Search

Suicide has been recognized as a major global public health issue. Depressed adolescents are more prone to experiencing it. We explore risk factors and their differences on suicidal ideation and suicide attempts to further enhance our understanding of suicidal behavior. 2343 depressed adolescents aged 12–18 from 9 provinces/cities in China participated in this cross-sectional study. We utilized decision tree model, incorporating 32 factors encompassing participants' suicidal behavior. The feature importance of each factor was measured using Gini coefficients. The decision tree model demonstrated a good fit with high accuracy (SI = 0.86, SA = 0.85 and F-Score (SI = 0.85, SA = 0.83). The predictive importance of each factor varied between groups with suicidal ideation and with suicide attempts. The most significant risk factor in both groups was depression (SI = 16.7 %, SA = 19.8 %). However, factors such as academic stress (SI = 7.2 %, SA = 1.6 %), hopelessness (SI = 9.1 %, SA = 5.0 %), and age (SI = 7.1 %, SA = 3.2 %) were more closely associated with suicidal ideation than suicide attempts. Factors related to the schooling status (SI = 3.5 %, SA = 10.1 %), total years of education (SI = 2.6 %, SA = 8.6 %), and loneliness (SI = 2.3 %, SA = 7.4 %) were relatively more important in the suicide attempt stage compared to suicidal ideation. The cross-sectional design limited the ability to capture changes in suicidal behavior among depressed adolescents over time. Possible bias may exist in the measurement of suicidal ideation. The relative importance of each risk factor for suicidal ideation and attempted suicide varies. These findings provide further empirical evidence for understanding suicide behavior. Targeted treatment measures should be taken for different stages of suicide in clinical interventions. • Factors associated with suicidal ideation and suicide attempts share both similarities and differences. • Severity of depression is the most important risk factor for both suicidal ideation and suicide attempts. • Academic stress, level of hopelessness, and age, have a more pronounced relation with the occurrence of suicidal ideation. • Schooling status, total years of education, and level of loneliness exert a stronger association with suicide attempts. [ABSTRACT FROM AUTHOR]

Background: Uterine leiomyoma (UL) is one of the most common benign tumors in women, and its incidence is gradually increasing in China. The clinical complications of UL have a negative impact on women's health, and the cost of treatment poses a significant burden on patients. Diagnosis-related groups (DRG) are internationally recognized as advanced healthcare payment management methods that can effectively reduce costs. However, there are variations in the design and grouping rules of DRG policies across different regions. Therefore, this study aims to analyze the factors influencing the hospitalization costs of patients with UL and optimize the design of DRG grouping schemes to provide insights for the development of localized DRG grouping policies. Methods: The Mann–Whitney U-test or the Kruskal–Wallis H-test was employed for univariate analysis, and multiple stepwise linear regression analysis was utilized to identify the primary influencing factors of hospitalization costs for UL. Case combination classification was conducted using the exhaustive chi-square automatic interactive detection (E-CHAID) algorithm within a decision tree framework. Results: Age, occupation, number of hospitalizations, type of medical insurance, Transfer to other departments, length of stay (LOS), type of UL, admission condition, comorbidities and complications, type of primary procedure, other types of surgical procedures, and discharge method had a significant impact on hospitalization costs (P< 0.05). Among them, the type of primary procedure, other types of surgical procedures, and LOS were the main factors influencing hospitalization costs. By incorporating the type of primary procedure, other types of surgical procedures, and LOS into the decision tree model, patients were divided into 11 DRG combinations. Conclusion: Hospitalization costs for UL are mainly related to the type of primary procedure, other types of surgical procedures, and LOS. The DRG case combinations of UL based on E-CHAID algorithm are scientific and reasonable. [ABSTRACT FROM AUTHOR]

Purpose: High hepatitis B infection rates in China are a major public health issue, and mother-to-child transmission (MTCT) is a significant risk factor. Patients and Methods: This study was conducted with a prospective multicentre design from January 2021 to December 2022 in 245 hospitals providing midwifery services in southeastern China. The participants were pregnant women who were positive for hepatitis B surface antigen (HBs Ag) and their children. The HBs Ag concentration was tested in children aged 8– 12 months. The odds ratio for each risk factor was calculated by logistic regression analysis, and the decision tree model was used. Results: A total of 5369 children born to hepatitis B-infected mothers between 8 and 12 months of age were enrolled, among whom 81 (1.51%) were positive for HBsAg. The risk factors for hepatitis B infection in 5369 children under one-year-old were a high intrauterine hepatitis B exposure level, a history of hepatitis B immunoglobulin (HBIG) delay beyond 12 hours after birth, and lack of full hepatitis B vaccine (HepB), with risks of 3.356 (1.223~9.209), 5.691 (1.931~16.773), and 5.137 (2.265~11.650), respectively. The discrimination accuracy of the decision tree was 98.5%. The risk factors for hepatitis B infection in 4542 children under one year old with high exposure risk were nonstandard treatment by the mother during pregnancy, HBIG delay beyond 12 hours after birth, and no complete HepB administration, with risks of 2.925 (1.063– 8.047), 5.354 (1.806– 15.871) and 5.147 (2.258– 11.733), respectively. The discrimination accuracy of the decision tree was 98.3%. Conclusion: To prevent mother-to-child transmission of hepatitis B, it is necessary to standardize the treatment of pregnant women with a high exposure risk of hepatitis B, implement combined vaccination within 12 hours of birth, and standardise the full course of HepB. [ABSTRACT FROM AUTHOR]

We used tree-ring width data of Larix gmelinii and Pinus sylvestris var. mongolica from the northern region of the Daxing'an Mountains, China; normalized difference vegetation index (NDVI) data; and microtopographic information (elevation, slope direction, slope gradient, and topographic location index) to assess spatiotemporal dynamics in the growth of the boreal forest and topographic patterns of forest decline under the background of climate warming. Forest growth trends were determined based on tree growth decline indicators and NDVI time series trends, and topographic patterns of forest decline were analyzed using the C5.0 decision tree model. More climatic information was present in the radial growth of the trees at higher elevations, and P. sylvestris var. mongolica was influenced strongly by climatic factors of the previous year. Since 1759, tree radial growth trends in the study area have experienced two recessions during 1878–1893 and 1935–1943, which were characterized by persistent narrow whorls of tree rings of below-average growth. Changes in NDVI and tree-ring information were similar, and they together indicate a high risk of declining forest growth in the northern Daxing'an Mountains after 2010, especially at higher elevations. The NDVI time series showed that the high temperatures in 2003 negatively affected forest growth in the study area, which was confirmed by the tree-ring data. The decision tree terrain model results had an accuracy of 0.861, and elevation was the most important terrain factor affecting forest decline. The relative importance of elevation, topographic position index, aspect, and slope was 58.41%, 17.70%, 16.81%, and 7.08%, respectively. Classification rule-based decision tree models can be used to quantify the effects of terrain factors on tree growth. This research methodology can aid the management of regional forestry resources and the conservation of forest resources under the background of climate change, which increases the risk of forest decline. [ABSTRACT FROM AUTHOR]

Recently, the rise in student numbers has led to the establishment of many new colleges and universities in China. As a result, there has been a significant increase in data collection on students' athletic skills. To manage these data, educational institutions are implementing information management systems. However, tracking sports results remain challenging since sports information may not always be collected during sports teaching. This work presents a systematic strategy to address this problem by evaluating student athletes' abilities using an Apriori algorithm, decision tree (DT), and association rule. It covers the processes for collecting data, preprocessing, selecting features, and evaluating models. The efficiency of the DT algorithm in solving classification problems, including student achievement analysis, is emphasized. The association rule algorithm is applied to figure out the correlation between students' physical fitness and their involvement in physical education. The Apriori algorithm is introduced to reduce the amount of data needed in merging item sets. Lastly, the overall architecture of the college students' physical fitness analysis system is presented. It covers the insertion of sports test scores, the calculation of total scores, and the application of DT analysis for evaluating student achievements. The process involves standardizing database information, selecting a training instance set, and determining attributes based on information gain. The efficiency of the system is evaluated in terms of accuracy, precision, recall, and F1 score. In comparison with previous works, our recommended system can track and analyze students' athletic capability, fitness, and physical ability to create personalized workout routines and monitor their health in real-time. [ABSTRACT FROM AUTHOR]

Software effort estimation has constituted a significant research theme in recent years. The more important provocation for project managers concerns reaching their targets within the fixed time boundary. Machine learning strategies can lead software management to an entire novel stage. The purpose of this research work is to compare an optimized long short-term memory neural network, based on particle swarm optimization, with six machine learning methods used to predict software development effort: K-nearest neighbours, decision tree, random forest, gradient boosted tree, multilayer perceptron, and long short-term memory. The process of effort estimation uses five datasets: China and Desharnais, for which outputs are expressed in person-hours; and Albrecht, Kemerer, and Cocomo81, for which outputs are measured in person-months. To compare the accuracy of these intelligent methods four metrics were used: mean absolute error, median absolute error, root mean square error, and coefficient of determination. For all five datasets, based on metric values, it was concluded that the proposed optimized long short-term memory intelligent method predicts more accurately the effort required to develop a software product. Python 3.8.12 programming language was used in conjunction with the TensorFlow 2.10.0, Keras 2.10.0, and SKlearn 1.0.1 to implement these machine learning methods. [ABSTRACT FROM AUTHOR]

Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin'an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of ≥0.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (<1.2 m), a lower NDVI (<0.70), a heavier MDR (>115 mm), or a shorter DTRD (<500 m) were predicted to be landslide-prone. Information on forest cover is essential for predicting landslides in areas with rich forest cover, and conversion from natural forests to plantations could increase landslide risk. Across the study areas, the northwestern part was the most landslide-prone. In terms of landslide prevention, the RF model-based map produced the most accurate predictions for the "very high" category of landslide. These results will help us better understand landslide occurrences in forest-covered areas and provide valuable information for governments in designing disaster mitigation. [ABSTRACT FROM AUTHOR]

• Vaccinating Chinese females against cytomegalovirus could be cost-effective. • Efficacy type is critical for optimizing age-specific vaccination. • Pre-infection vaccine optimization is sensitive to vaccine price. The global burden of disease caused by congenital cytomegalovirus (CMV) infection is high. Previous modeling studies have suggested that CMV vaccination may be cost-effective in developed countries. Congenital CMV infection is more likely driven by maternal non-primary infection in China. We aimed to measure the effectiveness and cost-effectiveness of population-level CMV vaccination in Chinese females. A decision tree Markov model was developed to simulate potential CMV vaccination strategies in a multi-cohort setting, with a population size of 1,000,000 each for the infant, adolescent (10-year-old) and young adult (20-year-old) cohorts. The hypothetical vaccines were assumed to have 50% efficacy, 20 years of protection, 70% coverage, at a price of US$120/dose for base-case analysis. Costs and disability-adjusted life years (DALYs) were discounted by 3% per year and the vaccination would be considered cost-effective if an incremental cost-effectiveness ratio (ICER) was lower than 2021 Chinese per capita GDP (US$12,500). For the pre-infection (PRI) vaccine efficacy type, the adolescent strategy was the most cost-effective, with an ICER of US$12,213 (12,134 to 12,291) pre DALY averted, compared with the next best strategy (young adult strategy). For pre- and post-infection (P&PI) efficacy type, the young adult strategy was the most cost-effective as it was cost-saving. In one-way analysis varying the PRI vaccine price, the infant strategy, adolescent strategy and the young adult strategy would be a dominant strategy over others if the vaccine cost ≤US$60, US$61–121 and US$122–251 per dose respectively. In contrast, the young adult strategy continued to be the preferred strategy until the P&PI vaccine price exceeded US$226/dose. Our main results were robust under a wide variety of sensitivity analyses and scenario analyses. CMV vaccination for females would be cost-effective and even cost-saving in China. Our findings had public health implications for control of CMV diseases. [ABSTRACT FROM AUTHOR]

Assessing Land Use and Land Cover Change (LULCC) related with aquaculture areas is vital for evaluating the impacts of aquaculture ponds on the environment and developing a sustainable aquaculture production system. Most studies analyze changes in aquaculture land in coastal areas, and little research focuses on the inland area, where the conversions between agriculture and aquaculture land is primarily driven by socioeconomic factors. This study assessed LULCC related to aquaculture areas in Qianjiang City, China, from 1990 to 2022, using multitemporal Landsat images and a combination of decision tree classifier and visual interpretation. The LULCC was analyzed by the transition matrix. Results showed that the main LULC type was farmland, which accounted for more than 70% of the study area from 1990 to 2022. The built-up and aquaculture land showed an increasing trend year by year. In contrast, there was a gradual decline in forest/grass land from 1990 to 2016, and then its area increased slightly from 2016 to 2022 due to the policy of returning farmland to forest. Water areas were mainly composed of rivers and ponds, with subtle changes during the study period. The main driving forces of LULCC in Qianjiang City were economic and policy factors, with rapid GDP growth and government policies being the dominant factors. [ABSTRACT FROM AUTHOR]

The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models. [ABSTRACT FROM AUTHOR]

At present, the problem of an aging population in China is severe. The integration of existing healthcare services with elderly care services is inefficient and cannot meet the needs of the elderly. As such, China urgently needs the concerted efforts of various social forces to cope with the increasingly serious problem of aging. In accordance with Andersen's behavioral model, a survey was conducted in Tangshan City among seniors 60 years of age and older. Using logistic regression models, decision tree models, and random forest models, we examined the factors impacting senior people's desire to choose the integrated medical care and nursing care model. The results of the three models displayed that the elderly's propensity to choose the combined medical care and nursing care model is significantly influenced by the amount of insurance, life care needs, and healthcare needs. Moreover, the study found that the willingness of the elderly in Tangshan to improve the combined medical and nursing care service system is low. The government should appeal to the community to participate in multiple developments to improve the integrated medical and nursing service system. [ABSTRACT FROM AUTHOR]

Forest fires are major disturbances in forest ecosystems. The rapid detection of the spatial and temporal characteristics of fires is essential for formulating targeted post-fire vegetation restoration measures and assessing fire-induced carbon emissions. We propose an accurate and efficient framework for extracting the spatiotemporal characteristics of fires using vegetation change tracker (VCT) products and the Google Earth Engine (GEE) platform. The VCT was used to extract areas of persistent forest and forest disturbance patches from Landsat images of Xichang and Muli, Liangshan prefecture, Sichuan province in southwestern China and Huma, Heilongjiang province, in northeastern China. All available Landsat images in the GEE platform in a year were normalized using the VCT-derived persisting forest mask to derive three standardized vegetation indices (normalized burn ratio (NBRr), normalized difference moisture index (NDMIr), and normalized difference vegetation index (NDVIr)). Historical forest disturbance events in Xichang were used to train two decision trees using the C4.5 data mining tool. The differenced NBRr, NDMIr, and NDVIr (dNBRr, dNDMIr, and dNDVIr) were obtained by calculating the difference in the index values between two temporally adjacent images. The occurrence time of disturbance events were extracted using the thresholds identified by decision tree 1. The use of all available images in GEE narrowed the disturbance occurrence time down to 16 days. This period was extended if images were not available or had cloud cover. Fire disturbances were distinguished from other disturbances by comparing the dNBRr, dNDMIr, and dNDVIr values with the thresholds identified by decision tree 2. The results showed that the proposed framework performed well in three study areas. The temporal accuracy for detecting disturbances in the three areas was 94.33%, 90.33%, and 89.67%, the classification accuracy of fire and non-fire disturbances was 85.33%, 89.67%, and 83.67%, and the Kappa coefficients were 0.71, 0.74, and 0.67, respectively. The proposed framework enables the efficient and rapid extraction of the spatiotemporal characteristics of forest fire disturbances using frequent Landsat time-series data, GEE, and VCT products. The results can be used in forest fire disturbance databases and to implement targeted post-disturbance vegetation restoration practices. [ABSTRACT FROM AUTHOR]

Currently, the monitoring of rocky desertification(RD) is concentrated in the karst area, whereas the study in red-bed areas is rare. In this paper, we present a multi-scale classification framework for RD monitoring based on spectral-spatial features. At the pixel scale, we explored several spectral indices based on spectral statistics and separability analysis of land cover samples. The homogeneous land covers were classified by the decision rules from the selected spectral indices (such as NDIOI, NRRI and NDGI); At the patch scale, RD classes were further distinguished by spatial decision rules based on multiple neighborhood features including proximity, linear density, and buffer distance. The method was applied on an OLI image over the red-bed area of northwestern Jiangxi, south central China, and validated using ground-based observations. The experimental results of verification and comparison are satisfactory. This work demonstrates a methodological supplement to the monitoring of red bed RD. [ABSTRACT FROM AUTHOR]

Educational data mining (DEM) provides valuable educational information by applying data mining tools and techniques to analyze data at educational institutions. In this paper, tree-based machine learning algorithms are used to predict students' overall academic performance in their bachelor's program. The transcript data of the students in the same department in a Chinese university were collected. All the courses in the bachelor's program were then divided into six typical categories, and the mean GPAs of each category were taken as primary input features for prediction. Three tree-based machine learning models were established, i.e. decision tree (DT), Gradient boosting decision tree (GBDT) and random forest (RF). Results show that we can successfully identify more than 80% of the students at low-performance risk using the RF model at the end of the second semester, which is meaningful because the global quality of teaching and learning of the department can be improved by taking targeted measures in time according to the machine learning model. Feature importance and the structure of decision tree were also analyzed to extract knowledge that is valuable for both students and teachers. The results of this case study can be used as a reference for other engineering departments in China. [ABSTRACT FROM AUTHOR]

Eliminating poverty is the primary goal of sustainable development. China has eliminated absolute poverty in 2020, yet there is a chance that it could happen again. The poor population is mostly concentrated in ecologically fragile areas. We need to take more inclusive and effective initiatives to prevent the population in ecologically fragile areas from returning to poverty. In this study, a decision tree and logistic regression model were used to assess the risk of returning to poverty in Karst ecologically fragile areas. The data comes from 303 households in four counties in Guizhou and Guangxi. There are 12 main influencing factors identified, with the percentage of workforce numbers and loans having interactive effects. The results show that: (1) Poor resilience of livelihood assets, external shocks, and the effects of some support measures will be visible after a long period, leading to "transient" poverty and return to poverty. (2) Ecological environment management in ecologically fragile areas is very important to solve the problem of returning to poverty. (3) Appropriate loans can reduce poverty, especially when loans are used to cultivate a new excellent labor force. At the same time, it is necessary to evaluate farmers' repayment ability reasonably and scientifically to reduce the risk of returning to poverty. The combination of ecological restoration and agricultural development is the key to solving ecological and social problems in Karst areas. Efforts should be made to improve the risk-resilience of farmers' livelihood assets and the efficiency of livelihood assets utilization by implementing targeted support measures. This research provides a new approach to studying the mechanism of poverty recurrence, which is of great practical significance for consolidating the results of poverty eradication and realizing rural vitalization. [ABSTRACT FROM AUTHOR]

With the successful launch of China's high spatial resolution satellite Gaofen-2 (GF-2), the use of high spatial resolution satellite images for land change detection has high research potential. Based on the images from GF-2, this study combines principal component analysis and the spectral feature change method to identify different land changes in the form of different coloured patches. Then, three decision tree classification models are constructed to automatically detect the change, which includes information on the increase in the number of airports and buildings and increased or decreased vegetation. Further, through Quick Bird images for identical regions in the same periods, a sample of 2624 pixels is selected using a stratified random sampling method to verify the accuracy of the results indicating a change. The results show that the overall accuracy of the extracted information on land change was 98.21%, and the Kappa coefficient was 0.9604. Therefore, the method for land change detection and extraction of land change information used in this study is proven to be effective. [ABSTRACT FROM AUTHOR]

The performance evaluation is one of the most important organizational management strategies used to guide the sales behavior of sales staff. However, it should be work process-oriented or sales result-oriented has become a dilemma for the management when evaluating employees' performance. Therefore, comprehensively evaluating the work process and sales results has become a challenge when it comes to salespeople performance appraisal. To solve this dilemma, this research builds a salesperson performance evaluation model which considering both of work effciency and effectiveness, based on the decision tree model. Specifically, we use the Data Envelopment Analysis (DEA) model to quantify the work effciency of salespeople and measure the work effectiveness by amount of sales. Moreover, this research proposes an advanced integrated DEA model by integrating the self-evaluation DEA models, peer-evaluation DEA models, and Gini impurity, which is identified to be more stable compared with the current DEA model. Finally, a case study of a Chinese liquor company is introduced to illustrate the applicability and feasibility of the salesperson performance evaluationmodel. The proposedmodel is applied to evaluate the performance of the salespeople, and a set of comprehensive and objective sales performance evaluation results are obtained. The estimated results can provide feasible salesmanagement suggestions for the company in diagnosing work problems of salespeople. [ABSTRACT FROM AUTHOR]

Background: Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).Methods: This study enrolled 2470 hospitalized patients with BD (n = 1333) or MDD (n = 1137) at reproductive age from 2009 to 2018 in China. We extracted 8 parameters, uric acid (UA), direct bilirubin (DBIL), indirect bilirubin (IDBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), high-density lipoprotein (HDL) and prealbumin of male, patients and 12 parameters, UA, DBIL, IBIL, LDH, FT3, TSH, glutamic-pyruvic transaminase (GPT), white blood cell (WBC), alkaline phosphatase (ALP), fasting blood glucose (FBG), triglyceride and low-density lipoprotein (LDL) of female patients. Backward stepwise multivariate regression analysis and the Chi-Square Automatic Interaction Detection (CHAID) segmentation analysis via SPSS Decision Tree were implemented to define the discrimination of BD and MDD.Results: DBIL was extracted as the first splitting variable, with LDH and IBIL as the second, TSH and prealbumin as the third in the model of male patients (p-value < .05). For the model of female patients, DBIL was also extracted as the first splitting variable, with UA, LDH, and IBIL as the second, triglyceride and FT3 as the third (p-value < .05). The predictive accuracies of the Decision Tree and multiple logistic regression models were similar (74.9% vs 76.9% in males; 74.4% vs 79.5% in females).Conclusions: This study suggests the value of the Decision Tree models, which employ biochemical parameters as diagnostic predictors for BD and MDD. The CHAID Decision Tree identified that patients with concomitantly increased LDH, IBIL, and decreased DBIL could be in the group that showed the highest risk of being diagnosed as BD. [ABSTRACT FROM AUTHOR]

To meet the demand of the fast increasing population, enhancing the wheat (Triticum aestivum L.) yield and resource use efficiency by optimizing water and nitrogen (N) management can greatly improve agricultural sustainability and enhance regenerative farming in developing countries such as China. Based on 126 studies conducted in China between 1996 and 2018, using meta-analysis in combination with decision regression tree modeling and life cycle assessment (LCA), this study aimed to (1) quantify the effect of water and N input on wheat yield, water productivity (WP c ), and N use efficiency (NUE f ), and evaluate the subsequent environmental impact in different regions using LCA; and (2) evaluate, model, and rank the roles of environmental (e.g., soil nutrient status and climatic factors) and agronomic factors (e.g., water and N management practices) affecting wheat yield, WP c , and NUE f . The results showed that irrigation and N addition increased the average yield and WP c by 40 and 15%, respectively, relative to control treatments with no irrigation or fertilizer application. The mean water saving potential (WSP) and N saving potential (NSP) in China were estimated at 11 and 10%, respectively. Soil nutrient status [e.g., initial soil phosphorus (P) and potassium (K)] and soil organic carbon content affected the wheat yield, WP c , and NUE f more significantly than climatic factors [mean annual temperature (MAT)] or water and N management practices. The structural equation-based modeling indicated that initial soil nutrient condition impacted productivity and resource use efficiency more at the below optimal water and N levels than above. The risk-factor-based feature ranking indicated that site-specific environmental and soil condition was highly informative toward model construction but split input of N or water had less impact on yield and input use efficiency. LCA demonstrated that to further mitigate greenhouse gas emissions, water- or N-saving management should be promoted in China. Collectively, our research implies that long-term soil health and nutrient enhancement should be more beneficial for increasing yield and resource use efficiency in wheat production. [ABSTRACT FROM AUTHOR]

The brain seems to be the most complex organ in the human body and operates as the central part of the nervous system. The classification of brain tumors is the most complicated problem which medical experts face. In our proposed work, the novelty is that we have adopted the hybridization of feature exaction techniques and a conglomerate of classification methods. The hybrid features are extracted from the tumor region using the Gray Level Co-occurrence Matrix technique and shape-based features. There are three different types of brain magnetic resonance imaging datasets used in work. For the segmentation, the Marker-controlled watershed segmentation strategy is implemented in this work. In this work, the hybrid classifier, which includes the Random Forest (RF) classifier, K-Nearest Neighbour classifier, and Decision Tree classifier, is implemented. Dataset 1 (China Hospital data) shows a better overall accuracy of 98.75%; for dataset 2, accuracy is 98.99%; and for dataset 3, the achieved accuracy is 98.92%. The other parameters that prove the overall work's efficacy include sensitivity, precision, specificity, F1-score, Cohen's kappa value, Dice coefficient, Jaccard Index, and area under the curve (AUC). The proposed work is also compared with the state-of-the-art methods and observed better one. [ABSTRACT FROM AUTHOR]

Forty-one apple samples from 7 geographical regions and 3 botanical origins in China were investigated. A total of 29 volatile compounds have been identified by flash GC E-nose. They are 17 esters, 5 alcohols, 3 aldehydes, 1 ketone, and 3 others. A principal component analysis was employed to study the relationship between varieties and volatiles. A partial least squares discriminant analysis (PLS-DA), stepwise linear discriminant analysis (SLDA), and decision tree (DT) are used to discriminate apples from 4 geographical regions (34 apple samples) and 3 botanical origins (36 apple samples). The most influential markers identified by PLS-DA are 2-hexadecanone, methyl decanoate, tetradecanal, 1,8-cineole, hexyl 2-butenoate, (Z)-2-octenal, methyl 2-methylbutanoate, ethyl butyrate, dimethyl trisulfide, methyl formate, ethanol, S(-)2-methyl-1-butanol, ethyl acetate, pentyl acetate, butyl butanoate, butyl acetate, and ethyl octanoate. From the present work, SLDA reveals the best discrimination results in geographical regions and botanical origins, which are 88.2% and 88.9%, respectively. Although machine learning DT is attempted to classify apple samples, the results are not satisfactory. [ABSTRACT FROM AUTHOR]

Predicting secondary school students' geospatial thinking ability can provide targeted guidance for teachers. To date, few scholars have focused on predicting students' geospatial thinking ability. In this paper, we address this gap by constructing a prediction model based on the decision tree algorithm, to predict the geospatial thinking ability of secondary school students. A total of 1029 secondary school students were surveyed using the Spatial Thinking Ability Test, the Students' Geography Learning Status Questionnaire, and the Middle Students Motivation Test. Our model indicates that geospatial thinking ability can be predicted by nine factors, in order of importance: academic achievement in geography, geography learning strategy, geography classroom environment, gender, learning initiative, learning goals, extracurricular time spent learning geography, ego-enhancement drive, and interest in learning geography. The model accuracy is 81.25%. Specifically, our study is the first to predict geospatial thinking ability. It provides a tool for teachers that can help them identify and predict students' geospatial thinking ability, which is conducive to designing better teaching plans and making adjustments to the curriculum. [ABSTRACT FROM AUTHOR]

Background: Many previous studies have investigated the risk factors associated with eating disorders (EDs) from the perspective of emotion regulation (ER). However, limited research has investigated interactions between co-existing risk factors for EDs, especially in China where research in EDs is underrepresented. Methods: This study examined core risk factors related to maladaptive eating behaviors and ER, and how their interactions affect the detection of EDs. Using machine learning, a decision tree model was constructed on a data set of 830 non-clinical Chinese young women with an average age of 18.91 years (SD = 0.95). The total data set was split into training and testing data sets with a ratio of 70 to 30%. Results: Body image inflexibility was identified as the major classifier for women at high risk of EDs. Furthermore, interactions between body image inflexibility, psychological distress, and body dissatisfaction were important in detecting women at high risk of EDs. Overall, the model classifying women at high-risk for EDs had a sensitivity of 0.88 and a specificity of 0.85 when applied to the testing data set. Conclusions: Body image inflexibility, psychological distress, and body dissatisfaction were identified as the major classifiers for young women in China at high risk of EDs. Researchers and practitioners may consider these findings in the screening, prevention, and treatment of EDs among young women in China. Plain English summary: Previous studies have identified multiple risk factors of eating disorders that are related to emotion regulation and coping strategies in the Western context. However, most of these studies failed to describe any kind of hierarchy or interaction between risk factors that co-occur. To address this knowledge gap, the present study investigated a broad range of risk factors from the perspective of emotion regulation and then used a decision tree classification method to screen for EDs among young women in China. Results showed that body image inflexibility, psychological distress, and body dissatisfaction were the primary classifiers for Chinese women at high risk of EDs. [ABSTRACT FROM AUTHOR]

Background: Early accurate diagnosis and risk assessment for malaria are crucial for improving patients' terminal prognosis and preventing them from progressing to a severe or critical stage. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics and the factors that affect the accuracy in the context of the agenda for a world free of malaria. Methods: A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated, and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed. Results: Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level and provincial-level hospitals were 27.5% (n = 136), 26.3% (n = 130) and 8.3% (n = 41), respectively; the proportions of patients seeking care in clinic, township health centre and Centres for Disease Control and Prevention were 25.9% (n = 128), 4.1% (n = 20), and 7.9% (n = 39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% had complications. The median time from onset to the first visit was 2 days (IQR: 0-3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0–4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age. Conclusions: Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. To reduce diagnostic errors, clinicians, government decision-makers and communities should consider strengthening the primary care facilities, the time interval between onset and initial diagnosis, residence type, and health insurance status. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naïve bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms. [ABSTRACT FROM AUTHOR]

At present, China's cross-border e-commerce has ushered in a golden period of development. When developing cross-border e-commerce, enterprises should first assess the market climate of the target country and reasonably select the target country. Based on the PESTEL theory, an evaluation index system is established for China's cross-border e-commerce overseas strategic climate. Taking "One Belt, One Road" as the opportunity and background, the overseas strategic climate of cross-border e-commerce in 62 countries along the "One Belt, One Road" is selected as the research object, and the Decision Tree and Adaptive Boosting classification methods in machine learning are applied to train and predict the established index system. Finally an overall picture of the overseas strategic climate of the 62 countries is obtained. The results are compared and analysed in depth to identify the most suitable countries for cross-border e-merchants and to provide reference for cross-border e-merchants investors. [ABSTRACT FROM AUTHOR]

Food production requires a large amount of water. As a country facing a serious scarcity of per capita water resources and severe water pollution, China must explore the spatial distribution characteristics of its dietary water footprint. China is the world's largest developing country, and water consumption inevitably has increased with its economic development. It is essential to explore the factors influencing the water footprint and water conservation mechanisms. Based on China Health and Nutrition Survey (CHNS) data, individual-level data of dietary water footprint and residents' socio-economic characteristics were obtained. The decision tree was applied to classify the dietary water footprint based on socio-economic factors, and multinomial logistic regression was then performed to investigate the influence of each factor. The results showed that all six selected socio-economic factors had a statistically significant impact on the dietary water footprint. Income and education level were positively related to the dietary water footprint; urban residents, males, and residents with a higher body mass index (BMI) consumed more dietary water than rural residents, females, and those with a lower BMI, respectively. Age exhibited an inverted U-shaped influence. Understanding the drivers and disparities of the water footprint of food consumption can support the development of policy for energy conservation, which can ultimately help achieve the goal of reducing water waste. [ABSTRACT FROM AUTHOR]

Background: Cognitive dysfunction is one of the leading causes of disability and dependence in older adults and is a major economic burden on the public health system. The aim of this study was to investigate the risk factors for cognitive dysfunction and their predictive value in older adults in Northwest China., Methods: A cross-sectional study was conducted using a multistage sampling method. The questionnaires were distributed through the Elderly Disability Monitoring Platform to older adults aged 60 years and above in Northwest China, who were divided into cognitive dysfunction and normal cognitive function groups. In addition to univariate analyses, logistic regression and decision tree modelling were used to construct a model to identify factors that can predict the occurrence of cognitive dysfunction in older adults., Results: A total of 12,494 valid questionnaires were collected, including 2617 from participants in the cognitive dysfunction group and 9877 from participants in the normal cognitive function group. Univariate analysis revealed that ethnicity, BMI, age, educational attainment, marital status, type of residence, residency status, current work status, main economic source, type of chronic disease, long-term use of medication, alcohol consumption, participation in social activities, exercise status, social support, total scores on the Balanced Test Assessment, total scores on the Gait Speed Assessment total score, and activities of daily living (ADL) were significantly different between the two groups (all P < 0.05). According to logistic regression analyses, ethnicity, BMI, educational attainment, marital status, residency, main source of income, chronic diseases, annual medical examination, alcohol consumption, exercise status, total scores on the Balanced Test Assessment, and activities of daily living (ADLs) were found to influence cognitive dysfunction in older adults (all P < 0.05). In the decision tree model, the ability to perform activities of daily living was the root node, followed by total scores on the Balanced Test Assessment, marital status, educational attainment, age, annual medical examination, and ethnicity., Conclusions: Traditional risk factors (including BMI, literacy, and alcohol consumption) and potentially modifiable risk factors (including balance function, ability to care for oneself in daily life, and widowhood) have a significant impact on the increased risk of cognitive dysfunction in older adults in Northwest China. The use of decision tree models can help health care workers better assess cognitive function in older adults and develop personalized interventions. Further research could help to gain insight into the mechanisms of cognitive dysfunction and provide new avenues for prevention and intervention., (© 2024. The Author(s).)

Introduction: China has the world's largest diabetes epidemic and has been facing a serious shortage of primary care providers for chronic diseases including diabetes. To help primary care physicians follow guidelines and mitigate the workload in primary care communities in China, we developed a guideline-based decision tree. This study aimed to validate it at 3 months with real-world data. Methods: The decision tree was developed based on the 2017 Chinese Type 2 Diabetes (T2DM) guideline and 2018 guideline for primary care. It was validated with the data from two registry studies: the NEW2D and ORBIT studies. Patients' data were divided into two groups: the compliance and non-compliance group, depending on whether the physician's prescription was consistent with the decision tree or not. The primary outcome was the difference of change in HbA1c from baseline to 3 months between the two groups. The secondary outcomes included the difference in the proportion of patients achieving HbA1c < 7% at 3 months between the two groups, the incidence of self-reported hypoglycemia at 3 months, and the proportion of patients (baseline HbA1c ≥ 7%) with a HbA1c reduction ≥ 0.3%. The statistical analysis was performed using linear or logistic regression with inverse probability of treatment weighting with adjustments of confounding factors. Results: There was a 0.9% reduction of HbA1c in the compliance group and a 0.8% reduction in the non-compliance group (P < 0.001); 61.1% of the participants in the compliance group and 44.3% of the participants in the non-compliance group achieved a HbA1c level < 7% at 3 months (P < 0.001). The hypoglycemic events occurred in 7.1% of patients in the compliance group vs. 9.4% in the non-compliance group (P < 0.001). Conclusion: The decision tree can help physicians to treat their patients so that they achieve their glycemic targets with fewer hypoglycemic risks. (http://www.clinicaltrials.gov NCT01525693 & NCT01859598). [ABSTRACT FROM AUTHOR]

Background: Nonalcoholic fatty liver disease (NAFLD) is the commonest form of chronic liver disease worldwide and its prevalence is rapidly increasing. Screening and early diagnosis of high-risk groups are important for the prevention and treatment of NAFLD; however, traditional imaging examinations are expensive and difficult to perform on a large scale. This study aimed to develop a simple and reliable predictive model based on the risk factors for NAFLD using a decision tree algorithm for the diagnosis of NAFLD and reduction of healthcare costs. Methods: This retrospective cross-sectional study included 22,819 participants who underwent annual health examinations between January 2019 and December 2019 at Physical Examination Center in Shengjing Hospital of China Medical University. After rigorous data screening, data of 9190 participants were retained in the final dataset for use in the J48 decision tree algorithm for the construction of predictive models. Approximately 66% of these patients (n=6065) were randomly assigned to the training dataset for the construction of the decision tree, while 34% of the patients (n=3125) were assigned to the test dataset to evaluate the performance of the decision tree. Results: The results showed that the J48 decision tree classifier exhibited good performance (accuracy=0.830, precision=0.837, recall=0.830, F-measure=0.830, and area under the curve=0.905). The decision tree structure revealed waist circumference as the most significant attribute, followed by triglyceride levels, systolic blood pressure, sex, age, and total cholesterol level. Conclusion: Our study suggests that a decision tree analysis can be used to screen high-risk individuals for NAFLD. The key attributes in the tree structure can further contribute to the prevention of NAFLD by suggesting implementable targeted community interventions, which can help improve the outcome of NAFLD and reduce the burden on the healthcare system. [ABSTRACT FROM AUTHOR]

Background: Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide. Methods: A total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video‐assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm. Results: The combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient. Conclusions: Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population. [ABSTRACT FROM AUTHOR]

The stipulation of the COVID-19 (Corona Virus Disease 2019) as a global pandemic by the WHO (World Health Organization) made a number of countries lockdown. Countries like Italy, Denmark, China, and Ireland have taken lockdown steps to prevent this disease from spreading and taking many lives. COVID-19, SARS (Severe Acute Respiratory Syndrome), and MERS (Middle-East Respiratory Syndrome) are viral infections in the respiratory tract that can be fatal. SARS first became an epidemic in China in 2002, while MERS first appeared in the Middle East in 2012. At the end of 2019, a new disease appeared in China called COVID- 19. These three viruses are still in the same family so they have very similar nucleotide sequences. The tested COVID-19 primer was able to adhere well with a similarity level of more than 70% in all DNA SARS and MERS isolates tested. To distinguish DNA samples between MERS, SARS, and COVID-19 using the basic local alignment sequence nucleotide approach alone is not enough. We propose an optimization of machine learning methods to predict the COVID-19, the optimization method depends on the method we improved. In Discriminant Analysis, we use Wilks Lamda's approach and change Linear into Diagonal Discriminant Matrix. In the Decision Tree method, we make optimization by making gain formulation to minimize the entropy value to get more information on the result. We optimized K-NN with add weighted distance optimization, and in SVM we try several kernels and optimize the hyperplane with SRM (Structural Risk Minimization) approach to looking for the best result. Besides that, in preparation for input features, we use Edit Levenshtein Method with the calculation of the optimum similarity from each DNA sequence. The results of our test, optimization of the Decision Tree method produces an accuracy of 98.3%, optimization of Discriminant Analysis 98.3%, and optimization of SVM and KNN 100% respectively. We also find a fact in the DNA Alignment process, when the primer being compared is 'R', the nucleotides in the COVID-19 sample data are always 'A' and this approach from the bioinformatic side can be used as analytical material in the medical world. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with type 2 diabetes (T2DM). Carotid intima-media thickness (CIMT) is considered a preclinical stage of atherosclerosis. Therefore, it is necessary to identify the related risk factors for CIMT to facilitate the early prevention of CVD. Previous studies have shown that visceral fat area (VFA) is a risk factor for T2DM and CVD. However, few studies have focused on the effects of VFA on CIMT associated with T2DM. Moreover, considering that the body fat distribution shows regional and racial heterogeneity, the purpose of this study was to investigate the predictive value of VFA and other risk factors for CIMT associated with T2DM in Western China. Methods: In a cross-sectional study, a total of 1372 patients with T2DM were divided into the CIMT (−) group (n = 965) and the CIMT (+) group (n = 407) based on CIMT values. In addition to the univariate analyses, logistic regression analysis and a decision tree model were simultaneously performed to establish a correlation factor model for CIMT. Results: Univariate analyses showed that sex, smoking status, age, heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, weight, body mass index (BMI), waist circumference, hip circumference, waist-hip ratio, VFA, subcutaneous fat area, and the levels of 2-h C-peptide, serum creatinine, urea nitrogen and uric acid were significantly different between the two groups (all p < 0.05). Smoking, increased VFA, female sex and increased BMI were risk factors in the logistic regression analyses (OR = 5.759, OR = 1.364, OR = 2.239, OR = 1.186, respectively). In the decision tree model, smoking was the root node, followed by sex, waist circumference, VFA and chronic kidney disease (CKD) in order of importance. Conclusions: In addition to smoking, sex and BMI, VFA has a significant effect on CIMT associated with T2DM in the Chinese Han population in Western China. In addition, the decision tree model could help clinicians make more effective decisions, with its simplicity and intuitiveness, making it worth promoting in future medical research. Trial registration ChiCTR, ChiCTR1900027739. Registered 24 November 2019-Retrospectively registered, http://www.chictr.org.cn/index.aspx. [ABSTRACT FROM AUTHOR]

Seasonal prediction of extreme precipitation has long been a challenge especially for the East Asian Summer Monsoon region, where extreme rains are often disastrous for the human society and economy. This paper introduces a decision‐tree (DT) method for predicting extreme precipitation in the rainy season over South China in April–June (SC‐AMJ) and the North China Plain in July–August (NCP‐JA). A number of preceding climate indices are adopted as predictors. In both cases, the DT models involving ENSO and NAO indices exhibit the best performance with significant skills among those with other combinations of predictors and are superior to their linear counterpart, the binary logistic regression model. The physical mechanisms for the DT results are demonstrated by composite analyses of the same DT path samples. For SC‐AMJ, an extreme season can be determined mainly via two paths: the first follows a persistent negative NAO phase in February–March; the second goes with decaying El Niño. For NCP‐JA, an extreme season can also be traced via two paths: the first is featured by "non El Niño" and an extremely negative NAO phase in the preceding winter; the second follows a shift from El Niño in the preceding winter to La Niña in the early summer. Most of the mechanisms underlying the decision rules have been documented in previous studies, while some need further studies. The present results suggest that the decision‐tree approach takes advantage of discovering and incorporating various nonlinear relationships in the climate system, hence is of great potential for improving the prediction of seasonal extreme precipitation for given regions with increasing sample observations. [ABSTRACT FROM AUTHOR]

With the popularization of higher education in China, a series of social problems caused by mental health problems rise frequently because of the imperfect mental quality of college students, attracting attention of the school and society. To gain insight into the main mental symptoms and factors affecting their mental health, decision tree owns an incomparable advantage, which is an important means in data mining. In this study, methods and procedures of decision tree construction were introduced and decision tree C4.5 algorithm was elaborated. In this algorithm, appropriate attributes were selected as tree roots and subtree roots, and then the decision tree was constructed through repetition. Decision tree algorithm could classify the mental health of college students precisely, analyze rules and guide mental health counselors with correct plans, which was helpful to the decision making and mental health of college students improving. [ABSTRACT FROM AUTHOR]

• A scientific mechanism (DTIB) is proposed to determine the benchmark of urban indicators. • A Deviation-Degree-Based assessment tool is proposed for urban indicator performance evaluation. • A case study of the Urban Health Examination in China is conducted to verify the effectiveness and applicability of the DTIB and Deviation-Degree-Based assessment tool. • Critical urban problems are diagnosed and the shortages of urban development are highlighted. The rapid process of urbanization has brought out a series of challenges in urban governance, which prompts an increasing number of studies focusing on monitoring and evaluating the status of the cities. However, the detailed illustration of the establishment of a scientific-made basis for setting cities' indicators benchmarks is often overlooked or inadequately explained in previous studies. Therefore, this study proposes a hybrid method to determine the benchmark for assessing cities' performance. Firstly, this study establishes a mechanism called the Decision Tree of Indicator Benchmark (DTIB) which incorporates decision tree theory and benchmark management theory for urban indicator benchmark determination. Then, a deviation-degree-based model is proposed for assessing the performance of each indicator and the AHP method is applied for attaining the comprehensive performance of the city. To verify the effectiveness of the proposed hybrid method, an empirical study is conducted, and a comparison between the proposed method with other traditional evaluation methods is made to demonstrate the advantages of the proposed method. This hybrid method can facilitate decision-makers to understand the status of a city and explore the underlying problems of urban development to design more tailored policies and plans for cities. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract With the wide application of big data technology and the continuous innovation of the financial industry, the development of Internet finance has become irreversible. The new financial model has improved the efficiency of financial services and formed new ways of payment and transaction. The traditional credit information system has been unable to meet the needs of the development of the financial industry. The research methods adopted in this paper mainly include literature research, case analysis and comparative analysis. The development process, current situation and characteristics of China's personal credit system, as well as the development process and current situation of China's personal credit system under Internet finance are analyzed. It also points out the main problems faced by China's personal credit reporting system under the Internet finance. Then, aiming at the specific problems faced by China's personal credit reporting system under Internet finance, this paper makes case studies of representative market-oriented credit reporting institutions such as Beijing, Tianjin and Hebei, and uses convolutional neural network to analyze the specific practices of these individual credit reporting institutions and achieves results. At the same time, it compares how different credit agencies make up for the shortcomings of personal credit reporting system. Finally, it draws the conclusion of perfecting the personal credit system of our country under the Internet finance, points out the new problems that may be brought by the superposition of various solutions, and puts forward the countermeasures and suggestions of perfecting the personal credit system of our country under the Internet finance. [ABSTRACT FROM AUTHOR]

Grassland ecosystems play an important role in the carbon (C) balance of arid and semi–arid regions. These ecosystems provide C for grass growth and soil microbial activities and represent one of the main sources of atmospheric C. In this study, we estimated the C density and storage of 223 sampling sites in grassland ecosystems on the Loess Plateau using elevation, vegetation indexes, precipitation, air temperature, day and night land surface temperature (LSTd and LSTn, respectively), evapotranspiration (ET), percent tree cover and the non–vegetated area to build decision regression tree and generalized linear regression models (GLMs). The results showed that the C density decreased from south to north and ranged from 0.22 to 29.29 kg C/m 2 . The average amount of C stored in the ecosystems was 1.46 Pg. The typical steppe and forest steppe stored the most C, and the steppe desert stored the least. The soil (0–1 m) stored most of the organic C, accounting for > 90%, and the belowground biomass (BGB) contained > 3 times the amount of C as the aboveground biomass (AGB). This study provides reference information for the loss of C and associated mitigation strategies on the Loess Plateau. [ABSTRACT FROM AUTHOR]

In China, the cadmium (Cd) levels in paddy fields have increased, which has led to the excessive uptake of Cd into rice grains. In this study, we determined the physicochemical properties of soil samples, including the pH, soil organic matter (SOM) content, cation exchange capacity (CEC), and total Cd content (Cd soil ) in order to establish a quadratic discriminant analysis (QDA) model for assessing the risk of Cd in rice and to calculate its prior probability. Decision tree and logistic regression models were also established for comparison. The results showed that the accuracy rate was 74% with QDA, which was significantly higher than that obtained using the decision tree (67%) and logistic regression (68%) models. The correlation coefficients between the soil pH and the other three factors (CEC, SOM, and Cd soil ) were higher in the inaccurate set than the accurate set, whereas the correlation coefficients were smaller in the inaccurate set than the accurate set. [ABSTRACT FROM AUTHOR]

Purpose - Although many disaster resettlement programs are implemented throughout the world, their ramifications continue to challenge our understanding of scope, impact, context, and nature of efforts to rebuild livelihoods. The purpose of this paper is to examine involved rural farmers' livelihood risk perceptions (LRPs) in Southern Shaanxi Disaster Resettlement based on the case of Ziyang County. Design/methodology/approach - Utilizing a livelihood risk perspective, this paper identified factors contributing to LRPs. The research categorized the involved rural farmers into groups and explored the factors affecting their LRPs using decision tree (DT). Findings - Analyzing LRPs of different groups, this paper derived influential factors and the decision rules followed by groups. Additionally, critical factors were discussed according to their relative importance for decision making and to propose some intervention measures for effectiveness. Originality/value - The results and implications enhance understanding on how local community members perceive the problem or risk in disaster resettlement. The application of a DT also provides methodological contributions for risk analysis and disaster preparedness. [ABSTRACT FROM AUTHOR]

Accurate and rapid perception of fruit bunch posture is necessary for the cherry tomato harvesting robot to successfully achieve the bunch's holding and separating. According to the postural relationship of the fruit bunch, bunch pedicel, and plant' main-stem, the robotic end-effector's holding region and approach path could be determined, which were important for successful picking operation. The main goal of this research was to propose a multitask-aware network (MTA-YOLACT), which simultaneously performed region detection on fruit bunch, and region segmentation on pedicel and main-stem. The MTA-YOLACT extended from the pre-trained YOLACT model, included two detector branch networks for detection and instance segment, which shared the same backbone network, and the loss function with weighting coefficients of the two branches was adopted to balance the multi-task learning, according to multi-task's homoscedastic uncertainty during the model training. Furthermore, in order to cluster the fruit bunch, pedicel and main-stem from the same bunch target, a classification and regression tree (CART) model was built, based on the region's positional relationship from the MTA-YOLACT output. An image dataset of cherry tomato plants in China greenhouse was built to training and test the model. The results indicated a promising performance of the proposed network, with an F1-score of 95.4% on detecting fruit bunches and the mean Average Precision of 38.7% and 51.9% on the instance segmentation of pedicel and main-stem, which was 1.1% and 3.5% more than original YOLACT. Beyond that, our approach performed a real-time detection and instance segmentation of 13.3 frames per second (FPS). The whole bunch could be identified by the CART model with an average accuracy of 99.83% and the time cost of 9.53 ms. These results demonstrated the research could be a viable support to the harvesting robot's vision unit development and the end-effector's motion planning in the future research. • A Multi-task Awareness Network (MTA-YOLACT) improved fruit bunch identification. • MTA-YOLACT detected and segmented regions on the fruit bunch at the same time. • Analysis of features satisfying subordination constraints in multi-fruit bunches. • A decision model clustered the fruit bunch, pedicel and main-stem from same bunch. • Experiments show maintain excellent at the real-time and accuracy. [ABSTRACT FROM AUTHOR]

The short-term photovoltaic (PV) power prediction is mainly used to prevent photovoltaic power generation fluctuations, grid overvoltage, reverse current, and islanding effects. At the same time, the mid-term PV power forecast is of great significance to improving the availability of PV power generation and the benefit of PV grid connection. Because of the significant differences in time scales between mid-term and short-term power prediction, the short-term power prediction model or method cannot be directly applied to mid-term power prediction. Therefore, few scholars have developed effective short-to-mid-term power forecasting strategies for photovoltaics. As a result, this research proposes a prediction model based on a fast cull outlier algorithm (FC), a decision tree (DT), least squares support vector regression (LSSVR), and an improved whale bat optimization algorithm (IWBOA), which is used for the prediction of the short-to-mid-term photovoltaic power on different time scales, one day and one month in advance. The FC algorithm is used to analyze the difference between the overall data and the abnormal data in the local density and distance to remove the outliers in the original data set. The revised DT model differentiates the data according to climate characteristics to obtain different input data subsets with the best correlation and constructs an independent LSSVR-based model corresponding to the data subsets. The WOA algorithm is improved by independently designing the Chebyshev chaotic initialization strategy, chaotic adaptive inertia weight and BOA algorithm, and the kernel function width and penalty factor of each short-to-mid-term independent sub-PV power prediction model are optimized by IWBOA, thereby improving the computational speed and prediction accuracy of the LSSVR model. Finally, the effectiveness of the proposed strategy is evaluated using actual data from photovoltaic power plants in China. The regression coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) of short-term forecasting ability of the FCDT-IWBOA-LSSVR model established in this paper are 0.99657, 0.22009, 0.32515, and 0.20959, respectively, while the R2, MSE, RMSE, and MAE of medium-term forecasting ability are 0.98374, 1.91371, 1.38336, and 0.6256. The calculation results show that the prediction accuracy of the FCDT-IWBOA-LSSVR model is higher than that of the WOA-LSSVR, PSO-LSSVR, PSO-BP, I-ACO-SVR, ANN, EMS-DSM-RT, 5 ML, and ELM models. • Short-to-mid-term photovoltaic power generation prediction model established. • The FC algorithm analyze local density and distance for data and remove outliers. • The DT model differentiates the data according to climate characteristics. • The IWBOA was used to optimize the parameters of the LSSVR prediction model. [ABSTRACT FROM AUTHOR]

Background: Diabetes is a chronic non-communicable disease that causes a substantial economic burden on diabetic suffers and their households. The aim of this study was to explore the prevalence, equity, and determinants of catastrophic health expenditure (CHE) among households with people with diabetes in Northwest China., Methods: A total of 3,000 households were included in the 6th Health services survey in Gansu Province, China of which 270 households with people with diabetes. The equity of CHE was evaluated by concentration curve and concentration index (CI). We adopted the Pareto chart to analyze the main economic intervals of the occurrence of CHE. Finally, we combined the decision tree and logistic model and analyzed the determinants of the occurrence of CHE., Results: The incidence of CHE at 15%, 25% and 40% were 75.19%, 58.89% and 35.19%, respectively. CHE tended to occur in households with a lower economic level, with the phenomenon being more pronounced at Z = 40%. The Pareto chart showed that households in the group with an annual per capita income of 0-740 USD (0-5,000 Chinese Yuan) were most likely to experience CHE. Both decision tree and logistic models suggested that economic level, comorbidities, and small household size were potential risk factors. In addition, the decision tree model also suggested the interaction between the influencing factor of health checks in the past 12 months and the number of chronic diseases., Conclusions: In summary, Households with people with diabetes were more likely to incur CHE. It is essential to focus on low- and middle-income households with people with diabetes, strengthen the management of patients with diabetes, and provide timely health interventions to reduce the occurrence of chronic comorbidity and the risk of CHE in households., (© 2023. The Author(s).)

This paper presents a decision-tree based model to forecast rail transit ridership at the station level according to the surrounding land-use patterns. The canonical correlation analysis (CCA) method is used to identify key land use variables by evaluating their degrees of contribution to the rail transit station demand, which can effectively reduce dimensionality and complexity of the decision tree. A full month of Smart Card data and detailed regulatory land use plan from Chongqing, China are collected for model development and validation. The proposed model offers the capability of targeting key lane use patterns and associating them with rail transit station boarding and alighting demand at a high level of accuracy. The proposed model can reveal underlying rules between rail transit station demand and land use variables, and can be used to assist in developing the Transit Oriented Development (TOD) plans to improve land use and transit operational efficiency. [ABSTRACT FROM AUTHOR]

Flood prediction for ungauged karst wetland is facing a great challenge. How to build a wetland hydrological model when there is a lack of basic hydrological data is the key to dealing with the above challenge. Napahai wetland is a typical ungauged karst wetland. In ungauged wetland/condition, this article used the wetland open water area (OWA) extracted from Landsat remote sensing images during 1987-2018 to characterize the hydrological characteristics of Napahai wetland. The local daily precipitation in the 1987-2018 rainy season (June-October) was used to set the variables. Based on the following hypothesis: in the rainy season, the OWA of the Napahai wetland rises when there is an increase in accumulated precipitation (AP), two data-driven models were established. The study took the area difference (AD) between two adjacent OWAs as the dependent variable, the accumulated precipitation (AP) within the acquisition time of two adjacent OWAs, and the corresponding time interval (TI) of the OWA as explanatory variables. Two data-driven models (a piecewise linear regression model and a decision tree model) were established to carry out flood forecasting simulations. The decision tree provided higher goodness of fit while the piecewise linear regression could offer a better interpretability between the variables which offset the decision tree. The results showed that: (1) the goodness of fit of the decision tree is higher than that of the piecewise linear regression model (2) the piecewise linear model has a better interpretation. When AP increased by 1 mm, the average AD increased by 2.41 ha; when TI exceeded 182 d and increased by 1 d, the average AD decreased to 3.66 ha. This article proposed an easy decision plan to help the local Napahai water managers forecast floods based on the results from the two models above. In addition, the modelling method proposed in this article, based on the idea of difference for non-equidistant time series, can be applied to karst wetland hydrological simulation problems with data acquisition difficulty., Competing Interests: The authors declare there are no competing interests., (©2023 Li and Li.)

In recent years, online Peer-to-Peer (P2P) lending market is rapidly expanding in China. In this paper, we use public dataset from PPDai, a leading online P2P platform in China to study the loan default. We construct a credit scoring model by fusing social media information based on decision tree. The experimental result shows that our model has good classification accuracy. From the credit scoring model and classification rules, we get a conclusion that the loan information, social media information, and credit information are most important factors for predicting the default. However, the credit rating is not as important as the platform described. [ABSTRACT FROM AUTHOR]

The paper is an exploratory study on the travel intentions of mainland Chinese residents to Japan after Diaoyu Island political crisis. Due to the superior location and excellent service, Japan has become one of the most popular outbound tourism destinations for Mainland Chinese tourists in the past 10 years. However, since the complicated and subtle relations between China and Japan, the tourism industry of Japan is evidently sensitive and vulnerable to various crisis. Under such circumstances, it is necessary to investigate the decision tree model research as well as compare domestic and overseas researches at the very beginning of the study. On the basis of literature review and in-depth interview with tourism operators via a three rounds of pre-test and revision, a self-completion questionnaire was developed and used to measure domestic residents' attitude and travel intentions after the Diaoyu Island political crisis. Data was collected through surveys in cities of different sizes from both developed and undeveloped areas in eastern and western China, such as Shanghai and cities in Fujian, Guizhou, Hubei, Jiangsu and Sichuan provinces. All respondents have abilities to travel to Japan economically and potentials to travel. Altogether 800 questionnaires were distributed and 756 were returned. Descriptive analysis was used to analyze the travel intention characteristics of domestic residents to Japan utilizing SPSS21.0. Four vital realities were found from the analysis. First, seeking entertainment opportunities can be the main motivation for Chinese residents travelling to Japan. Second, the family-based tour ought to be considered as the main trip mode. Third, the primary channels for tourism information dissemination include travel agencies and tourism e-commercial websites. Fourth, four to six-day staying in Japan could be proved as a suitable choice for domestic residents who prefer travelling during the national holidays and paid vacations. The CHAID decision tree model is reasonably used to analyze the differences of the intentions to travel among potential tourists in China. Based on the decision tree analysis, most citizens would choose to "cancel the travel plan" (29.0%), followed by "replace tourist destination" and "postpone the travel", yet only 19.6% would "travel as scheduled" in the light of the post impacts of Diaoyu Island political crisis. There could be no doubt that the vast majority of citizens in China lay emphasis on the macro political environment when making a travel decision. Four dominant nodes were generated in tree simulation, namely respective age, number of times to Japan, monthly income and marital status. This study has made a detailed analysis on the causes, and provides each group with tailored marketing measures eventually. Thereby, mixed measures should be implemented, including short-term and long-term plans, to recover the tourism market effectively. From the perspective of crisis decision-making and crisis marketing, this study segments the mainland Chinese citizens in order to disclose the heterogeneity and clustering features of them. The results of the study may set up a systematic foundation for better positioning potential tourists by furnishing an exploratory method for crisis management and crisis marketing. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of the proposed method, five other classifications (the Wishart supervised classification, the proposed method without polarimetric parameters, the proposed method without an object-based analysis, the proposed method without textural and geometric information and the proposed method using the nearest-neighbor classifier) were applied for comparison. The results indicated that some polarimetric parameters, such as Shannon entropy, Krogager_Kd, Alpha, HAAlpha_T11, VanZyl3_Vol, Derd, Barnes2_T33, polarization fraction, Barnes1_T33, Neuman_delta_mod and entropy, greatly improved the classification results. The shape index was a useful feature in distinguishing fish ponds and rivers. The distance to the sea can be regarded as an important factor in reducing the confusion between herbaceous wetland vegetation and grasslands. Furthermore, the decision tree algorithm increased the overall accuracy by 6.8% compared with the nearest neighbor classifier. This research demonstrated that different polarimetric parameters and the object-based approach significantly improved the performance of land cover classification in coastal wetlands using ALOS PALSAR data. [ABSTRACT FROM AUTHOR]