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Purpose: To identify the key risk factors for venous thromboembolism (VTE) in urological inpatients based on the Caprini scale using an interpretable machine learning method. Methods: VTE risk data of urological inpatients were obtained based on the Caprini scale in the case hospital. Based on the data, the Boruta method was used to further select the key variables from the 37 variables in the Caprini scale. Furthermore, decision rules corresponding to each risk level were generated using the rough set (RS) method. Finally, random forest (RF), support vector machine (SVM), and backpropagation artificial neural network (BPANN) were used to verify the data accuracy and were compared with the RS method. Results: Following the screening, the key risk factors for VTE in urology were "(C1) Age," "(C2) Minor Surgery planned," "(C3) Obesity (BMI > 25)," "(C8) Varicose veins," "(C9) Sepsis (< 1 month)," (C10) "Serious lung disease incl. pneumonia (< 1month) " (C11) COPD," "(C16) Other risk," "(C18) Major surgery (> 45 min)," "(C19) Laparoscopic surgery (> 45 min)," "(C20) Patient confined to bed (> 72 h)," "(C18) Malignancy (present or previous)," "(C23) Central venous access," "(C31) History of DVT/PE," "(C32) Other congenital or acquired thrombophilia," and "(C34) Stroke (< 1 month." According to the decision rules of different risk levels obtained using the RS method, "(C1) Age," "(C18) Major surgery (> 45 minutes)," and "(C21) Malignancy (present or previous)" were the main factors influencing mid- and high-risk levels, and some suggestions on VTE prevention were indicated based on these three factors. The average accuracies of the RS, RF, SVM, and BPANN models were 79.5%, 87.9%, 92.6%, and 97.2%, respectively. In addition, BPANN had the highest accuracy, recall, F1-score, and precision. Conclusions: The RS model achieved poorer accuracy than the other three common machine learning models. However, the RS model provides strong interpretability and allows for the identification of high-risk factors and decision rules influencing high-risk assessments of VTE in urology. This transparency is very important for clinicians in the risk assessment process. [ABSTRACT FROM AUTHOR]

Machine learning methods have improved in recent years and provide increasingly powerful tools for understanding landscape evolution. In this study, we used the random forest method based on Google Earth Engine to evaluate the desertification dynamics in northern China from 1995 to 2020. We selected Landsat series image bands, remote sensing inversion data, climate baseline data, land use data, and soil type data as variables for majority voting in the random forest method. The method's average classification accuracy was 91.6% ± 5.8 [mean ± SD], and the average kappa coefficient was 0.68 ± 0.09, suggesting good classification results. The random forest classifier results were consistent with the results of visual interpretation for the spatial distribution of different levels of desertification. From 1995 to 2000, the area of aeolian desertification increased at an average rate of 9977 km2 yr−1, and from 2000 to 2005, from 2005 to 2010, from 2010 to 2015, and from 2015 to 2020, the aeolian desertification decreased at an average rate of 2535, 3462, 1487, and 4537 km2 yr−1, respectively. [ABSTRACT FROM AUTHOR]

Background: Advance care planning (ACP) can contribute to individuals making decisions about their healthcare preferences in advance of serious illness. Up to now, the acceptance level and associated factors of ACP among the public in China remain unclear. This study aims to investigate the acceptance level of ACP in China and identify factors associated with it based on the socioecological model. Methods: A total of 19,738 participants were included in this survey. We employed a random forest regression analysis to select factors derived from the socioecological model. Multivariate generalized linear model analysis was then conducted to explore the factors that were associated with the acceptance level of ACP. Results: On a scale ranging from 0 to 100, the median score for acceptance level of ACP was 64.00 (IQR: 48.00–83.00) points. The results of the multivariate generalized linear model analysis revealed that participants who scored higher on measures of openness and neuroticism personality traits, as well as those who had greater perceptions of social support, higher levels of health literacy, better neighborly relationships, family health, and family social status, were more likely to accept ACP. Conversely, participants who reported higher levels of subjective well-being and greater family communication levels demonstrated a lower likelihood of accepting ACP. Conclusions: This study identified multiple factors associated with the acceptance level of ACP. The findings offer valuable insights that can inform the design and implementation of targeted interventions aimed at facilitating a good death and may have significant implications for the formulation of end-of-life care policies and practices in other countries facing similar challenges. [ABSTRACT FROM AUTHOR]

Understanding charging vehicles, charging stations, and built environment concerning idle connection times significantly guided the management of charging infrastructure. However, the interplay between these factors had remained incompletely understood. This study addressed this gap by investigating public charging stations in Changshu, Suzhou, China, as a case study. The random forest regression and partial dependence plots were employed to explore the nonlinear relationships between idle connection times of vehicles at public fast-charging stations and the built environment, charging stations, and charging vehicles. The exploration encompassed two typical scenarios: workdays and weekends. The findings reveal the distinct influences of various factors in different scenarios. Notably, catering service points of interests in the proximity of charging stations, significantly impact the idle connection time on both workdays and weekends. Furthermore, government groups and residential areas have a notable influence on idle connection times during workdays. Shopping service and Leisure sport have a significant impact on idle connection time during the weekends. Variables such as the charging start time and charged energy also exhibit significant effects. Importantly, these influencing factors demonstrate heterogeneity and exhibit different threshold effects. This research can offer valuable insights to planning authorities and charging facility operators for formulating strategies to enhance charging infrastructure utilization. [ABSTRACT FROM AUTHOR]

Horizontal principal stress is a fundamental parameter for reservoir reconstruction. For improving single well productivity, accurate evaluation of reservoir stress characteristics is of great importance. One of the main challenges in predicting the magnitude of the in situ stress is how to obtain the rock mechanical parameters accurately. An intelligent fusion model was proposed to predict rock mechanical parameters to address the issue that traditional approaches are not very reliable at predicting the rock mechanical parameters of complex lithology reservoirs, using transitional shale reservoir rocks as the research object. Machine learning algorithms such as nearest neighbor regression, support vector machine, and random forest were selected to construct intelligent fusion models of different rock mechanics parameters based on the laboratory test data. Finally, the logging profile of transitional shale reservoir horizontal principal stress in the study area was obtained under the constraints of the empirical physical model and measured in situ stress data. The results showed that the fusion models outperformed the single model on rock mechanics parameters and had higher accuracy in both training and test sets, meeting the engineering requirements for predicting the horizontal principal stress in the study area. [ABSTRACT FROM AUTHOR]

Objectives: To explore the influencing factors of family doctors agreement performance rate in Dongguan City, Guangdong Province. Methods: The multi-stage sampling method was used to randomly select family doctors in Dongguan City, Guangdong Province, to investigate the status quo of family doctors performance. Logistic regression analysis was used to investigate the influencing factors of family doctors performance, and decision trees and associations were used to further identify the influencing factors. Results: 389 family doctors participated in the survey, and 75.07% of them were generally able to perform their duties. Logistic regression analysis shows that whether a family doctor contracted residents is the influencing factor of family doctor performance behavior. The results of decision tree analysis showed that the biggest influence factor of family doctors performance behavior is whether the family doctors contracted residents or not. The most influential factor in decreasing the average Gini index of random forest also proved this result. Conclusions: The performance rate of family doctors in Dongguan City is relatively high. Whether family doctors sign contracts with residents is the influencing factor of family doctors performance. Improving the contract rate of family doctors is the key to improve the performance rate of family doctors. [ABSTRACT FROM AUTHOR]

The uncertainty inherent in three-dimensional (3D) mineral prospectivity mapping (MPM) encompasses (a) mineral system conceptual model uncertainty stemming from geological conceptual frameworks, (b) aleatoric uncertainty, attributable to the variability and noise due to multi-source geoscience datasets collection and processing, as well as 3D geological modeling process, and (c) epistemic uncertainty due to predictive algorithm modeling. Quantifying the uncertainty of 3D MPM is a prerequisite for accepting predictive models in exploration. Previous MPM studies were centered on addressing the mineral system conceptual model uncertainty. To the best of our knowledge, few studies quantified the aleatoric and epistemic uncertainties of 3D MPM. This study proposes a novel uncertainty-quantification machine learning framework to qualify aleatoric and epistemic uncertainties in 3D MPM by the uncertainty-quantification random forest. Another innovation of this framework is utility of the accuracy–rejection curve to provide a quantitative uncertainty threshold for exploration target delineation. The Bayesian hyperparameter optimization tunes the hyperparameters of the uncertainty-quantification random forest automatically. The case study of 3D MPM for exploration target delineation in the Wulong gold district of China demonstrated the practicality of our framework. The aleatoric uncertainty of the 3D MPM indicates that the 3D Early Cretaceous dyke model is the main source of this uncertainty. The 3D exploration targets delineated by the uncertainty-quantification machine learning framework can benefit subsurface gold exploration in the study area. [ABSTRACT FROM AUTHOR]

To investigate the distribution characteristics of selenium (Se) in mountainous soil-crop systems and examine the threshold value of Se-rich soil, 275 soil samples and 153 associated crop samples (rice, maize, tea, nuts, konjac, mushrooms, buckwheat, and coffee) were collected in Ximeng County, a typical mountainous area in southwest China. The total Se, available Se, organic matter, pH, sampling point elevation, and crop Se content were analyzed to examine the distribution characteristics of soil Se and the ability of primary crops to enrich Se in Ximeng County. Random forest and multiple regression models were established to identify the factors influencing the available soil Se and the crop Se enrichment coefficient. Finally, the Se-rich soil threshold was examined based on the total Se, available Se, and Se content in primary crops (rice, maize, and tea). The results showed soil Se resource abundance in the study region, with high Se soil accounting for 64.72% of the entire area. The soil Se content displayed significant spatial autocorrelation. The average Se enrichment coefficient of the main cultivated crops included mushrooms > nuts > rice > coffee > tea > maize > buckwheat > konjac. The total Se content in the soil had the highest impact on the available Se content in the soil and the Se enrichment coefficient of crops. A Se-rich soil threshold of 0.3 mg·kg−1 was used for rice and maize, while that of tea was 0.4 mg·kg−1. This result provided a theoretical basis for developing and utilizing Se resources in mountainous soil in southwestern China and dividing the Se-rich soil threshold. [ABSTRACT FROM AUTHOR]

Mapping spatiotemporal dynamics of crop-specific areas is of great significance in addressing challenges faced by agricultural systems. But comparable multi-phase crop maps in year series have not yet been developed in most regions of the global. In this study, we developed a framework for updating annual crop-specific area maps at 10 km resolution based on crop statistics disaggregating, multi-source data integrating and machine learning, taking factors related with crop distribution in different regions and complex agricultural systems into accounts. Experiments were conducted in three study areas (Africa, China, and USA) respectively corresponding to three conditions of the information coverage of crop distribution (low, median, and high). In our framework, we collected related spatial indicator used in previous studies and trained random forest regression models to predict spatiotemporal dynamics of crop-specific areas based on them. Annual crop statistics were further disaggregated based on probabilistic layer and harmonized based on multiple constraints. Our framework is a good attempt to integrate two strategies (top-down and bottom-up), creating more possibility for crop mapping to integrate statistic with remote sensing. Finally, our results include maps of crop-specific areas covering 42 types from 1961–2022 in Africa, maps of crop-specific areas covering 14 types from 1980–2022 in China and maps of crop-specific areas covering 15 types from 2008–2022 in USA. Results show that our products has a relatively good consistency with independent reference map or statistics. Our products provide approximate estimates for spatiotemporal dynamics of crop-specific areas in multiple regions over several decades, which could be used as data basis for food security and environmental impact assessments. [ABSTRACT FROM AUTHOR]

Background: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) systems are constructed in each hospital; while these data are only used as real-time surveillance but fail to realize the prediction and early warning function. Study is to screen effective predictors from the routine NIS data, through integrating the multiple risk factors and Machine learning (ML) methods, and eventually realize the trend prediction and risk threshold of Incidence of Nosocomial infection (INI). Methods: We selected two representative hospitals in southern and northern China, and collected NIS data from 2014 to 2021. Thirty-nine factors including hospital operation volume, nosocomial infection, antibacterial drug use and outdoor temperature data, etc. Five ML methods were used to fit the INI prediction model respectively, and to evaluate and compare their performance. Results: Compared with other models, Random Forest showed the best performance (5-fold AUC = 0.983) in both hospitals, followed by Support Vector Machine. Among all the factors, 12 indicators were significantly different between high-risk and low-risk groups for INI (P < 0.05). After screening the effective predictors through importance analysis, prediction model of the time trend was successfully constructed (R2 = 0.473 and 0.780, BIC = -1.537 and -0.731). Conclusions: The number of surgeries, antibiotics use density, critical disease rate and unreasonable prescription rate and other key indicators could be fitted to be the threshold predictions of INI and quantitative early warning. [ABSTRACT FROM AUTHOR]

Background: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China. Methods: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk. Results: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E–H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China. Conclusions: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease. [ABSTRACT FROM AUTHOR]

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O2 A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O2 A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km−1. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km−1. [ABSTRACT FROM AUTHOR]

A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China's county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods' accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops. [ABSTRACT FROM AUTHOR]

Machine learning models are widely used for landslide susceptibility prediction (LSP). To better develop a landslide susceptibility prediction model for the Loess Plateau, this study compared different machine-learning models (Logistic regression (LR), support vector machines (SVM), random forests (RF), Naïve Bayes (NB), and multilayer perceptron (MLP)). Using Xunyi County, China as the study area, 1096 landslides were collected and divided into a training set (70%) and a test set (30%). Significant influencing factors were selected from 20 landslide factors using GeoDetector. The relationships between these factors and landslides were determined by normalized frequency ratios. Five trained machine learning models calculated landslide susceptibility indices and generated landslide susceptibility maps. Additionally, the accuracy (ACC), precision (PRE), recall (REC), F1 score, AUC values, LSI distribution characteristics, and overlay analysis of historical landslides with LSM were used to evaluate the performance of the models. The results show that the LSP results of the five models are generally reasonable. The results further indicated that the use of GeoDetector was able to filter out the main factors of eolian landslides, improving the predictive performance of the model and obtaining better LSMs. The RF model had the best overall performance, followed by the SVM, and the MLP model created LSM high susceptibility zones that could contain the highest number of historical landslides. The q-values of factor contributions indicate that elevation, NDVI, rainfall, POI kernel density, and RDLS are the main factors controlling landslides, which need to be paid attention to for landslide control in the Loess Plateau region. The results of this study can provide a reference for the follow-up study of landslide prevention and control, especially landslide susceptibility modeling in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC. [ABSTRACT FROM AUTHOR]

Objectives: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. Materials and methods: Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. Results: The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. Conclusion: The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis. [ABSTRACT FROM AUTHOR]

With an increasingly urbanized world, there is an urgent need to examine how cities may evolve and achieve sustainability. This paper systematically looks at the Greater Bay Area (GBA) and the Poyang Lake Region (PLR) in China to examine the spatial processes for insights into cities and urbanization, balancing the environmental and socio‐economic dimensions. A total of 226 805 cells are analyzed to unveil the relationship between sustainability changes in 2015–2019 period and urban form indicators, considering sociodemographic variables, geographical features, and city size as control variables. Two tree‐based machine learning models (Random Forest and XGBoost) are developed. This study provides evidence that a monocentric urban form and a high share of small activity clusters are not good for sustainability. For each urban form indicator, there is a non‐linear relationship with sustainability. The results of the machine learning models reconfirm the sustainability benefits of having a strong second activity cluster comparable to the largest one. When planning cities, some forms of land use buffering are desirable. There is also support for developing relatively large activity nodes and promoting compactness in urban form. Beyond urban form characteristics, the levels of urbanization, economic development, and population are still highly relevant. [ABSTRACT FROM AUTHOR]

Snow density is of paramount importance in water resource management, snow avalanche warning, and climate change research. However, the lack of competent methods for long‐term and vast‐scale snow density mapping persists due to the intricate spatiotemporal dependencies inherent in snow density, resulting in scarce and inaccurate snow density products. To address this challenge, a spatiotemporal random forest (STRF) model is constructed by leveraging in‐situ measurements, multisource remote sensing, and reanalysis data. It tackles the spatiotemporal dependencies in snow density arising from its inherent heterogeneity and the relations involving snow density and nonlinearly connected meteorological, terrain, vegetation, and snow‐related factors. The effectiveness of the model is substantiated through rigorous validation methods, including random, temporal, and spatial block cross‐validations as well as independent validation, apparently surpassing ERA5‐Land snow density. The estimated snow density is also demonstrated to be able to improve existing snow water equivalent data set using fixed snow density. Utilizing the proposed model, a data set of daily 25‐km snow density from 1980 to 2018 is constructed for stable snow cover areas in China, which holds significant potential for research and applications in the realm of snow hydrology. Plain Language Summary: Snow density plays a crucial role in managing water resources and issuing snow avalanche warnings. Snow density exhibits great spatiotemporal dependent structure due to its spatiotemporal heterogeneity and its relations with various influencing factors, which pose challenges to mapping long‐term and large‐scale snow density. This leads to a scarcity of accurate snow density products. To tackle this issue, we develope a spatiotemporal random forest (STRF) model by combining ground measurements, remote sensing sources, and reanalysis data. Notably, our model shows impressive results apparently outperforming the ERA5‐Land snow density data set. Our estimated snow density can also enhance existing snow water equivalent data set that rely on fixed snow density. Using our model, we produce a data set of daily 25‐km snow density from 1980 to 2018 for stable snow cover areas in China. This data set holds significant potential for research and practical applications in the field of snow hydrology. Key Points: A spatiotemporal random forest (STRF) model is proposed for estimating large‐scale and long‐term snow densitySTRF depicts the spatiotemporal dependent structure of snow density and handles its nonlinear relations with various influencing factorsThe estimated snow density outperforms ERA5‐Land snow density and could improve snow water equivalent data set using fixed snow density [ABSTRACT FROM AUTHOR]

The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results. Plain Language Summary: Frequent urban flooding and waterlogging pose significant threats to human life and property. Accurate modeling urban floods is crucial for minimizing damages caused by urban flooding. However, the delayed initiation of urban observations has resulted in prevalent data scarcity in urban areas, leading to difficulties and low accuracy in estimating model parameters for ungauged urban catchments. This study proposes a parameter regionalization framework for an urban hydrological model to improve the accuracy of flood simulations in ungauged urban catchments. This framework involves classifying gauged catchments into multiple groups and identifying the optimal regression‐based methods in each group for parameter regionalization. The flood simulation performance and stability of the parameter regionalization framework is evaluated by comparing it with eight widely‐used regionalization methods in 37 urban catchments located in Shenzhen City, China. Our results show that the classification of gauged catchments can significantly improve the simulation performance of linear regression methods. Additionally, the parameter regionalization framework outperforms the other regionalization methods for both urban flood simulation in the study area and urban flood prediction in the ungauged urban catchments. This study provides a novel and efficient parameterization method for accurate flood prediction in ungauged urban catchments to reduce potential losses. Key Points: TVGM_Urban presents satisfying performance in urban flood modelingThe PRF integrating donor catchment clustering and optimal regression methods in each cluster outperforms single regionalization methodsCurve number, land cover area ratio, and slope are critical factors for runoff generation parameters of TVGM_Urban [ABSTRACT FROM AUTHOR]

Background: while most gut microbiota research has focused on term infants, the health outcomes of preterm infants are equally important. Very-low-birth-weight (VLBW) or extremely-low-birth-weight (ELBW) preterm infants have a unique gut microbiota structure, and probiotics have been reported to somewhat accelerate the maturation of the gut microbiota and reduce intestinal inflammation in very-low preterm infants, thereby improving their long-term outcomes. The aim of this study was to investigate the structure of gut microbiota in ELBW neonates to facilitate the early identification of different types of low-birth-weight (LBW) preterm infants. Methods: a total of 98 fecal samples from 39 low-birth-weight preterm infants were included in this study. Three groups were categorized according to different birth weights: ELBW (n = 39), VLBW (n = 39), and LBW (n = 20). The gut microbiota structure of neonates was obtained by 16S rRNA gene sequencing, and microbiome analysis was conducted. The community state type (CST) of the microbiota was predicted, and correlation analysis was conducted with clinical indicators. Differences in the gut microbiota composition among ELBW, VLBW, and LBW were compared. The value of gut microbiota composition in the diagnosis of extremely low birth weight was assessed via a random forest-machine learning approach. Results: we briefly analyzed the structure of the gut microbiota of preterm infants with low birth weight and found that the ELBW, VLBW, and LBW groups exhibited gut microbiota with heterogeneous compositions. Low-birth-weight preterm infants showed five CSTs dominated by Enterococcus, Staphylococcus, Klebsiella, Streptococcus, Pseudescherichia, and Acinetobacter. The birth weight and clinical indicators related to prematurity were associated with the CST. We found the composition of the gut microbiota was specific to the different types of low-birth-weight premature infants, namely, ELBW, VLBW, and LBW. The ELBW group exhibited significantly more of the potentially harmful intestinal bacteria Acinetobacter relative to the VLBW and LBW groups, as well as a significantly lower abundance of the intestinal probiotic Bifidobacterium. Based on the gut microbiota's composition and its correlation with low weight, we constructed random forest model classifiers to distinguish ELBW and VLBW/LBW infants. The area under the curve of the classifiers constructed with Enterococcus, Klebsiella, and Acinetobacter was found to reach 0.836 by machine learning evaluation, suggesting that gut microbiota composition may be a potential biomarker for ELBW preterm infants. Conclusions: the gut bacteria of preterm infants showed a CST with Enterococcus, Klebsiella, and Acinetobacter as the dominant genera. ELBW preterm infants exhibit an increase in the abundance of potentially harmful bacteria in the gut and a decrease in beneficial bacteria. These potentially harmful bacteria may be potential biomarkers for ELBW preterm infants. [ABSTRACT FROM AUTHOR]

This study investigated the potential effects of changes in inorganics on aerosol water uptake and, thus, on secondary organic aerosol (SOA) formation in wintertime haze based on the size-resolved measurements of non-refractory fine particulate matter (NR-PM 2.5) in Xi'an, northwestern China. The composition of inorganic aerosol showed significant changes in winter 2018–2019 compared to winter 2013–2014, shifting from a sulfate-rich profile to a nitrate-rich profile. In particular, the fraction of sulfate and chloride decreased, but that of nitrate increased in the entire size range, while ammonium mainly increased at larger particle sizes. These changes thus resulted in a size-dependent evolution in water uptake. Increased water uptake was observed in most cases, mainly associated with enhanced contributions of both nitrate and ammonium, with the highest increase ratio reaching 5 %–35 % at larger particle sizes and higher relative humidity (RH>70 %). The non-negligible influence of chloride on aerosol water uptake was also emphasized. The random forest analysis coupled with a Shapley additive explanation algorithm (SHAP) further showed an enhanced relative importance of aerosol water in impacting SOA formation. Aerosol water exhibited a significant contribution to SOA formation during winter 2018–2019, particularly at larger particle sizes. The SHAP value of aerosol water increased alongside higher levels of aerosol water, indicating an enhanced contribution of aerosol water to SOA formation. This implies that the majority of enhanced aerosol water uptake at larger particle sizes and high RH might facilitate the efficient aqueous-phase SOA formation. This study highlights the key role of aerosol water as a medium to link inorganics and organics in their multiphase processes. As challenges to further improve China's air quality remain and because SOA plays an increasing role in haze pollution, these results provide insight into the size-resolved evolution characteristics and offer guidance for future controls. [ABSTRACT FROM AUTHOR]

Landslides are widely distributed mountainous geological hazards that threaten economic development and people's daily lives. Interferometric synthetic aperture radar (InSAR) with comprehensive coverage and high‐precision ground displacement monitoring abilities are frequently utilized for regional‐scale active slope detection. Moreover, InSAR measurements that characterize ground dynamics are integrated with conventional topographic, hydrological, and geological landslide conditioning factors (LCFs) for landslide susceptibility mapping (LSM). Weining County in southwest China, with complex geological conditions, steep terrain, and frequent tectonic activities, is prone to catastrophic landslide failures. In this study, we refined the landslide inventory of Weining County using one ascending and one descending Sentinel‐1 dataset acquired during 2015–2021 through a small baseline subset InSAR (SBAS InSAR) analysis. We then combine the LOS measurements from both datasets using multidimensional SBAS to obtain time series two‐dimensional (2D) displacements to characterize the kinematics of active slopes. Hot spot and cluster analysis (HCA) was carried out on 2D displacement rate maps to highlight clustered deformed areas and suppress noisy signals that occurred on single pixels. Two hundred fifty‐eight landslides (including 71 active identified in this study) are used to construct 76,412 positive samples for LSM. In our study, the HCA maps, instead of the 2D displacement maps, are integrated with conventional LCFs to form an LCF_HCA set to feed support vector machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost) and Light Gradient‐Boosting Machine (LightGBM) models. A conventional LCF (LCF_CON) set and an integrated 2D displacement maps (LCF_2D) set have also been adapted for comparison. The performance of the tree‐based ensemble methods distinctly outperforms the SVM model. In the meantime, models' performances using the LCF_HCA set are superior to that of the other 2 LCF sets from all evaluation metrics. The ranks of HCA maps increased compared with 2D displacement maps from feature importance analysis, which might lead to the better performance of models using the LCF_HCA set. With the continuous accumulation of SAR images, ground dynamic characteristics from InSAR can offer us opportunities to understand landslide kinematics and enhance LSM. [ABSTRACT FROM AUTHOR]

Background: Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators. Methods: Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits. Results: Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models. Conclusion: Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention. [ABSTRACT FROM AUTHOR]

Aim. To explore the status of job crafting, leisure crafting, and burnout among nurses and to examine the impact of job crafting and leisure crafting variations on burnout using machine learning‐based models. Background. The prevalence of burnout among nurses poses a severe risk to their job performance, quality of healthcare, and the cohesiveness of nurse teams. Numerous studies have explored factors influencing nurse burnout; however, few involved job crafting and leisure crafting synchronously and elucidated the effect differences of the two crafting behaviors on nurse burnout. Methods. Multicentre cross‐sectional survey study. Nurses (n = 1235) from four Chinese tertiary hospitals were included. The Maslach Burnout Inventory‐General Survey, the Job Crafting Scale, and the Leisure Crafting Scale were employed for data collection. Four machine learning algorithms (logistic regression model, support vector machine, random forest, and gradient boosting tree) were used to analyze the data. Results. Nurses experienced mild to moderate levels of burnout and moderate to high levels of job crafting and leisure crafting. The AUC (in full) for the four models was from 0.809 to 0.821, among which the gradient boosting tree performed best, with 0.821 AUC, 0.739 accuracy, 0.470 sensitivity, 0.919 specificity, and 0.161 Brier. All models showed that job crafting was the most important predictor for burnout, while leisure crafting was identified as the second important predictor for burnout in the random forest model and gradient boosting tree model. Conclusion. Even if nurses experienced mild to moderate burnout, nurse managers should develop efficient interventions to reduce nurse burnout. Job crafting and leisure crafting may be beneficial preventative strategies against burnout among nurses at present. Implications for Nursing Management. Job and leisure crafting were identified as effective methods to reduce nurse burnout. Nurse managers should provide more opportunities for nurses' job crafting and encourage nurses crafting at their leisure time. [ABSTRACT FROM AUTHOR]

Tree-based machine learning algorithms, such as random forest, have emerged as effective tools for estimating fine particulate matter (PM2.5) from satellite observations. However, they typically have unchanged model structures and configurations over time and space, and thus may not fully capture the spatiotemporal variations in the relationship between PM2.5 and predictors, resulting in limited accuracy. Here, we propose geographically and temporally weighted tree-based models (GTW-Tree) for remote sensing of surface PM2.5. Unlike traditional tree-based models, GTW-Tree models vary by time and space to simulate the variability in PM2.5 estimation, and they can output variable importance for every location for the deeper understanding of PM2.5 determinants. Experiments in China demonstrate that GTW-Tree models significantly outperform the conventional tree-based models with predictive error reduced by >21%. The GTW-Tree-derived time-location-specific variable importance reveals spatiotemporally varying impacts of predictors on PM2.5. Aerosol optical depth (AOD) contributes largely to PM2.5 estimation, particularly in central China. The proposed models are valuable for spatiotemporal modeling and interpretation of PM2.5 and other various fields of environmental remote sensing. [ABSTRACT FROM AUTHOR]

This research aimed to explore the diverse phenotypic characteristics of moso bamboo in China and pinpoint essential characteristics of moso bamboo. In this study, 63 grids were selected using the grid method to investigate 28 phenotypic traits of moso bamboo across the entire distribution area of China. The results suggest that the phenotypic traits of moso bamboo exhibit rich diversity, with coefficients of variation ranging from 5.87% to 36.57%. The phenotypic traits of moso bamboo showed varying degrees of correlation. A principal component analysis was used to identify seven main phenotypic trait indicators: diameter at breast height (DBH), leaf area (LA), leaf weight (LW), branch-to-leaf ratio (BLr), leaf moisture content (Lmc), wall-to-cavity ratio (WCr), and node length at breast height (LN), which accounted for 81.64% of the total information. A random forest model was used, which gave good results to validate the results. The average combined phenotypic trait value (D-value) of most germplasm was 0.563. The highest D-value was found in Wuyi 1 moso in Fujian (0.803), while the lowest D-value was observed in Pingle 2 moso in Guangxi (0.317). The clustering analysis of phenotypic traits classified China's moso bamboo germplasm into four groups. Group I had the highest D-value and is an important candidate germplasm for excellent germplasm screening. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The use of multi-temporal remote sensing data for crop yield prediction has gained increasing attention. Previous research primarily focused on utilizing remote sensing data from individual or a few growth stages as input parameters or integrated data across the entire growth period. However, a detailed analysis of the impact of different temporal combinations on the accuracy of yield prediction has not been extensively reported. In this study, we optimized the temporal sequence of growth stages using interpolation methods, constructed a yield prediction model incorporating the enhanced vegetation index (EVI) at different growth stages as input parameters, and employed a random forest (RF) algorithm. The results indicated that the RF model utilizing the EVI from all the temporal combinations throughout the growth period as input parameters accurately predicted the winter wheat yield with an R2 of the calibrated dataset exceeding 0.58 and an RMSE less than 1284 kg/ha. Among the 1023 yield models tested in this study with ten different growth stage combinations, the most accurate temporal combination comprised five stages corresponding to the regreening, erecting, jointing, heading, and filling stages, with an R2 of 0.81 and an RMSE of 1250 kg/ha and an NRMSE of 15%. We also observed a significant decrease in estimation accuracy when the number of growth stages was fewer than five and a certain degree of decline when the number exceeded five. Our findings confirmed the optimal number and combination of growth stages for the best yield prediction, providing substantial insights for winter wheat yield forecasting. [ABSTRACT FROM AUTHOR]

The Yellow River Delta wetlands in China belong to the coastal wetland ecosystem, which is one of the youngest and most characteristic wetlands in the world. The Yellow River Delta wetlands are constantly changed by inland sediment and the influence of waves and storm surges, so the accurate classification of the coastal wetlands in the Yellow River Delta is of great significance for the rational utilization, development and protection of wetland resources. In this study, the Yellow River Delta sentinel-2 multispectral data were processed by super-resolution synthesis, and the feature bands were optimized. The optimal feature-band combination scheme was screened using the OIF algorithm. A deep learning model attention mechanism ResNet based on feature optimization with attention mechanism integration into the ResNet network is proposed. Compared with the classical machine learning model, the AM_ResNet model can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. The overall accuracy was 94.61% with a Kappa of 0.93, and they were improved by about 6.99% and 0.1, respectively, compared with the best-performing Random Forest Classification in machine learning. The results show that the method can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. [ABSTRACT FROM AUTHOR]

Optimizing production–living–ecological space (PLES) can benefit territorial space planning and sustainable development in China. Thus, spatiotemporal evolution characteristics and influence factor analyses are vital. Laiwu, China, was selected as the study area due to its complex terrain and development history. A multitemporal land-use and land-cover classification extracted from Landsat remote-sensing images for 2001–2018 and the self-organizing map method were combined to identify the area's multitemporal PLES. The PLES evolution trajectories were extracted based on the multitemporal PLES. The main factors influencing the PLES spatiotemporal evolution differences were revealed using the random-forest-based factor importance evaluation method. The results showed that changes in the PLES trajectories occupied ∼ 18% of the study area. The ecological space recovery type was mainly observed in previously stable ecological space areas. Changes in the production space expansion (PSE) and living space expansion were mainly agglomerated in the fringe areas of previously stable production–ecological and living space lands. Distance from early built-up areas was the main factor influencing PLES expansion. Ecological space was mainly distributed far away from built-up areas, and living and production spaces were mainly distributed near the built-up areas. Additionally, the slope of the terrain affected the spatial distribution of PLES expansion to a certain extent, and the gross domestic product clearly affected the distribution of PSE. [ABSTRACT FROM AUTHOR]

Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User's accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions. [ABSTRACT FROM AUTHOR]

Water-related cultural heritage architecture (WRCHA) represents a globally significant and potentially hybrid heritage found across river basins worldwide. Its spatial and temporal evolution characteristics offer insight into the development trends of river basin environments, yet their value within water-related cultural and environmental systems remain incompletely assessed. This study undertakes qualitative and quantitative analyses of the historical spatial and temporal distributions, influencing factors, and environmental changes affecting the water-related culture, climate, population, and urban areas of 295 WRCHA sites in the Li River Basin of China, employing drought–flood indices, GIS analyses, random forest algorithms, and other methodologies. The results reveal that (1) the Lishui Basin contains a significant distribution pattern of agglomeration for WRCHA within the river basin, concentrated along the river, at low altitudes, with minimal terrain variation, and radiating around ancient governance centers, with varying increases observed across different periods and aggregation zones and with significant spatial and temporal heterogeneities; (2) the distribution pattern is influenced by joint natural and human factors, closely tied to variables such as the river network density, DEM, population changes, and distance to ancient government sites; (3) the combination of the architecture's location and elevation, along with drought–flood curves, reflects the position of the ancient riverbed of the Lishui River and its historical maximum water level. The quantity of new constructions, in conjunction with the distance from ancient government centers and the trends in population change, thus indicates the urban scale and the frequency and severity of disasters. This study provides a research paradigm and historical reference model for investigating environmental changes in watershed systems, aiding in clarifying the historical human–water symbiosis pattern in the middle reaches of the Yangtze River. Such insights will furnish a scientific basis for future regional ecological planning and watershed environmental management. [ABSTRACT FROM AUTHOR]

Understanding the response of the mechanisms driving ecosystem services (ESs) to socioecological factors is imperative for regional sustainable ecosystem management. However, previous studies of the mechanisms driving ESs have focused more on the degree and direction (positive and negative) of effects on ES supply or the supply–demand balance, while their nonlinear response processes have not been fully considered. In this study, a theoretical framework was developed through integrating land use/land cover data and supply and demand matrices with random forest models to assess response processes, including the relative importance and marginal effects, of essential factors that drive ES demand, supply, and supply–demand balance. Using the Yangtze River Economic Belt (YREB) as an example, our results indicated that the ES deficit regions (332 of 1070 counties or 14.45% of the area) of the YREB were located mainly in the three national urban agglomerations. Moreover, this study indicated that natural environmental factors (such as slope and precipitation) significantly influence the supply and supply–demand balance of ESs, while socioeconomic factors (such as cropland ratios and population density) profoundly influence the demand for ESs. However, cropland ratios were the most important drivers of ES supply, demand, and supply–demand balance in the YREB. Moreover, three types of response processes were identified in this study: logarithmic increase, logarithmic decrease, and volatility increase. Specific driving factors (e.g., proportion of cropland area, precipitation, population density, and slope) had significant threshold effects on the supply–demand balance of ESs. The turning points that can be extracted from these response processes should be recommended for ecosystem restoration projects to maintain regional sustainable ecosystem management. [ABSTRACT FROM AUTHOR]

China, as the world's biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices' temporal characteristics. This method uses a feature space of six integrals from the crops' phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture. [ABSTRACT FROM AUTHOR]

Soil moisture is a key factor in arid ecosystems, with local variations influenced by topography and vegetation. Understanding this relationship is crucial for combating desertification. Employing ANOVA, Mean Decrease Accuracy (MDA) analysis from random forest modeling and Structural Equation Modeling (SEM), this study investigates the distribution of soil moisture and its associations with topographic and vegetative factors across four micro-geomorphic units in the Tengger Desert, China. Significant heterogeneity in soil moisture across various layers and locations, including windward and leeward slopes and the tops and bottoms of dunes, was observed. Soil moisture generally increases from the surface down to 300 cm, with diminishing fluctuations at greater depths. Soil moisture peaks in the surface and middle layers on windward slopes and in deep layers at the bottom of dunes, exhibiting an initial rise and then a decline on windward slopes. Topographic (including slope direction and elevation difference) and vegetation (including shrub and herb coverage) factors significantly influence soil moisture across three depth layers. Topographic factors negatively affect soil moisture directly, whereas vegetation positively influences it indirectly, with shrub and herb abundance enhancing moisture levels. These insights inform ecological management and the formulation of soil moisture-conservation strategies in arid deserts. The study underscores customizing sand-binding vegetation to various micro-geomorphic dune units. [ABSTRACT FROM AUTHOR]

Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction. [ABSTRACT FROM AUTHOR]

Background: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. Methods: This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. Results: A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. Conclusions: In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention. [ABSTRACT FROM AUTHOR]

Objective: To describe the lipid metabolic profile of different patients with coronavirus disease 2019 (COVID-19) and contribute new evidence on the progression and severity prediction of COVID-19. Methods: This case-control study was conducted in Peking University Third Hospital, China. The laboratory-confirmed COVID-19 patients aged =18 years old and diagnosed as pneumonia from December 2022 to January 2023 were included. Serum lipids were detected. The discrimination ability was calculated with the area under the curve (AUC). A random forest (RF) model was conducted to determine the significance of different lipids. Results: Totally, 44 COVID-19 patients were enrolled with 16 mild and 28 severe patients. The top 5 super classes were triacylglycerols (TAG, 55.9%), phosphatidylethanolamines (PE, 10.9%), phosphatidylcholines (PC, 6.8%), diacylglycerols (DAG, 5.9%) and free fatty acids (FFA, 3.6%) among the 778 detected lipids from the serum of COVID-19 patients. Certain lipids, especially lysophosphatidylcholines (LPCs), turned to have significant correlations with certain immune/cytokine indexes. Reduced level of LPC 20:0 was observed in severe patients particularly in acute stage. The AUC of LPC 20:0 reached 0.940 in discriminating mild and severe patients and 0.807 in discriminating acute and recovery stages in the severe patients. The results of RF models also suggested the significance of LPCs in predicting the severity and progression of COVID-19. Conclusion: Lipids probably have the potential to differentiate and forecast the severity, progression, and clinical outcomes of COVID-19 patients, with implications for immune/inflammatory responses. LPC 20:0 might be a potential target in predicting the progression and outcome and the treatment of COVID-19. [ABSTRACT FROM AUTHOR]

Background: Among the neurological complications of influenza in children, the most severe is acute necrotizing encephalopathy (ANE), with a high mortality rate and neurological sequelae. ANE is characterized by rapid progression to death within 1–2 days from onset. However, the knowledge about the early diagnosis of ANE is limited, which is often misdiagnosed as simple seizures/convulsions or mild acute influenza-associated encephalopathy (IAE). Objective: To develop and validate an early prediction model to discriminate the ANE from two common neurological complications, seizures/convulsions and mild IAE in children with influenza. Methods: This retrospective case-control study included patients with ANE (median age 3.8 (2.3,5.4) years), seizures/convulsions alone (median age 2.6 (1.7,4.3) years), or mild IAE (median age 2.8 (1.5,6.1) years) at a tertiary pediatric medical center in China between November 2012 to January 2020. The random forest algorithm was used to screen the characteristics and construct a prediction model. Results: Of the 433 patients, 278 (64.2%) had seizures/convulsions alone, 106 (24.5%) had mild IAE, and 49 (11.3%) had ANE. The discrimination performance of the model was satisfactory, with an accuracy above 0.80 from both model development (84.2%) and internal validation (88.2%). Seizures/convulsions were less likely to be wrongly classified (3.7%, 2/54), but mild IAE (22.7%, 5/22) was prone to be misdiagnosed as seizures/convulsions, and a small proportion (4.5%, 1/22) of them was prone to be misdiagnosed as ANE. Of the children with ANE, 22.2% (2/9) were misdiagnosed as mild IAE, and none were misdiagnosed as seizures/convulsions. Conclusion: This model can distinguish the ANE from seizures/convulsions with high accuracy and from mild IAE close to 80% accuracy, providing valuable information for the early management of children with influenza. [ABSTRACT FROM AUTHOR]

This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China's impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path "innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy" has significantly positive direct and indirect effects in both the treatment group and the control group. However, "innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector's impossible triangle coupling coordination degree" and "innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector's impossible triangle coupling coordination degree" have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese Medicine (TCM) defines constitutions which are relevant to corresponding diseases among people. As one of the common constitutions, Yin-deficiency constitution influences a number of Chinese population in the disease onset. Therefore, accurate Yin-deficiency constitution identification is significant for disease prevention and treatment. Methods: In this study, we collected participants with Yin-deficiency constitution and balanced constitution, separately. The least absolute shrinkage and selection operator (LASSO) and logistic regression were used to analyze genetic predictors. Four machine learning models for Yin-deficiency constitution classification with multiple combined genetic indicators were integrated to analyze and identify the optimal model and features. The Shapley Additive exPlanations (SHAP) interpretation was developed for model explanation. Results: The results showed that, NFKBIA, BCL2A1 and CCL4 were the most associated genetic indicators with Yin-deficiency constitution. Random forest with three genetic predictors including NFKBIA, BCL2A1 and CCL4 was the optimal model, area under curve (AUC): 0.937 (95% CI 0.844–1.000), sensitivity: 0.870, specificity: 0.900. The SHAP method provided an intuitive explanation of risk leading to individual predictions. Conclusion: We constructed a Yin-deficiency constitution classification model based on machine learning and explained it with the SHAP method, providing an objective Yin-deficiency constitution identification system in TCM and the guidance for clinicians. [ABSTRACT FROM AUTHOR]

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification. [ABSTRACT FROM AUTHOR]

Background: Non‐anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China. Objective: To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non‐anemic population. Methods: Digital morphological data from 76 TT gene carriers and 97 controls were collected. The AI technology‐based Mindray MC‐100i was used to quantitatively analyze the percentage of abnormal erythrocytes. Further, ML was used to construct a prediction model. Results: Non‐anemic TT carriers accounted for over 60% of the TT cases. Random Forest was selected as the prediction model and named TT@Normal. The TT@Normal algorithm showed outstanding performance in the training, validation, and external validation sets and could efficiently identify TT carriers in the non‐anemic population. The top three weights in the TT@Normal model were the target cells, microcytes, and teardrop cells. Elevated percentages of abnormal erythrocytes should raise a strong suspicion of being a TT gene carrier. TT@Normal could be promoted and used as a visualization and sharing tool. It is accessible through a URL link and can be used by medical staff online to predict the possibility of TT gene carriage in a non‐anemic population. Conclusions: The ML‐based model TT@Normal could efficiently identify TT carriers in non‐anemic people. Elevated percentages of target cells, microcytes, and teardrop cells should raise a strong suspicion of being a TT gene carrier. [ABSTRACT FROM AUTHOR]

The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of variables in optimal load distribution, which limit the progress of research. In this paper, we propose a new optimal method of the economic operation of hydropower stations based on improved Long Short-Term Memory neural network (I-LSTM) and Random Forest (RF) algorithm. Firstly, in order to accurately estimate the water consumption, the LSTM model's hyperparameters are optimized using improved particle swarm optimization, and the I-LSTM method is proposed to fit the flow characteristic curve of the hydraulic turbines. Secondly, the Random Forest machine learning algorithm is introduced to establish a load-distribution model with its powerful feature extraction and learning ability. To improve the accuracy of the load-distribution model, we use the K-means algorithm to cluster the historical data and optimize the parameters of the Random Forest model. A Hydropower Station in China is selected for a case study. It is shown that (1) the I-LSTM method fits the operating characteristics under various working conditions and actual operating characteristics of hydraulic turbines, ensuring that they are closest to the actual operating state; (2) the I-LSTM method is compared with Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Long Short-Term Memory neural network (LSTM). The prediction results of SVM have a large error, but compared with ELM and LSTM, MSE is reduced by about 46% and 38% respectively. MAE is reduced by about 25% and 21%, respectively. RMSE is reduced by about 27% and 24%, respectively; (3) the RF algorithm performs better than the traditional dynamic programming algorithm in load distribution. With the passage of time and the increase in training samples, the prediction accuracy of the Random Forest model has steadily improved, which helps to achieve optimal operation of the units, reducing their average total water consumption by 1.24%. This study provides strong support for the application of intelligent low-consumption optimization strategies in hydropower fields, which can bring higher economic benefits and resource savings to renewable energy production. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has shown its effectiveness in handling multi-geoinformation. Yet, the black-box nature of ML algorithms has restricted their widespread adoption in the domain of mineral prospectivity mapping (MPM). In this paper, methods for interpreting ML model predictions are introduced to aid ML-based MPM, with the goal of extracting richer insights from the ML modeling of an exploration geochemical dataset. The partial dependence plot (PDP) and accumulated local effect (ALE) plot, along with the SHAP value analysis, were utilized to demonstrate the application of random forest (RF) modeling within both regression and classification frameworks. Initially, the random forest regression (RFR) model established the relationship between the concentrations of Au and those of elements such as As, Sb, and Hg in the study area, and from this model, the most important geochemical elements and their quantitative relationships with Au were revealed by their contributions in the modeling through PDP and ALE analyses. Secondly, the RF classification modeling established the relationships of mineralization occurrences (i.e., known mineral deposits) with geochemical elements (i.e., Au, As, Sb, Hg, Cu, Pb, Zn, and Ag), as did RFR modeling. The most important geochemical elements for indicating regional Au mineralization and the trajectories of PDP and ALE reached a consensus that As and Sb contributed the most, both in the regression and classification modeling, with regard to Au mineralization. Finally, the SHAP values illustrated the behavior of the training samples (i.e., known mineral deposits) in RF modeling, and the resulting prospectivity map was evaluated using receiver operating characteristics. [ABSTRACT FROM AUTHOR]

This study evaluates whether random forest (RF) models are as effective as traditional Logistic Regression (LR) models in predicting multidrug-resistant Gram-negative bacterial nosocomial infections. Data were collected from 541 patients with hospital-acquired Gram-negative bacterial infections at two tertiary-level hospitals in Urumqi, Xinjiang, China, from August 2022 to November 2023. Relevant literature informed the selection of significant predictors based on patients' pre-infection clinical information and medication history. The data were split into a training set of 379 cases and a validation set of 162 cases, adhering to a 7:3 ratio. Both RF and LR models were developed using the training set and subsequently evaluated on the validation set. The LR model achieved an accuracy of 84.57%, sensitivity of 82.89%, specificity of 80.10%, positive predictive value of 84%, negative predictive value of 85.06%, and a Yoden index of 0.69. In contrast, the RF model demonstrated superior performance with an accuracy of 89.51%, sensitivity of 90.79%, specificity of 88.37%, positive predictive value of 87.34%, negative predictive value of 91.57%, and a Yoden index of 0.79. Receiver operating characteristic curve analysis revealed an area under the curve of 0.91 for the LR model and 0.94 for the RF model. These findings indicate that the RF model surpasses the LR model in specificity, sensitivity, and accuracy in predicting hospital-acquired multidrug-resistant Gram-negative infections, showcasing its greater potential for clinical application. [ABSTRACT FROM AUTHOR]

Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, R2 value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain in interpreting the predictions of ML models. To reveal the response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how the results of the ML model are realized. This study focuses on Zhenba County in Shaanxi Province, China, employing both Random Forest (RF) and Support Vector Machine (SVM) to develop LSM models optimized through Random Search (RS). To enhance interpretability, the study incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAP). The RS-optimized RF model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.965. The interpretability model identified the NDVI and distance from road as important factors influencing landslides occurrence. NDVI plays a positive role in the occurrence of landslides in this region, and the landslide-prone areas are within 500 m from the road. These analyses indicate the importance of improved hyperparameter selection in enhancing model accuracy and performance. The interpretability model provides valuable insights into LSM, facilitating a deeper understanding of landslide formation mechanisms and guiding the formulation of effective prevention and control strategies. [ABSTRACT FROM AUTHOR]

Cropland is a vital resource intricately connected to food security. Currently, the issue of cropland abandonment poses a serious threat to food production and supply, presenting a significant challenge to rural economies and the stability of the food supply chain. The hilly and cloudy regions of southwest China are particularly affected by cropland abandonment, presenting significant challenges in accurately mapping the distribution of abandoned cropland due to fragmentation and heavy cloud pollution. Therefore, this study focuses on Mingshan County, located in Ya'an City, Sichuan Province, China, as the study area. Utilizing Google Earth Engine (GEE) and a random forest algorithm, a method integrating multi-source data from Landsat 8, Sentinel-2, and Sentinel-1 is proposed to extract abandoned cropland spanning from 2018 to 2022. This study analyzes spatial and temporal characteristics, employing the Geodetector with optimal parameters to explore the underlying mechanisms. The findings reveal the following: (1) The method achieves an overall accuracy of land use classification surpassing 88.67%, with a Kappa coefficient exceeding 0.87. Specifically, the accuracy for identifying abandoned cropland reaches 87.00%. (2) From 2018 to 2022, the abandonment rate in Mingshan County fluctuated between 4.58% and 5.77%, averaging 5.03%. The lowest abandonment rate occurred in 2019–2020, while the highest was observed in 2020–2021. (3) Cropland abandonment is influenced by both natural and social factors. Elevation and slope are the main driving factors, alongside factors such as distance to road, town, and residential settlement that all significantly contribute to abandonment trends. These five factors exhibit positive correlation with the abandonment rate, with distance to the river showing relatively weaker explanatory power. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones. [ABSTRACT FROM AUTHOR]

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings. [ABSTRACT FROM AUTHOR]