Search Results (10,219)

Increasingly, adaptive processes and decentralization are vital aspects of watershed governance. Equitable and sustainable water governance requires an understanding that different societal members have unique relationships with the environment and varying levels of interaction with policymakers. However, the factors facilitating public involvement under centralized governance remain less understood. This study combined the Institutional Analysis and Development framework with ordered probit regression to empirically investigate the determinants of willingness to participate (WTP) and actual participation of the public in integrated watershed management (IWM). Data from 933 valid questionnaires collected across 36 counties in Fujian, China, were used to define stakeholders’ perceptions of IWM. Results show that stakeholders are predominantly willing to participate in watershed conservation, management, or planning (85.9%), while only 32.8% frequently attend related events. Pro-environmental intentions were mainly shaped by interactional capacity—information exposure, interpersonal exchanges, and cross-reach support recognition—while actual participation was influenced by perceived biophysical conditions, rules-in-use, socioeconomic factors, and interactional capacity. Frequent observations of poor forest management practices were correlated with higher behavioral intentions, and socioeconomic dynamics significantly affected self-reported actual participation. Information sharing had the most substantial positive impact on both WTP and actual participation. These findings reinforce the necessity for an integrated and holistic approach to regional watershed resource management that fosters inclusivity and sustainability. This study provides workable insights into the social and institutional factors that shape public participation in watershed governance as it evolves toward decentralization. Full article

The Brazilian Atlantic Forest (BAF) is one of the most important biodiversity hotspots and species-rich ecosystems globally. Due to human activities, it has been significantly reduced and fragmented. This study examined both biotic (floristic composition, diversity, and structure) and abiotic (topographic and soil) factors in BAF fragments undergoing varying levels and durations of human disturbance cessation: approximately 20 years (20 y), ~30 years (30 y), and over 40 years (>40 y). We aimed to understand the recovery dynamics of floristic composition, diversity, and structure in BAF fragments in relation to abiotic factors. Several statistical tools were employed to examine similarities/differences and relationships. Forests of the 30 y group exhibit significantly greater homogeneity in terms of floristic composition, while forests of the 20 y group are characterized by lower species abundance and diversity. The floristic composition was primarily influenced by soil features and the time of disturbance. Under “Environmental Protection Areas”, soil–vegetation recovery can occur more swiftly than usually observed for BAF. A significant BAF recovery was observed approximately 40 years after the end of human disturbance. A partial recovery featured 30 y disturbed areas, while in 20 y forests, recovery is in its early stages. Human-disturbed BAF can gradually rebound when effective management practices are implemented. Full article

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R² = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R² = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment. Full article

Highway traffic officers (HTOs) are often exposed to life-threatening workplace incidents while performing their duties. However, scant research has been undertaken to address these safety concerns. This research explores case study data from highway incident reports (held by National Highways, a UK government company) and employs deep neural network (DNN) in unearthing patterns which inform safety decision makers on pertinent safety challenges confronting HTOs. A mixed philosophical stance of positivism and interpretivism was adopted to synthesise the findings made. A four-phase sequential method was implemented to evaluate the validity of the research viz.: (i) architectural design; (ii) data exploration; (iii) predictive modelling; and (iv) performance evaluation. The DNN model’s predictive performance is benchmarked against three other machine learning models, namely Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes (NB). The DNN model outperformed the other three models. Findings from the data exploration also show that most work operations undertaken by HTOs have a medium risk level with night shifts posing the greatest risk challenges. Carriageways and traffic management enclosures had the highest incident occurrence. This is the first study to uncover such hidden patterns and predict risk levels using a database specifically for HTOs. This study presents evidence-based information for proactive risk management for HTOs. Full article

Climate change has significant natural and economic implications, but its extent is particularly challenging to assess in forest management, a field which combines both of the previous aspects and requires the evaluation of the impact of climate change on tree species over a 100-year timeframe. Oaks are among the tree species of significant natural and economic value in Europe. Therefore, the aim of this study was to analyze all oak stands in Poland and verify the hypothesis regarding differences between Quercus robur and Quercus petraea stands in terms of soil type, annual total precipitation, average annual air temperature, and the length of the growing season. Additionally, this study aimed to analyze the impact of these differences on the growth rates of both oak species and test whether climate change may affect oak stands. A database containing 195,241 tree stands, including different oak species with varying shares in the stand (from 10% to 100%), was analyzed. A particular emphasis was placed on Q. robur and Q. petraea. The results show that, although both oak species have a wide common range of occurrence, there are clear differences in their habitat preferences. Based on the ordinal regression analysis of selected oak stands, it was concluded that an increase in air temperature of 1 °C could impair the growth of Q. robur and slightly improve the growth of Q. petraea. This may indicate the possibility of expanding the geographic range of sessile oaks towards the east and northeast under warming climatic conditions, provided that appropriate moisture conditions are maintained. Full article

Debris flow disasters frequently occur and pose considerable hazards; thus, it is essential to thoroughly evaluate their risks. This study constructs a database comprising 20 assessment indicators, utilizing comprehensive natural disaster risk assessment theory and incorporating the triggering factors of Huokou Lake in the Changbaishan Mountains. This research employs a hybrid ANP-CRITIC methodology to allocate weights to the assessment indicators efficiently. For hazard assessment, this research utilizes both the Information Quantity and Random Forest models for comparative analysis. The ROC curve was employed to validate the outcomes, ultimately favoring the Random Forest model due to its superior accuracy in assessing debris flow hazards. In this study, the risk of debris flow disasters in Antu County is comparatively assessed under scenarios with and without an outburst event. The findings indicate that areas of high and very high risk are predominantly located within the central regions of economically prosperous and densely populated townships. Additionally, the risk in Erdao Baihe Township escalates significantly when considering the outburst of Huokou Lake. The significance of this study lies in its ability to furnish a robust scientific basis for decision-makers aimed at preventing future debris flow disasters. Furthermore, it serves as a crucial reference for advancing sustainable regional development and facilitates the equilibrium between economic growth and environmental protection within disaster management strategies. Full article

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM_2.5 and PM₁₀, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM_2.5 and PM₁₀ concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM_2.5 and PM₁₀, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution. Full article

Background: Heart failure poses a significant global health challenge, with high rates of readmission and mortality. Accurate models to predict these outcomes are essential for effective patient management. This study investigates the impact of data pre-processing techniques on XGBoost model performance in predicting all-cause readmission and mortality among heart failure patients. Methods: A dataset of 168 features from 2008 heart failure patients was used. Pre-processing included handling missing values, categorical encoding, and standardization. Four imputation techniques were compared: Mean, Multivariate Imputation by Chained Equations (MICEs), k-nearest Neighbors (kNNs), and Random Forest (RF). XGBoost models were evaluated using accuracy, recall, F1-score, and Area Under the Curve (AUC). Robustness was assessed through 10-fold cross-validation. Results: The XGBoost model with kNN imputation, one-hot encoding, and standardization outperformed others, with an accuracy of 0.614, recall of 0.551, and F1-score of 0.476. The MICE-based model achieved the highest AUC (0.647) and mean AUC (0.65 ± 0.04) in cross-validation. All pre-processed models outperformed the default XGBoost model (AUC: 0.60). Conclusions: Data pre-processing, especially MICE with one-hot encoding and standardization, improves XGBoost performance in heart failure prediction. However, moderate AUC scores suggest further steps are needed to enhance predictive accuracy. Full article

Background/Objectives: Alzheimer’s disease (AD) is the most common form of dementia and affects approximately 50 million individuals worldwide. Interest in coconut oil (CO) as a potential dietary intervention has surged owing to its substantial medium-chain triglyceride (MCT) content. Therefore, sustaining cognitive function and potentially slowing the progression of AD are crucial. This systematic review and meta-analysis evaluated the effects of CO and its bioactive metabolites on AD and dementia. Methods: The review protocol is registered in PROSPERO (CRD42023450435). Relevant research articles published between January 2015 and June 2023 were systematically searched. Seven studies met the predetermined eligibility criteria. Thematic analysis was utilized to synthesis the data about the qualitative features, while meta-analysis was employed for the quantitative findings. A meta-analysis was conducted to assess the standardized mean difference (SMD) and the corresponding 95% confidence interval (CI). Forest plots were generated using Review Manager 5.3 (RevMan 5.3). Results: The analysis revealed that all studies showed consistent results regarding the effects of CO on cognitive scores, with little variability in the true effects of CO on cognitive scores across the studies included in the meta-analysis. Conclusions: CO improved cognitive scores in patients with AD compared with those in the control group (p < 0.05). The results of this study add to the increasing amount of evidence indicating that MCTs found in CO might be a way to improve abilities and potentially slow the advancement of AD. The findings of this study may encourage the development of targeted dietary strategies and interventions for individuals at risk of or diagnosed with AD. Full article

Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal yield variability. However, equipment’s sensor malfunctioning can result in data loss, and yield sensor systems are expensive to purchase and maintain. In this study, we analyzed corn silage yield data from two fields and three years each for two dairy farms (Farm A and B). We aimed to explore the potential of integrating high-resolution satellite data, topography, and climate data with machine learning models to estimate missing yield data for a field or a year. Our objectives were to identify key yield-explaining features and assess the accuracy of different machine learning models in estimating silage yield. Results showed that the features differed among farms with a Two-Band Enhanced Vegetation Index, EVI2 (Farm A), and elevation (Farm B) emerging as the most prominent predictors. Ensemble-based models like XGBoost, Random Forest, and Extra Tree regressors exhibited superior predictive performance. However, XGBoost performed poorly when applied to unseen fields or years, whereas Extra Tree regressor, followed closely by Random Forest, emerged as a more reliable model for predicting missing data. Despite achieving reasonable accuracy, the best performance for estimating data for a missing field (6.46 Mg/ha) and year (5.51 Mg/ha) fell short of the acceptable error threshold of 4.9 Mg/ha currently used in state policy to evaluate if a management change resulted in a yield increase. These findings emphasize the need for higher-resolution data and extended years of yield records to better capture the trends in farm-scale yield applications. Full article

Black bloom is a very serious water pollution phenomenon in eutrophic lakes, with Fe(II) and S(−II) being the key limiting factors for this problem. In this paper, three different machine learning methods, namely, Random Forest (RF), Gaussian Mixture Model (GMM), and Bayesian Network (BN), were used to explore the complex interactions among Fe(II), S(−II), and other aquatic factors in the estuary of Chaohu Lake to better characterize and monitor water degradation by black bloom. The results of RF showed that total nitrogen (TN), ammonia, total phosphorous (TP), suspended sediment concentration (SSC), and oxidation–reduction potential (ORP), which were chosen from 11 factors, had the most important relationships with Fe(II) and S(−II). The 69 sampling sites were divided in three groups identified as worst, worse, and bad according to the observed values of seven factors using the GMM. Then, the BN model was applied to three observation groups. The results showed that the structures of the interaction networks were different between the groups. S(−II) controlled only SSC production in the bad and worse group sites, while SSC was determined by both S(−II) and Fe(II) in the worst group. Ammonia and TN exhibited the most direct importance for S(−II) and Fe(II) production in all observation groups. According to the indications from the BNs, potential management strategies for different water pollution conditions were developed. Finally, the threshold values of Fe(II), S(−II), TP, ammonia, TN, SSC, and ORP, which were 0.80 mg/L, 0.04 mg/L, 0.45 mg/L, 3.44 mg/L, 4.15 mg/L, 55 mg/L, and 135 mv, respectively, were determined on the basis of the BN models. These values will be helpful to develop accurate strategies of oxygenation to quickly eliminate black bloom in the lake. Full article

Fires affect forest dynamics in seasonally dry regions such as the Mediterranean Basin. There, fire impacts on tree growth have been widely characterized in conifers, particularly pine species, but we lack information on broadleaf tree species that sprout after fires. We investigated post-fire radial growth responses in two coexisting Mediterranean hardwood species (the evergreen Quercus ilex, the deciduous Celtis australis) using tree-ring width data. We compared growth data from burnt and unburnt stands of each species subjected to similar climatic, soil and management conditions. We also calculated climate–growth relationships to assess if burnt stands were also negatively impacted by water shortage, which could hinder growth recovery. Tree-ring data of both species allowed us to quantify post-fire growth enhancements of +39.5% and +48.9% in Q. ilex and C. australis, respectively, one year after the fire. Dry spring climate conditions reduced growth, regardless of the fire impact, but high precipitation in the previous winter enhanced growth. High June radiation was negatively related to the growth of unburnt Q. ilex and burnt C. australis stands, respectively. Post-fire growth enhancement lasted for five years after the fire and it was a transitory effect because the growth rates of burnt and unburnt stands were similar afterwards. Full article

The Fujian coastal zone, a key region in China’s coastal belt, has experienced significant landscape and ecological changes due to intense human activities. Understanding the relationship between landscape patterns and ecological quality is critical for sustainable development and ecological protection. Taking the coastal cities, including Fuzhou, Xiamen, and Ningde in Fujian Province of China, as a case, the spatio–temporal changes in landscape patterns and the remote sensing-based ecological index (RSEI) during 2000 and 2020 were explored by the Google Earth Engine (GEE) cloud platform, and then their spatial relationships were identified through Pearson correlation analysis and bivariate spatial autocorrelation analysis. The findings reveal that (1) forest land was the dominant landscape in Fuzhou and Ningde, while cropland prevailed in Xiamen. Significant changes occurred in the land use landscape patterns of the three cities, mainly due to a substantial increase in the built-up land and varying degrees of reduction in arable and forest land. At the landscape level, both Fuzhou and Xiamen exhibited increased landscape fragmentation, while Ningde showed a trend of landscape aggregation; at the class level, forest land in Fuzhou and Xiamen exhibited increased fragmentation, whereas in Ningde, it showed an aggregation trend. (2) Between 2000 and 2020, the ecological–environmental quality of Fuzhou and Ningde continuously improved, while the improvement in Xiamen was less significant. Poor and fair ecological environments in the three cities were mainly concentrated in city centers and coastal zones, and areas of ecological quality degradation were primarily concentrated in coastal zones. (3) Correlation analysis indicates that, whether at the landscape level or the class level, the ecological quality of the three cities is significantly negatively correlated with the fragmentation index and significantly positively correlated with the aggregation index. Moreover, the positive correlation between ecological quality and the forest landscape aggregation index, as well as the negative correlation with the forest landscape fragmentation index, are both significantly stronger than those at the landscape level. As urbanization progresses, forest landscape fragmentation intensifies, especially in city centers and coastal areas, having a significant negative impact on ecological quality. These results highlight the importance of landscape pattern management in maintaining ecological quality. This paper provides insights for coastal cities on balancing urban development with ecological preservation in the context of rapid urbanization. Full article

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R²) ranging from 0.516 to 0.649. The R² of the RF (CIS) model is 0.446–0.537. The R² of the PLSR (CIS) model is 0.323–0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R² increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R² = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles. Full article

To meet the demands of a growing population, natural wetlands are being converted to arable land, significantly impacting soil biodiversity. This study investigated the effects of land use changes on bacterial communities in wetland, arable land, and forest soils in the Lesser Khingan Mountains using Illumina MiSeq 16S rRNA sequencing. Soil physicochemical properties and enzyme activities were measured using standard methods, while microbial diversity was assessed through sequencing analysis. Our findings revealed that forest soils had significantly higher levels of total potassium (2.62 g·kg⁻¹), electrical conductivity (8.22 mS·cm⁻¹), urease (0.18 mg·g⁻¹·d⁻¹), and nitrate reductase (0.13 mg·g⁻¹·d⁻¹), attributed to rich organic matter and active microbial communities. Conversely, arable soils showed lower total potassium (1.94 g·kg⁻¹), reduced electrical conductivity, and suppressed enzyme activities due to frequent tilling and fertilization. Wetland soils exhibited the lowest values primarily due to water saturation, which limits organic matter decomposition and microbial activity. Land use changes notably reduced microbial diversity, with conversion from forest to arable land leading to habitat loss. Forest soils supported higher abundances of Proteobacteria (37.59%) and Actinobacteriota (34.73%), while arable soils favored nitrogen-fixing bacteria. Wetlands were characterized by chemoheterotrophic and anaerobic bacteria. Overall, these findings underscore the profound influence of land use on soil microbial communities and their functional roles, highlighting the need for sustainable management practices. Full article