Search

Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a "gray zone", where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

This article studies the intricate dynamics of family resilience within the Chinese productivist regime over the course of seven decades, emphasizing the instrumental role of public policy in shaping this resilience. Drawing on a dynamic approach, the research demonstrates how, across distinct periods of central planning and economic reform, policies have persistently harnessed families to advance economic objectives. Showing that the productivist regime's double-edged role on family resilience emerged while public policies bolstered short-term family resilience in times of sustained economic growth and favorable demographics, and signs of family sector downturn—declining fertility rates, family dysfunctions—became evident, the current article underscores the challenges of a model that primarily perceives families as economic instruments. Advocating for a policy paradigm shift that harmonizes economic ambitions with family well-being, this study offers valuable insights for policy-makers and lays the groundwork for future research in comparative welfare systems. [ABSTRACT FROM AUTHOR]

Warm-sector heavy rainfall often occurs along the coast of South China, and it is usually localized and long-lasting, making it challenging to predict. High-resolution numerical weather prediction (NWP) models are increasingly used to better resolve topographic features and forecast such high-impact weather events. However, when the grid spacing becomes comparable to the length scales of convection, known as the gray zone, the turbulent eddies in the atmospheric boundary layer are only partially resolved and parameterized to some extent. Whether using a convection parameterization (CP) scheme in the gray zone remains controversial. Scale-aware CP schemes are developed to enhance the representation of convective transport within the gray zone. The multi-scale Kain-Fritsch (MSKF) scheme includes modifications that allow for its effective implementation at a grid resolution as high as 2 km. In recent years, there has been an increasing application of machine learning (ML) models to various domains of atmospheric sciences, including the replacement of physical parameterizations with ML models. This work proposes a multi-output bidirectional long short-term memory (Bi-LSTM) model as a replace the scale-aware MSKF CP scheme. The Weather Research and Forecast (WRF) model is used to generate training and testing data over South China at a horizontal resolution of 5 km. Furthermore, the WRF model is coupled with the ML based CP scheme and compared with WRF simulations with original MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the potential use of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

Despite a rapid increase in people with Parkinson's disease (PD) in China, studies focusing on caregiver burden remain scarce. Challenges faced by caregivers in other Asian societies are also underexplored in the PD‐related research field. To addresses this gap, this study drew on the theory of resilience and examined the caregiving experiences of Chinese family caregivers for older people with PD. We conducted participant observation and semi‐structured interviews with 15 family caregivers of older people with PD in Shanghai in 2018. We used thematic analysis to identify key patterns of stressors and factors affecting the capacity of caregivers to address burdens. Our analysis shows that stressors faced by Chinese PD caregivers included physical limitations, psychological and emotional problems, reduction in social connections, financial burden and lack of information and knowledge. Three levels of factors—personal, social and structural—played different roles in resilience among Chinese PD caregivers. However, resilience was unevenly developed among PD caregivers across gender, kinship, residential status and generation. Our study provides novel empirical evidence about the burden on Chinese PD caregivers. It can inform policymakers and social workers as they develop a holistic understanding of promoting resilience building in this rapidly growing group. Our study also enriches cross‐cultural knowledge about factors contributing to caregivers' resilience building in Asian societies. [ABSTRACT FROM AUTHOR]

Existing literature has examined the contextual effects on individual environmental activism, but little research has been conducted to investigate how local-level governance factors affect individual environmental activism, particularly in developing countries. This paper investigates the moderating effects of local government transparency and responsiveness on the motivation-activism relationship in China. We compiled a provincial-level database capturing local government transparency and responsiveness and combined this with data from the China General Social Survey 2013. Multilevel modeling analysis revealed that motivation factors at the individual level have stronger effects on individual engagement in environmental activism when government transparency and responsiveness are more encouraging at the provincial level. The results not only point to local governance as a contextual factor for understanding individual environmental activism in China but also highlight the need to consider the interactions of individuals in the political context to understand how individual factors translate into environmental activism in many developing countries. [ABSTRACT FROM AUTHOR]

The timely detection and management of hemorrhagic shock hold paramount importance in clinical practice. This study was designed to establish a nomogram that may facilitate early identification of hemorrhagic shock in pediatric patients with multiple-trauma. A retrospective study was conducted utilizing a cohort comprising 325 pediatric patients diagnosed with multiple-trauma, who received treatment at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China. For external validation, an additional cohort of 144 patients from a children's hospital in Taizhou was included. The model's predictor selection was optimized through the application of the Least Absolute  Shrinkage and Selection Operator (LASSO) regression. Subsequently, a prediction nomogram was constructed using multivariable logistic regression analysis. The performance and clinical utility of the developed model were comprehensively assessed utilizing various statistical metrics, including Harrell's Concordance Index (C-index), receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Multivariate logistic regression analysis identified systolic blood pressure (ΔSBP), platelet count, activated partial thromboplastin time (APTT), and injury severity score (ISS) as independent predictors for hemorrhagic shock. The nomogram constructed using these predictors demonstrated robust predictive capabilities, as evidenced by an impressive area under the curve (AUC) value of 0.963. The model's goodness-of-fit was assessed using the Hosmer-Lemeshow test (χ 2  = 10.023, P = 0.209). Furthermore, decision curve analysis revealed significantly improved net benefits with the model. External validation further confirmed the reliability of the proposed predictive nomogram. This study successfully developed a nomogram for predicting the occurrence of hemorrhagic shock in pediatric patients with multiple trauma. This nomogram may serve as an accurate and effective tool for timely and efficient management of children with multiple trauma., (© 2024. The Author(s).)

Objective: To investigate serum levels of vitamin A and E in pregnant women in Huai'an and evaluate their nutritional status., Methods: Totally 2824 cases of pregnant women were chosen from the Huai'an Maternal and Child Health-Care Center during January 2017 to May 2018. The serum level of vitamin A and vitamin E in the blood was detected by high performance liquid chromatography(HPLC)., Results: The serum level of vitamin A was(0. 39±0. 08) mg/L, the overall abnormal rate was 12. 3%. The serum level of vitamin E was(13. 28±4. 67) mg/L, the overall abnormal rate was 3. 7%. Vitamin A level were positively correlated with age and dysarteriotony. Vitamin E level were also positively correlated age, dysarteriotony and appetite., Conclusion: The abnormal of vitamin A is mainly lack, the abnormal of vitamin E is mainly excessive.