Search Results (139)

This paper reports a statistical analysis of a database archiving information on the strengths of the materials in existing Italian bridges having pre- and post-tensioned concrete beams. Data were collected in anonymous form by analyzing a stock of about 170 bridges built between 1960 and 2000 and located in several Italian regions. To date, the database refers to steel reinforcing bars, concrete, and prestressing steel, whose strengths were gathered from design nominal values, acceptance certificates, and in situ test results, all derived by consulting the available documents for each examined bridge. At first, this paper describes how the available data were collected. Then, the results of a statistical analysis are presented and commented on. Moreover, goodness-of-fit tests are carried out to verify the assumption validity of a normal distribution for steel reinforcing bars and prestressing steel, and a log-normal distribution for concrete. The database represents a valuable resource for researchers and practitioners for the assessment of existing bridges. It may be applied for the use of prior knowledge within a framework where Bayesian methods are included for reducing uncertainties. The database provides essential information on the strengths of the materials to be used for a simulated design and/or for verification in the case of limited knowledge. Goodness-of-fit tests make the collected information very useful, even if probabilistic methods are applied. Full article

Promoting social equity is a global issue, and common prosperity is an important goal for human society’s sustainable development. This study is the first to examine climate risks’ impacts on common prosperity from the perspective of corporate employees, providing micro-level evidence for the coordinated development of climate governance and social equity. Employing data from companies listed on the Shanghai and Shenzhen stock exchanges from 2016 to 2023, a fixed-effects model analysis was conducted, and the results showed the following: (1) Climate risks are positively associated with the common prosperity of corporate employees in a significant way, and this effect is mainly achieved through employee guarantees, rather than employee remuneration or employment. (2) Climate risk will increase corporate financing constraints, but it will also force companies to improve their ESG performance. (3) The mechanism tests show that climate risks indirectly promote improvements in employee rights and interests by forcing companies to improve the quality of internal controls and audits. (4) The results of the moderating effect analysis show that corporate size and performance have a positive moderating effect on the relationship between climate risk and the common prosperity of corporate employees. This finding may indicate the transmission path of “climate pressure—governance upgrade—social equity” and suggest that climate governance may be transformed into social value through institutional changes in enterprises. This study breaks through the limitations of traditional research on the financial perspective of the economic consequences of climate risks, incorporates employee welfare into the climate governance assessment framework for the first time, expands the micro research dimension of common prosperity, provides a new paradigm for cross-research on ESG and social equity, and offers recommendations and references for different stakeholders. Full article

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article

Forests play a pivotal role in the global carbon cycle, making accurate estimation of forest carbon stocks essential for climate change mitigation efforts. However, the diverse methods available for assessing forest carbon yield varying results and have different limitations. This study provides a comprehensive review of current methods for estimating forest carbon stocks, including field-based measurements, remote sensing techniques, and integrated approaches. We systematically collected and analyzed recent studies (2010–2025) on forest carbon estimation across various ecosystems. Our review indicates that field-based methods, such as forest inventories and allometric equations, offer high accuracy at local scales but are labor-intensive. Remote sensing methods (e.g., LiDAR and satellite imagery) enable large-scale carbon assessment with moderate accuracy and efficiency. Integrated approaches that combine ground measurements with remote sensing data can improve accuracy while expanding spatial coverage. We discuss the strengths and weaknesses of each method category in terms of accuracy, cost, and scalability. Based on the synthesis of findings, we recommend a balanced approach that leverages both ground and remote sensing techniques for reliable forest carbon monitoring. This review also identifies knowledge gaps and suggests directions for future research to enhance the precision and applicability of forest carbon estimation methods. Full article

Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation and Clustered Means (BICM) method, which fuses multi-view point cloud data captured by RGB-D cameras to enable robust 3D surface reconstruction and precise volume estimation. By incorporating point cloud splicing, down-sampling, clustering, and 3D B-spline interpolation, the proposed method effectively mitigates issues such as surface notches and misalignment, significantly enhancing the accuracy of grain pile volume calculations across different viewpoints and sampling resolutions. The results of this study show that a volumetric measurement error of less than 5% can be achieved using an RGB-D camera located at two orthogonal viewpoints in combination with the BICM method, and the error can be further reduced to 1.25% when using four viewpoints. In addition to providing rapid inventory assessment of grain stocks, this approach also generates accurate local maps for the autonomous navigation of grain silo robots, thereby advancing the level of intelligent management within grain storage facilities. Full article

Saninten (Castanopsis argentea (Blume) A.DC.) is a protected plant that grows in the Mount Gede Pangrango National Park (MGPNP) area in West Java. Its population is limited, and as a valuable biological resource, Castanopsis has traditionally been utilized by indigenous communities, particularly those residing in proximity to the forest. However, the expansion and development of tourism pose a potential threat to the ecosystems of C. argentea and other endemic plant species, as well as to the wildlife that depend on these habitats. Comprehensive data on biodiversity, species composition, forest structure, and carbon stock status are crucial for assessing the potential impact of future tourism development. Our investigation was conducted from November 2023 to March 2024 in a three-hectare utilization zone within the confines of the national park. The findings documented a total of 36 species across 23 distinct plant families, with the families Fagaceae, Moraceae, and Myrtaceae exhibiting the highest levels of dominance. The regeneration of stands at the study site predominantly comprised arboreal species with the most substantial carbon stocks, including C. acuminatissima (Blume) A.DC. (Riung anak), C. argentea (Saninten), and Litsea sp. (Huru). C. argentea supplies several functions within this ecosystem that are interconnected with other components. With aboveground carbon stocks reaching 560.47 tons C/ha, the forest demonstrates high sequestration potential, reinforcing the need to conserve mature stands for both biodiversity and climate benefits. Therefore, in the future, the conservation of C. argentea will benefit the maintenance of the ecosystem’s attractiveness without adversely affecting the social and cultural structures of the local population. Full article

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach. Full article

To ensure sustainability, the Ecosystem Approach to Fisheries (EAF) requires the evaluation of the impacts of fisheries beyond the main targeted species, to include those on bycaught, endangered, threatened and protected populations and keystone species. However, traditional stock assessments require extensive datasets that are often unavailable for data-limited fisheries, particularly in small-scale settings or in the Global South. This study evaluates the robustness of length-based approaches for fish stock assessment by comparing simple indicators and quantitative methods using an age-structured Operating Model. Simulations were conducted for a range of scenarios, for a range of life-history types and recruitment and natural mortality dynamics. Results reveal that while length-based approaches can effectively track trends in fishing mortality, performance varies significantly depending on species-specific life histories and assumptions about key parameters. Simple indicators often matched or outperformed complex methods, particularly when assumptions about equilibrium conditions or natural mortality were violated. The study highlights the limitations of length-based methods for classifying stock status relative to reference points, but demonstrates their utility when used with historical reference periods or as part of empirical harvest control rules. The findings provide practical guidance for applying length-based approaches in data-limited fisheries management, ensuring sustainability in data- and capacity-limited situations. Full article

The degradation of ‘black soil beach’ (BSB) ecosystems in the Three-River-Source region, characterized by widespread bald patches and severe soil erosion, poses a critical threat to regional ecological security and sustainable pastoralism. This study aims to elucidate the spatial distribution patterns and driving factors of bald patches in BSB degraded grasslands within the Guoluo Tibetan Autonomous Prefecture, providing a scientific basis for targeted restoration strategies. Utilizing multi-source remote sensing data (Landsat 8–9 OLI, UAV imagery, and Google Earth), we employed the Multiple Endmember Spectral Mixture Analysis (MESMA) method to identify bald patches, combined with the landscape pattern index and spatial autocorrelation to quantify their spatial heterogeneity. Geographical detector analysis was applied to assess the influence of natural and anthropogenic factors. The results indicate the following: (1) The patches are bounded by the Yellow River, showing a distribution pattern of ‘high in the west and low in the east’. The total area of patches reached 32,222.11 km², accounting for 43.43% of the total area of Guoluo Prefecture, among which Maduo County and Dari County had the highest degradation rate. (2) With the aggravation of degradation, the patch density of each county increased first and then decreased, while the aggregation index and landscape shape index continued to decrease. (3) Spatial autocorrelation of bare patches strengthens with degradation severity (Moran’s I index 0.6543→0.7999). LISA identified two clusters: the high–high agglomeration area in the north of Maduo–Dari and the low–low agglomeration area in the southeast of Jiuzhi–Banma, revealing the spatial heterogeneity of the degradation process. (4) The spatial distribution pattern of bare patches was mainly affected by the annual average precipitation and actual stocking capacity, and the synergistic effect was significantly higher than that of a single factor. The combination of a 4491–4708 m high altitude area, 0–5° gentle slope zone, and soil texture (clay 27–31%, silt 43–100%) has the highest degradation risk. This multi-factor coupling effect explains the limitations of traditional single factor analysis and provides a new perspective for accurate repair. Full article

Regular measurement of carbon stock in the world’s forests is critical for carbon accounting and reporting under national and international climate initiatives and for scientific research but has been largely limited in scalability and temporal resolution due to a lack of ground-based assessments. Increasing efforts have been made to address these challenges by incorporating remotely sensed data. We present a new methodology that uses multi-sensor, multispectral imagery at a resolution of 10 m and a deep learning-based model that unifies the prediction of aboveground biomass density (AGBD), canopy height (CH), and canopy cover (CC), as well as uncertainty estimations for all three quantities. The model architecture is a custom Feature Pyramid Network consisting of an encoder, decoder, and multiple prediction heads, all based on convolutional neural networks. It is trained on millions of globally sampled GEDI-L2/L4 measurements. We validate the capability of the model by deploying it over the entire globe for the year 2023 as well as annually from 2016 to 2023 over selected areas. The model achieves a mean absolute error for AGBD (CH, CC) of 26.1 Mg/ha (3.7 m, 9.9%) and a root mean squared error of 50.6 Mg/ha (5.4 m, 15.8%) on a globally sampled test dataset, demonstrating a significant improvement over previously published results. We also report the model performance against independently collected ground measurements published in the literature, which show a high degree of correlation across varying conditions. We further show that our pre-trained model facilitates seamless transferability to other GEDI variables due to its multi-head architecture. Full article

Plantation forests (PFs) play a crucial role in China’s climate change mitigation strategy due to their significant capacity to sequestrate carbon (C). Understanding the long-term trend in PFs’ C uptake capacity and the key drivers influencing it is crucial for optimizing PF management and planning for climate mitigation. In this study, we quantified the long-term (1981–2019) C sequestration of PFs in Jiangsu Province, where PFs have expanded considerably in recent decades, particularly since 2015. Seasonal and interannual variations in gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) were assessed using the boreal ecosystem productivity simulator (BEPS), a process-based terrestrial biogeochemical model. The model integrates multiple sources of remote-sensing datasets, such as leaf area index and land cover data, to simulate the critical biogeochemical processes governing land surface dynamics, enabling the quantification of vegetation and soil C stocks and nutrient cycling patterns. The results indicated a significant increasing trend in GPP, NPP, and NEP over the past four decades, suggesting enhanced C sequestration by PFs across the study region. The interannual variability in these indicators was associated with that of nitrogen (N) deposition in recent years, implying that nutrient availability could be a limiting factor for plantation productivity. Seasonal GPP and NPP exhibited peak values in spring (April to May) or late summer (August to September), with increases in growing season productivity in recent years. In contrast, NEP peaked in spring (April to May) but declined to negative values in early summer (July to August), indicating a seasonal C source–sink transition. All three indicators showed a general negative correlation with late-growing-season temperature (August to September), suggesting that summer droughts probably highly constrained the C sequestration of the existing PFs. These findings provide insights for the strategic implementation and management of PFs, particularly in regions with a warm temperate climate undergoing afforestation expansion. Full article

This paper critically examines artificial intelligence applications in stock market forecasting, addressing significant gaps in the existing literature that often overlook the tension between theoretical market efficiency and empirical predictability. While numerous reviews catalog methodologies, they frequently fail to rigorously evaluate model performance across different market regimes or reconcile statistical significance with economic relevance. We analyze techniques ranging from traditional statistical models to advanced deep learning architectures, finding that ensemble methods like Extra Trees, Random Forest, and XGBoost consistently outperform single classifiers, achieving directional accuracy of up to 86% in specific market conditions. Our analysis reveals that hybrid approaches integrating multiple data sources demonstrate superior performance by capturing complementary market signals, yet many models showing statistical significance fail to generate economic value after accounting for transaction costs and market impact. By addressing methodological challenges including backtest overfitting, regime changes, and implementation constraints, we provide a novel comprehensive framework for rigorous model assessment that bridges the divide between academic research and practical implementation. This review makes three key contributions: (1) a reconciliation of the Efficient Market Hypothesis with AI-driven predictability through an adaptive market framework, (2) a multi-dimensional evaluation methodology that extends beyond classification accuracy to financial performance, and (3) an identification of promising research directions in explainable AI, transfer learning, causal modeling, and privacy-preserving techniques that address current limitations. Full article

The effects of climate change are particularly pronounced in cities, where urban green infrastructure—such as trees, parks, and green spaces—plays a vital role in both climate adaptation and mitigation. This study assesses the carbon sequestration potential of urban forests in Budapest, the capital city of Hungary, which lies at the intersection of the Great Hungarian Plain and the Buda Hills, and is traversed by the Danube River. The city is characterized by a temperate climate with hot summers and cold winters, and a diverse range of soil types, including shallow Leptosols and Cambisols in the limestone and dolomite hills of Buda, well-developed Luvisols and Regosols in the valleys, Fluvisols and Arenosols in the flood-affected areas of Pest, and Technosols found on both sides of the city. The assessment utilizes data from the National Forestry Database and the Copernicus Land Monitoring Service High Resolution Layer Tree Cover Density. The results show that Budapest’s urban forests and trees contribute an estimated annual carbon offset of −41,338 tCO₂, approximately 1% of the city’s total emissions. The urban forests on the Buda and Pest sides of the city exhibit notable differences in carbon sequestration and storage, age class structure, tree species composition, and naturalness. On the Buda side, older semi-natural forests dominated by native species primarily act as in situ carbon reservoirs, with limited additional sequestration capacity due to their older age, slower growth, and longer rotation periods. In contrast, the Pest-side forests, which are primarily extensively managed introduced forests and tree plantations, contain a higher proportion of non-native species such as black locust (Robinia pseudoacacia) and hybrid poplars (Populus × euramericana). Despite harsher climatic conditions, Pest-side forests perform better in carbon sink capacity compared to those on the Buda side, as they are younger, with lower carbon stocks but higher sequestration rates. Our findings provide valuable insights for the development of climate-resilient urban forestry and planning strategies, emphasizing the importance of enhancing the long-term carbon sequestration potential of urban forests. Full article

Background: Saudi Arabia is undertaking a comprehensive reform of its healthcare system to improve the efficiency and accessibility of public healthcare services. A key aspect of this initiative is outsourcing outpatient pharmacy services within the public health sector to retail pharmacies through an electronic prescribing platform known as Wasfaty. The National Unified Procurement Company (NUPCO) manages this platform to ensure spending efficiency and patient accessibility to essential medications. However, there has been a lack of research evaluating the adherence of the Wasfaty e-prescribing platform to established best practices for Computerized Provider Order Entry (CPOE), which are commonly used to assess the performance of various ambulatory e-prescribing systems globally. Objective: This study aimed to assess the level of adherence of Wasfaty to best practices for CPOE. Methods: This descriptive cross-sectional single-center study reviewed filled prescriptions through Wasfaty from May 2022 to December 2023. A list of 60 functional features, including but not limited to patient identification and data access, medication selection, alerts, patient education, data transmission and storage, monitoring and renewals, transparency and accountability, and feedback, was utilized to evaluate adherence. The adherence level was categorized into four groups: fully implemented, partially implemented, not implemented, and not applicable. Two pharmacy interns, a clinical pharmacist, and a researcher, reviewed the prescriptions to determine the platform’s adherence to these 60 CPOE features. Results: From May 2022 to December 2023, a total of 1965 prescriptions were filled in retail pharmacies for out-of-stock medications for 1367 patients. These prescriptions included medications for various areas, with the following distribution: gastroenterology (44.10%), cardiology (18.14%), anti-infectives (2.42%), urology (8.85%), dermatology (3.6%), hematology (0.29%), muscle relaxants (0.8%), neurology (19.17%), pulmonology (1.46%), and other categories (1.23%). Of the 60 functional characteristics a CPOE platform should include, only 19 (31.66%) were fully implemented, while 10 (16.66%) were partially implemented. Conclusions: The Wasfaty platform is deficient in several key functional features necessary for e-prescribing, which are essential for ensuring patient safety and enhancing the satisfaction of both prescribers and patients. This study underscores the importance of improving the Wasfaty platform to reduce the risk of adverse drug events. Full article

This study developed an integrated approach for estimating tropical forest aboveground biomass (AGB) by combining UAV–LiDAR structural metrics and Sentinel-2B spectral data, optimized through successive projections algorithm (SPA) feature selection and random forest (RF) regression. Field surveys across three tropical forest sites in Hainan Province (49 plots) provided ground-truth AGB measurements, while UAV–LiDAR (1 m resolution) and Sentinel-2B (10 m) data were processed to extract 98 and 69 features, respectively. The results showed that LiDAR-derived elevation metrics (e.g., percentiles and kurtosis) correlated strongly with the AGB measurements (r = 0.652–0.751), outperforming Sentinel-2B vegetation indices (max r = 0.520). SPA–RF models with selected features significantly improved accuracy compared to full-feature RF, achieving R² = 0.670 (LiDAR), 0.522 (Sentinel-2B), and 0.749 (coupled data), with the fusion model reducing errors by 46–54% in high-biomass areas. Despite Sentinel-2B’s spectral saturation limitations, its integration with LiDAR enhanced spatial heterogeneity representation, particularly in complex canopies. The 200-iteration randomized validation ensured a robust performance, with mean absolute relative errors of ≤0.071 for fused data. This study demonstrates that strategic multi-sensor fusion, coupled with SPA-optimized feature selection, significantly improves tropical AGB estimation accuracy, offering a scalable framework for carbon stock assessments in support of Reducing Emissions from Deforestation and Forest Degradation (REDD+) and climate mitigation initiatives. Full article