promoting optimal use of limited transportation funds, and contributing to safer, more resilient mobility systems. Full article

Cross-ecosystem transmission of plant pathogens from crops to natural forests is increasingly recognized as a key factor in disease emergence and biodiversity loss. Agricultural systems serve as major sources of inoculum, with landscape interfaces—such as crop–forest edges, riparian zones, abandoned orchards, and nursery–wildland transitions—acting as active epidemiological gateways. Biological vectors, abiotic dispersal, and human activities collectively enable pathogen movement across these boundaries. Host-range expansion, recombination, and hybridization allow pathogens to infect both cultivated and wild hosts, leading to generalist and recombinant lineages that survive across diverse habitats. In natural ecosystems, such introductions can alter community composition, decrease resilience, and intensify the impacts of climate-driven stress. Advances in molecular diagnostics, genomic surveillance, environmental DNA, and remote sensing–GIS (Geographic Information System) approaches now enable high-resolution detection of pathogen flow across landscapes. Incorporating these tools into interface-focused monitoring frameworks offers a pathway to earlier detection, better risk assessment, and more effective mitigation. A One Health, landscape-based approach that treats agro–wild interfaces as key control points is essential for reducing spillover risk and safeguarding both agricultural productivity and the health of natural forest ecosystems. Full article

Lichens, symbiotic associations between fungi and photobionts, are essential and sensitive bioindicators of environmental change. Despite their resilience, lichens face increasing threats from air pollution, land-use change, unsustainable harvesting, and climate change. This study presents a bibliometric analysis of global research on lichen threats between 1981 and 2024, using data from Scopus and Web of Science, combined with an additional analysis based on the database Recent Literature on Lichens (RLL). A total of 319 research publications were analyzed through VOSviewer (version 1.6.20)and Biblioshiny (R core team version 4.5.2) to assess temporal trends, thematic evolution, authorship, and geographical distribution of affiliations, and 1354 publications from RLL were studied for frequent authors and geographical distribution of study sites. Results show that research output was initially dominated by air pollution studies (1981–2004) but shifted after 2005 toward conservation and climate change impacts, with a sharp increase after 2017. North America and a few European countries led in scientific production, while biodiversity-rich regions in Africa, South America, and Southeast Asia remained underrepresented. Despite increasing publication trends, collaboration remains moderate (23% international co-authorship), and many threatened species remain unassessed. Recovery measures emphasize habitat protection, improved forest management, pollution control, integration of lichens into global biodiversity frameworks, and enhanced international collaboration. This study provides a systematic overview of how lichen conservation research has evolved, suggesting strategies for decelerating lichen diversity loss under accelerating global change. Full article

Metal-composite joints, leveraging the high specific strength/stiffness and superior fatigue resistance of carbon fiber reinforced polymers (CFRP) alongside metallic materials’ excellent toughness and formability, have become prevalent in aerospace structures. Fastener flexibility serves as a critical parameter governing load distribution prediction and fatigue life assessment, where accurate quantification directly impacts structural reliability. Current approaches face limitations: experimental methods require extended testing cycles, numerical simulations exhibit computational inefficiency, and conventional machine learning (ML) models suffer from “black-box” characteristics that obscure mechanical principle alignment, hindering aerospace implementation. This study proposes an integrated framework combining numerical simulation with explainable ML for fastener flexibility analysis. Initially, finite element modeling (FEM) constructs a dataset encompassing geometric features, material properties, and flexibility values. Subsequently, a random forest (RF) prediction model is developed with five-fold cross-validation and residual analysis ensuring accuracy. SHapley Additive exPlanations (SHAP) methodology then quantifies input features’ marginal contributions to flexibility predictions, with results interpreted in conjunction with theoretical flexibility formulas to elucidate key parameter influence mechanisms. The approach achieves 0.99 R2 accuracy and 0.11 s computation time while resolving explainability challenges, identifying fastener diameter-to-plate thickness ratio as the dominant driver with negligible temperature/preload effects, thereby providing a validated efficient solution for aerospace joint optimization. Full article

Background and Objectives: Acute pancreatitis (AP) has a wide range of clinical severity, and early prediction of disease progression is still challenging. Trypsinogen-activating peptide (TAP) and trypsin-2 serve as direct biomarkers for intrapancreatic proteolytic activation and may provide earlier pathophysiological information compared with traditional markers. Materials and Methods: In this retrospective cohort analysis involving 54 AP patients, we evaluated 24 h serum and urinary TAP and trypsin-2 concentrations by Bayesian correlation, mediation analysis, unsupervised K-means clustering, and supervised machine learning (Elastic Net and Random Forest). The analyses investigated the relationships of biomarkers with inflammation (CRP), enzymatic activities (amylase, lipase), and clinical factors, as well as inflammation severity (CRP levels). Results: Bayesian correlations indicated moderate evidence for a relationship between serum TAP and CRP (BF₁₀ = 8.42), as well as strong evidence linking age to serum TAP (BF₁₀ = 12.75). Serum trypsin-2 showed no correlation with CRP, while urinary trypsin-2 had a correlation with amylase (BF₁₀ = 6.89). Mediation analysis indicated that TAP and trypsin-2 accounted for 42–44% of the impact of CRP on pancreatic enzyme elevation. Clustering revealed three phenotypic subgroups (“Mild Activation”, “Moderate System”, and “Severe Pancreatic-Renal”), the latter showing the highest levels of CRP and biomarkers. Machine learning models highlighted urinary trypsin-2 and age as the most significant predictors of inflammation, with Random Forest achieving the highest performance (R² = 0.53). Conclusions: Early urinary trypsin-2 outperforms serum markers as a predictor of systemic inflammatory intensity, indicating total proteolytic impairment and renal clearance. This integrative analysis reveals unique biological phenotypes and highlights the potential of these biomarkers for early assessment of the inflammatory burden. Their role in predicting clinical disease progression requires prospective validation. Integrative biomarker analysis reveals unique biological phenotypes and improves assessment of inflammatory burden in PA. Larger cohorts are required for prospective validation to incorporate these biomarkers into precision-based diagnostic frameworks. Full article

The M_JMA 7.6 (Mw 7.5) Noto Peninsula Earthquake of 1 January 2024 in Japan triggered widespread slope failures across northern Noto region, but their spatial controls and susceptibility patterns remain poorly quantified. Most previous studies have focused mainly on fault rupture, ground deformation, and tsunami impacts, leaving a clear gap in machine learning based assessment of earthquake-induced slope failures. This study integrates 2323 mapped landslides with eleven conditioning factors to develop the first data-driven susceptibility framework for the 2024 event. Spatial analysis shows that 75% of the landslides are smaller than 3220 m² and nearly half occurred within about 23 km of the epicenter, reflecting concentrated ground shaking beyond the rupture zone. Terrain variables such as slope (mean 31.8°), southwest-facing aspects, and elevations of 100–300 m influenced the failure patterns, along with peak ground acceleration values of 0.8–1.1 g and proximity to roads and rivers. Six supervised machine learning models were trained, with Random Forest and Gradient Boosting achieving the highest accuracies (AUC = 0.95 and 0.94, respectively). Explainable AI using SHapley Additive exPlanations (SHAP) identified slope, epicentral distance, and peak ground acceleration as the dominant predictors. The resulting susceptibility maps align well with observed failures and provide an interpretable foundation for post-earthquake hazard assessment and regional risk reduction. Further work should integrate post-seismic rainfall, multi-temporal inventories, and InSAR deformation to support dynamic hazard assessment and improved early warning. Full article

Over the past decade, the frequency and impact of both natural and human-induced disasters have increased significantly, highlighting the urgent need for effective and timely relief operations. Disaster response requires efficient allocation of resources to the right locations and disaster types in a cost- and time-effective manner. However, during such events, large volumes of unverified and rapidly spreading information—especially on social media—often complicate situational awareness and decision-making. Consequently, extracting actionable insights and accurately classifying disaster-related information from social media platforms has become a critical research challenge. Machine Learning (ML) approaches have shown strong potential for categorizing disaster-related tweets, yet substantial variations in model accuracy persist across disaster types and regional contexts, suggesting that universal models may overlook linguistic and cultural nuances. This paper investigates the categorization and sub-categorization of natural disaster tweets using a labeled dataset of over 32,000 samples. Logistic Regression and Random Forest classifiers were trained and evaluated after comprehensive preprocessing to predict disaster categories and sub-categories. Furthermore, a country-specific prediction framework was implemented to assess how regional and cultural variations influence model performance. The results demonstrate strong overall classification accuracy, while revealing marked differences across countries, emphasizing the importance of context-aware, culturally adaptive ML approaches for reliable disaster information management. Full article

Background: Systemic inflammation is an essential factor in the formation of the tumor microenvironment and has an impact on patient response to immune checkpoint inhibitors. Although there is a growing interest in biomarkers of inflammation, there is a gap in understanding their predictive value for response to nivolumab in clinical practice. The objective of this research was to design and assess a multi-algorithmic machine learning (ML) model based on regular systemic inflammation measurements to forecast the response of treatment to nivolumab. Methods: An analysis of a retrospective real-world cohort of 177 nivolumab-treated patients was performed. Baseline inflammatory biomarkers, such as neutrophils, lymphocytes, platelets, CRP, LDH, albumin, and derived indices (NLR, PLR, SII), were derived. After preprocessing, 5 ML models (Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Neural Network) were trained and tested on a 70/30 stratified split. Accuracy, AUC, precision, recall, F1-score, and Brier score were used to evaluate predictive performance. The interpretability of the model was analyzed based on feature-importance ranking and SHAP. Results: Gradient Boosting performed best in terms of discriminative (AUC = 0.816), whereas Support Vector Machine performed best on overall predictive profile (accuracy = 0.833; F1 = 0.909; recall = 1.00; and Brier Score = 0.134) performance. CRP and LDH became the most common predictors of all models, and then neutrophils and platelets. SHAP analysis has verified that high CRP and LDH were strong predictors that forced the prediction to non-response, whereas higher lymphocyte levels were weak predictors that increased the response probability prediction. Conclusions: Machine learning models based on common inflammatory systemic markers give useful predictive information about nivolumab response. Their discriminative ability is moderate, but the high performance of SVM and Gradient Boosting pays attention to the opportunities of inflammation-based ML tools in making personalized decisions regarding immunotherapy. A combination of clinical, radiomic, and molecular biomarkers in the future can increase predictive capabilities and clinical use. Full article

This systematic review examined how the Travel Cost Method (TCM) works together with other valuation methods, such as stated and declared preferences, to improve estimates of total economic value (TEV). Despite the widespread use of TCM, no systematic synthesis has examined how its integration with complementary methods enhances TEV estimation across different ecosystems and geographical contexts. Following PRISMA guidelines, we conducted searches in Scopus and Web of Science, identifying 245 records. After the screening process, 57 studies remained for analysis. Results show that 74% of the studies combined TCM with Contingent Valuation Method (CVM), and 12.3% with Choice Experiment (CEM). Three chronological phases were identified: early domination by the United States (1985–2000), international expansion and diversification (2001–2015), and recent methodological innovation led by China (2016–2024). Forest and recreational ecosystems accounted for 25% of applications, followed by marine-coastal (21%). Within cultural ecosystem services, the subcategory of physical and experiential interactions predominates with 63.1%. Comparative analysis indicates that TCM systematically produces higher and more variable monetary estimates than CVM, reflecting its sensitivity to travel behavior and spatial scale, while stated preference methods provide more stable estimates of non-use values. Persistent methodological limitations include non-probabilistic sampling and uneven ecosystem coverage. This review advances the literature by providing the first comprehensive synthesis of integrated TCM applications, demonstrating how methodological combinations strengthen TEV estimation beyond single-method approaches. The findings offer practical guidance for policymakers designing environmental impact assessments, environmental managers selecting valuation tools tailored to ecosystem and management objectives, and researchers seeking standardized and robust frameworks for integrated ecosystem service valuation. Full article

As cities continue to expand, the role of forests in mitigating carbon emissions during urban growth has become a critical concern for both researchers and policymakers. This study constructs a comprehensive framework to assess new-type urbanization and forest health, calculates relevant metrics, and applies the Environmental Kuznets Curve model to examine how contemporary urbanization affects carbon emissions while accounting for the moderating role of forest quality. The results indicate that the impact of urbanization on carbon emissions generally follows an inverted U-shaped pattern, although significant regional variations exist. Forest quality has not yet fully realized its potential in reducing carbon footprints, largely due to the need for overall improvement in the forestry sector. In terms of how urbanization affects forest quality, traditional factors such as population migration and industrial restructuring remain the primary drivers. There is a discernible tension between conventional urban expansion and sustainable forestry development. Although modern urbanization and forest quality show promising synergies, both are constrained by their current developmental stages, which limits their effectiveness in substantially curbing carbon emissions. Full article