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Abstract The crack fault of gear is usually accompanied by temperature rise. Therefore, to master the temperature characteristics of the cracked gear of the wind turbine gearbox, a temperature rise calculation model of wind turbine gearbox gear considering crack fault and tooth number difference is proposed. Firstly, the time‐varying meshing stiffness model of the cracked gear considering the tooth number difference is established based on the potential energy method. Secondly, the calculation method of the meshing surface normal load of the cracked gear is deduced. Thirdly, the temperature rise calculation model of the meshing surface of the cracked gear is constructed based on Blok flash temperature theory. Finally, the data of the high‐speed gear of a wind turbine gearbox in northern China is selected for simulation verification. By comparing with the finite element method, the effectiveness of the proposed method is verified. The simulation results reveal the gear temperature characteristics of the wind turbine gearbox with different crack ratios. The research can provide some theoretical support for the accurate fault diagnosis and maintenance of wind turbine gearbox, and can also be applied to the fault diagnosis of gear cracks in other mechanical structures with a large transmission ratio.

Locking the location of the fault area accurately can effectively reduce the downtime. Therefore, a fault location method of wind turbine transmission chain system based on lumped parameter thermal network (LPTN) is proposed. Firstly, we analyzed the main heat generation forms and heat transfer paths based on the structure of transmission chain system. Secondly, the LPTN of transmission chain system is established. The heat generation power and thermal resistance of transmission chain system are calculated, and the solution formula of transient thermodynamic equation of node temperature is deduced. Thirdly, two fault location methods of wind turbine transmission chain system based on LPTN are proposed: (1) the method based on LPTN only, (2) the method considering the modification of operating conditions based on LPTN. Finally, 2 MW wind turbine data are used for experimental verification, and the results show that the maximum temperature residual of LPTN is less than 5℃. The fault location is verified by five kinds of transmission chain fault cases. The results show that the two methods can accurately locate the fault area. The research results can guide the fault maintenance of wind turbine, which is of great significance in reducing the downtime of wind turbine.

Landslides and geological disasters occur frequently in the mountainous areas of northwest China, seriously threatening people’s life and property of the region. In this study, we investigated the Lijie Beishan landslide as a typical case and combines the results of on-site geological surveys to conduct two-dimensional and three-dimensional numerical simulations of the landslide, evaluating its stability under self-weight, rainfall, and earthquake action. By analyzing the generalized shear strain, displacement, and stability coefficient of the landslide, it is determined that the stability of the landslide shows a decreasing trend under static, rainfall, and earthquake conditions. Landslides exhibit tension controlled failure modes under normal static and rainfall conditions, and translational failure modes under earthquake conditions. Compared with static and rainfall conditions, landslides have the largest volume and sliding distance under earthquake conditions. By combining the transfer coefficient method and the simplified Bishop method, a comparative analysis was conducted on the stability of the landslide. It was found that the stability coefficients under different working conditions were consistent with the simulation results, which verified the reliability of the simulation results. The research results of this paper will assist in clarifying the development mechanism of this type of landslides and provide valuable references for the stability evaluation of landslides in the northwest mountainous areas.

This paper explores the systemic relationship between urban civilization initiatives and economic prosperity by examining the economic impact of China’s “National Civilized City” (NCC) designation. We begin by synthesizing literature that addresses the complex interactions between urban development and environmental sustainability, emphasizing a systems approach. A theoretical model is developed to capture the dynamic interplay between urban environmental strategies and socio-economic outcomes, framed within the context of the Sustainable Development Goals (SDGs). Employing a quasi-experimental difference-in-differences (DID) method, we analyze data from a panel of Chinese cities (1997–2019) to assess how the NCC title influences economic growth and urban sustainability. Our results indicate a notable 2.88% increase in GDP per capita associated with the designation; however, this effect varies based on the city’s administrative status and geographical location, highlighting the complex and conditional nature of policy impact on economic development. We conclude by offering policy recommendations that promote a holistic approach to urban planning, fostering synergies between environmental conservation and economic growth. These insights contribute to a deeper understanding of sustainable urban development strategies with relevance to both emerging and developed economies.

With the in-depth development of globalization and informatization, digital trade, as an emerging form of trade, is gradually reshaping the global economic landscape and becoming a new engine for driving economic growth. Among them, the impact of digital trade on the export competitiveness of enterprises in developing countries has become a common concern in academia. To reveal the causal relationship between the development of digital trade and the enhancement of export competitiveness in developing countries, this paper first constructs a theoretical model based on product quality heterogeneity and analyzes the impact of digital trade on the export competitiveness of enterprises on the basis of achieving supply and demand equilibrium; then, this paper constructs a comprehensive index system for measuring digital trade and enterprise export competitiveness, and establishes an empirical analysis model; on this basis, this paper uses the data of listed companies in the A-share market in the Yangtze River Economic Belt area from 2011 to 2021 for empirical analysis. The results of the empirical analysis show that for every one-unit increase in the level of digital trade development in the region, there will be a positive impact of 0.9041 units on the export competitiveness of enterprises. After a series of robustness tests and endogeneity analyses, the above empirical results are confirmed to be robust and reliable. Furthermore, this paper conducts a heterogeneity analysis and finally puts forward corresponding policy recommendations based on the above theoretical and empirical research results.

Abstract Early detection of sepsis is key to ensure timely clinical intervention. Since very few end-to-end pipelines are publicly available, fair comparisons between methodologies are difficult if not impossible. Progress is further limited by discrepancies in the reconstruction of sepsis onset time. This retrospective cohort study highlights the variation in performance of predictive models under three subtly different interpretations of sepsis onset from the sepsis-III definition and compares this against inter-model differences. The models are chosen to cover tree-based, deep learning, and survival analysis methods. Using the MIMIC-III database, between 867 and 2178 intensive care unit admissions with sepsis were identified, depending on the onset definition. We show that model performance can be more sensitive to differences in the definition of sepsis onset than to the model itself. Given a fixed sepsis definition, the best performing method had a gain of 1–5% in the area under the receiver operating characteristic (AUROC). However, the choice of onset time can cause a greater effect, with variation of 0–6% in AUROC. We illustrate that misleading conclusions can be drawn if models are compared without consideration of the sepsis definition used which emphasizes the need for a standardized definition for sepsis onset.

In order to quantitatively analyze the influence of extreme low temperature on wind turbine blade performance, considering the uncertainty of its operation process, this paper proposed a quantitative modeling and analysis method based on nonlinear random-coefficient regression model for the influence of extreme low temperature on wind turbine blade performance. Compared with the traditional regression model, the random-coefficient regression model can better adapt to the complex and variable operating environment of wind turbines. Firstly, in order to quantitatively describe the effect of extreme low temperature on wind turbine blade performance, a quantitative model of extreme low temperature-blade performance was established based on the nonlinear random-coefficient regression model, and the function form of the quantitative model was determined based on the nonlinear least squares algorithm. Secondly, in order to improve the accuracy of prediction results, the expectation maximization algorithm and Bayesian updating algorithm are combined to realize the updating of random parameter of the model and the estimation of fixed parameters. Finally, the maximum blade stress is taken as the performance characteristic, and the simulation data of actual operation are used to carry out simulation tests to verify the superiority of the proposed algorithm. The results show that the maximum percentage of mean square error and mean absolute error of the proposed method are 0.0341 and 2.780% respectively, both of which are much lower than the model parameters estimation algorithm using only expectation maximization algorithm. Moreover, comparing the simulation results of the two methods, it can be seen that the proposed method has the advantages of high accuracy, fast convergence and strong robustness.

Since the reform and opening-up, China has developed into the world’s number one manufacturing country. Meanwhile, China’s environmental protection efforts continue to strengthen. So, will changes in the intensity of environmental regulatory policies have an impact on the technological development level and international competitiveness of China’s high-tech manufacturing industries? In response to this issue, we have reviewed relevant research in the field of environmental regulation and export technology complexity, and then selected appropriate indicators to quantify the environmental regulation and export technology complexity of high-tech manufacturing industries in different regions of China. Furthermore, the entropy method was used to calculate the intensity of environmental regulations in different regions of China. In the subsequent empirical analysis, based on relevant indicator data from 30 provinces in China, excluding Tibet, from 2006 to 2021, we quantitatively analyzed the impact of China’s environmental regulations on the complex export technology of high-tech manufacturing industries. The degree of influence and the robustness of the benchmark regression results was proved through endogeneity testing and robustness testing. The main conclusions are as follows: (1) from 2006 to 2021, China’s environmental regulation intensity and the technological complexity of high-tech industry exports have shown an upward trend. (2) The empirical analysis results show that the increase in intensity has a significant “U-shaped” impact on the technological complexity of exports of high-tech manufacturing industries. (3) The “U-shaped” impact of environmental regulation on the technological complexity of exports of high-tech manufacturing industries has regional differences. However, the high-tech manufacturing industry does not show obvious industry differences. (4) Environmental regulations will affect the level of export technology complexity of the high-tech manufacturing industry through foreign direct investment, human capital, and innovative R D investment, which cause indirect effects. Based on those conclusions, this paper has suggested corresponding policy measures and future research directions.

In order to explore the causal relationship between the level of urban digital construction and urban economic growth, this paper takes 280 cities in China as the research object and constructs a comprehensive indicator evaluation system covering digital infrastructure, overall economic level, innovation development level, digital industry development status, and ecological environment conditions. Using the entropy method to weigh various indicators, this paper has obtained the evaluation results of the digital construction level of each city from 2011 to 2021. Furthermore, a panel data regression model is used to empirically analyze the impact of urban digital construction level on urban economic growth. The results show that for every 1% increase in the level of urban digital construction, the GDP will increase by 0.974. Through the above research, we hope to further enrich the theoretical and empirical research in the field of the digital economy, provide a scientific and reasonable method for quantitatively evaluating the level of urban digital construction, and provide decision-making references for improving the level of urban digital construction and promoting sustainable urban development.

BackgroundEating disorders (EDs) and depression are common in university students, especially during the COVID-19 pandemic. The aim of this study was to elucidate characteristics of EDs and depression symptoms networks among Chinese university students in the later stage of the COVID-19 pandemic in China.MethodsA total of 929 university students completed the SCOFF questionnaire measuring EDs and Patient Health Questionnaire with 9 items (PHQ-9) measuring depression in Guangzhou, China. The network model was applied to identify central symptoms, bridge symptoms, and important connections between SCOFF and PHQ-9 using R studio. The subgroup analyses of both genders in medical and non-medical students were further explored.ResultsIn the networks of the whole sample, central symptoms included “Loss of control over eating” (EDs) and “Appetite changes” (depression). The bridge connections were between “Loss of control over eating” (EDs) and “Appetite changes” (depression), between “Deliberate vomiting” (EDs) and “Thoughts of death” (depression). “Appetite changes” (depression) and “Feeling of worthlessness” (depression) were central symptoms in both subgroups of medical and non-medical students. “Fatigue” (depression) was the central symptom in the female and medical students group. The edge between “Loss of control over eating” (EDs) and “Appetite changes” (depression) acted as a bridge in all subgroups.ConclusionSocial network approaches offered promising ways of further understanding the association between EDs and depression among university students during the pandemic of COVID-19 in China. Investigations targeting central and bridge symptoms would help to develop effective treatments for both EDs and depression for this population.

Introduction: Measurement of biomarkers early after acute myocardial infarction (AMI) might provide a cost-effective and widely available tool to assess infarct severity, myocardial dysfunction, and clinical outcomes. We aimed to induce AMI in miniature pigs, measure the levels of serum biomarkers and global LV function dynamically and explore the release kinetics and optimal sampling time points of copeptin and its correlation with global LV function. Methods: We induced AMI in the experimental group using a closed-chest model. Left ventricular (LV) function was detected by dual-source computed tomography (DSCT) and serum copeptin was determined by ELISA. Results: The serum copeptin levels were increased at 1 hour, peaked at 3 hours, gradually decreased after 6 hours, and returned to baseline 3 days after AMI. At 3 to 6 hours, the copeptin cutoff of 16.97 to 17.44 pmol/l had 100% sensitivity and 100% specificity ( P ⩽ .001) for AMI. Serum copeptin levels at 3 hours and 3 days were negatively correlated with the 3-hours LVEF ( P ⩽ .001), respectively. Conclusion: Serum copeptin levels change in time, and measurements at 3 to 6 hours after AMI had the highest predictive value.

ObjectivePostoperative nosocomial pneumonia is a terrible complication, especially for elderly patients. This study attempts to investigate the incidence and risk factors for postoperative nosocomial pneumonia and its influence on hospitalization stay in elderly patients with hip fractures.MethodsThis study retrospectively retrieved hospitalization records of patients who presented a hip fracture and underwent surgeries in our institution between January 2014 and December 2021. Postoperative new-onset pneumonia was determined in accordance with discharge diagnosis. Multivariate logistic regression analysis was performed to identify the associated risk factors with pneumonia, and its influence on total hospitalization stay or postoperative hospitalization stay was investigated by multivariate linear regression analyses.ResultsTotally, 808 patients were included, among whom 54 developed a pneumonia representing the incidence rate of 6.7% (95% CI, 5.0%–8.4%). Six factors were identified as independently associated with pneumonia, including advanced age (OR, 1.50 for each 10-year increment), history of chronic respiratory disease (OR, 4.61), preoperative DVT (OR, 3.51), preoperative delay to operation (OR, 1.07 for each day), surgical duration ≥120 min (OR, 4.03) and arthroplasty procedure (OR, 4,39). When adjusted for above confounders, pneumonia was significantly positively associated with total hospitalization stay (standardized coefficient, 0.110; p

Aiming to combat the problems of slow speed and poor accuracy of reliability evaluation of the power system in wind farms with energy storage, this paper proposes a method of reliability evaluation based on Latin hypercube important sampling (LHIS). Firstly, we aimed to establish the Latin hypercube important sampling evaluation model by combining the Latin hypercube sampling method with the important sampling method. Secondly, we aimed to optimize the sample probability distribution of the components and conduct hierarchical sampling of the system. Then, the comprehensive risk indicator (CRI) was proposed to evaluate the operational risk and the wind storage generation interrupted energy benefit (WSGIEB) was proposed to evaluate the contribution of the reliability. Finally, simulation experiments were carried out through various power system operation scenarios. The simulation results show that the proposed method is 47% higher than the improving importance sampling method (IM-IS) in evaluation speed and 33% higher than the improving importance sampling method in calculation accuracy.

Aiming to resolve the problem where the reliability of gearbox bearings of wind turbines is easily affected by random impact, this paper puts forward a reliability evaluation method for the competitive failure of gearbox bearings of wind turbines considering the fault incubation point. Firstly, we use Weibull distribution to simulate the fault latent process of wind turbine gearbox bearings. Secondly, a natural degradation model of gearbox bearing is established based on the Wiener process. Then, we model the random impact arrival frequency and impact intensity through a homogeneous Poisson process and normal distribution, respectively. Finally, based on considering the fault incubation point, the natural degradation of the bearing, the instantaneous degradation caused by impact, and the decline in the impact resistance of the bearing, a reliability evaluation model of gearbox bearings of wind turbines is established. A high-speed bearing of a gearbox from a wind farm in northern China is selected for simulation analysis. The results show that the proposed method can better describe the reliability decline process of the gearbox bearings of wind turbines, which has a specific guiding significance for the maintenance of wind turbines.

Objective:To observe the effect of combined mirror therapy and conventional speech training on the rehabilitation of subcortical aphasia after acute cerebral infarction.Methods:A total of thirty-four patients with subcortical aphasia after cerebral infarction were randomly divided into treatment group and control group. The treatment group received conventional speech therapy combined with mirror therapy, and patients in the control group were only given conventional speech therapy. Both groups received conventional speech therapy for acute cerebral infarction on the 7th day after onset. The aphasia quotient (AQ) in the western aphasia test, National Institutes of Health Stroke Scale (NIHSS) and the Modified Rankin Scale (mRS) were assessed before treatment, and at the third and 12th week of disease. AQ which increased≥30 scores within three months was regarded as significant improvement of speech function, and mRS scores of 0-2 were considered to have a good prognosis for neurological function.Results:There was no significant difference between the two groups before the treatment in all the indicators (P>0.05). After 2 weeks'treatment, NIHSS, AQ, and AQ (spontaneous speech, listening comprehension, retelling and naming) and mRS scores of the two groups were improved at the third and 12th week of disease, which were statistically significalt differences compared with before treatment (PPP=0.013). After adjusting for confounding factors, the intervention factor and age were correlated with aphasia function significant improvement (P

BackgroundIrritable bowel syndrome (IBS) has become a common public health issue among university students, impairing their physical and mental health. This meta-analysis aimed to examine the pooled prevalence of IBS and its associated factors among Chinese university students.MethodsDatabases of PubMed, EMBASE, MEDLINE (via EBSCO), CINAHL (via EBSCO), Wan Fang, CNKI and Weipu (via VIP) were systematically searched from inception date to May 31, 2021. Meta-analysis was performed using random-effects models. Meta-regression and subgroup analysis were used to detect the potential source of heterogeneity.Key ResultsA total of 22 cross-sectional studies (14 were in Chinese and 8 were in English) with 33,166 Chinese university students were included. The pooled prevalence of IBS was estimated as 11.89% (95% CI = 8.06%, 16.35%). The prevalence was 10.50% (95% CI = 6.80%, 15.87%) in Rome II criteria, 12.00% (95% CI = 8.23%, 17.17%) in Rome III criteria, and 3.66% (95% CI = 2.01%, 6.60%) in Rome IV criteria. The highest prevalence of IBS was 17.66% (95% CI = 7.37%, 36.64%) in North China, and the lowest was 3.18% (95% CI = 1.28%, 7.68%) in South China. Subgroup analyses indicated that gender, major, anxiety and depression symptoms, drinking and smoking behaviors were significantly associated with the prevalence of IBS. Meta-regression analyses suggested that region influenced prevalence estimates for IBS.Conclusions and InferencesThis meta-analysis illustrated that IBS is very common in Chinese university students. Regular screening, effective prevention, and appropriate treatments should be implemented to reduce the risk of IBS in this population. More future studies should be conducted in Northeastern and Southwestern parts of China.

This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces two significant contributions. Firstly, a novel obstacle detection method, named the point-map fusion (PMF) algorithm, was proposed. This method integrates point cloud data from the LiDAR, camera, and odometer, along with local grid maps. The PMF algorithm consists of two components: the point-fusion (PF) algorithm, which combines LiDAR point cloud data and camera laser-like point cloud data through a point cloud library (PCL) format conversion and concatenation, and selects the most proximate point cloud to the quadruped robot dog as the valid data; and the map-fusion (MF) algorithm, which incorporates local grid maps acquired using the Gmapping and OctoMap algorithms, leveraging Bayesian estimation theory. The local grid maps obtained by the Gmapping and OctoMap algorithms are denoted as map A and map B, respectively. This sophisticated methodology enables seamless map fusion, which significantly enhances the precision and reliability of the approach. Secondly, a motion distortion removal (MDR) method for LiDAR point cloud data based on odometer readings was proposed. The MDR method utilizes legged odometer data for linear data interpolation of the original distorted LiDAR point cloud data, facilitating the determination of the corresponding pose of the quadruped robot dog. Subsequently, the LiDAR point cloud data are then transformed to the quadruped robot dog coordinate system, efficiently mitigating motion distortion. Experimental results demonstrated that the proposed PMF algorithm achieved a 50% improvement in success rate compared to using only LiDAR or the PF algorithm in isolation, while the MDR algorithm enhanced mapping accuracy by 45.9% when motion distortion was taken into account. The effectiveness of the proposed methods was confirmed through rigorous experimentation.

The evaluation of China’s air pollution and the effectiveness of its governance policies is currently a topic of general concern in the academic community. We have improved the traditional evaluation method to construct a comprehensive air quality assessment model based on China’s major air pollutants. Using the daily air pollutant data of 2015−2018, we calculated and analyzed the monthly air quality of nine cities in the Pearl River Delta of China, and conducted a comparative study on the effect of the air pollution control policies of the cities in the Pearl River Delta. We found that the air quality control policies in those nine cities were not consistent. Specifically, the pollution control policies of Guangzhou and Foshan have achieved more than 20% improvement. The pollution control policies of Dongguan and Zhaoqing have also achieved more than 10% improvement. However, due to the relative lag of the formulation and implementation of air pollution control policies, the air quality of Jiangmen, Zhuhai and Zhongshan has declined. Based on the analysis of the air quality assessment results and the effects of governance policies in each city during the study period, we propose suggestions for further improvement of the effectiveness of air pollution control policies in the region.

With increasing emissions of industrial wastewater and poor control measures, environmental pollution has become a serious issue haunting China’s economic development. Meanwhile, the current pollution management policy system in China is mainly under the supervision of the central government and executed by local governments. Under the current economic growth model of China, the industrial sector remains the dominant segment of our economy, which makes the Total Factor Efficiency (TFE) evaluation and policy analysis of industrial wastewater control decisive factors concerning China’s future economic growth and sustainable development. Based on existing studies of China and abroad, and with the help of a Data Envelope Analysis (DEA) model, this paper used 39 industrial sectors and their input-output data from 2003 to 2014 of China as Decision Making Units to calculate the TFE of wastewater control in different industrial sectors of China. Moreover, we have designed and adopted our own MATLAB programming for optimization solutions of multi-variable constrained nonlinear functions in order to obtain a more accurate estimation of the TFE of wastewater control. Based on our calculation results, this paper further explained the difference in TFE and policy implications across typical industries in China, and offered policy recommendations accordingly.

Previous studies have shown that the school-based physical activity programme entitled "11 for Health" has been effective in improving the physical fitness, well-being and overall health profile of European children. The main purpose of the present study was to examine whether the "11 for Health" programme can have a positive impact on the physical fitness of primary school pupils in China. A total of 124 primary school pupils aged 9-11 years were included in the experiment, randomly divided into the experimental group (EG, n = 62) and the control group (CG, n = 62). EG carried out three weekly 35-minute sessions of small-sided football for a total of 11 weeks. All data were analysed by a mixed ANOVA with the Student-Newman-Keuls post hoc test. The results showed greater improvements (p<0.001) in EG than CG in relation to systolic blood pressure (-2.9 vs +2.0 mmHg). Moreover, greater improvements (all p<0.05) were observed in postural balance (13 vs 0%), standing long jump (5.0 vs 0.5%), 30-metre sprint (4.1 vs 1.3%) and Yo-Yo IR1C running performance (17% vs 6%). In both EG and CG, physical activity enjoyment was observed to be higher (P<0.05) compared to the start of the intervention with increases of 3.7 and 3.9 AU respectively. In conclusion, the study revealed that the "11 for Health" programme has positive effects on aerobic and muscular fitness, and it appears to be a relevant tool in the physical activity promotion in the Chinese school system. [ABSTRACT FROM AUTHOR]