Background: China is facing with a crisis of the aging population. After the implementation of the latest fertility policy, the research on fertility related issues is urgent. Objective: The objective of this study is...Background: China is facing with a crisis of the aging population. After the implementation of the latest fertility policy, the research on fertility related issues is urgent. Objective: The objective of this study is to explore the fertility values among women of childbearing age and the socio-demographic factors associated with it under the background of three-child policy, which is helpful to cope with the aging of the population. Methods: This study was conducted among 383 women of childbearing age who met the inclusion criteria using a general information questionnaire and the fertility values questionnaire from May to August 2021 in Hunan Province, China. Data were collected on the women’s socio-demographic characteristics and fertility values. The descriptive statistics, t-test and analysis of variance were used for data analysis. Results: The total mean score of the positive values was 43.55 ± 10.10, and that of the negative values was 50.87 ± 13.85. There were significant differences in the scores of the overall positive and negative values, as well as scores of each dimension (p The item mean score of the overall negative values (3.38 ± 0.93) was higher than that of the overall positive values (2.90 ± 0.67). Among the positive values, “emotional value” (4.26 ± 0.93) scored the highest, while “worrying about life changes” (3.88 ± 1.10) scored the highest among the negative values. There were significant differences in both the positive and negative values in terms of age, marital status, and “only-child” women or not (p Conclusion: The fertility values among women of childbearing age in Hunan Province were relatively negative, especially, excessive worries about life change since having a child, which may lead to further declines in fertility levels. Relevant support measures are urgently needed from the government to adapt to the three-child policy.展开更多
We present an efficient deep learning method called coupled deep neural networks(CDNNs) for coupling of the Stokes and Darcy–Forchheimer problems. Our method compiles the interface conditions of the coupled problems ...We present an efficient deep learning method called coupled deep neural networks(CDNNs) for coupling of the Stokes and Darcy–Forchheimer problems. Our method compiles the interface conditions of the coupled problems into the networks properly and can be served as an efficient alternative to the complex coupled problems. To impose energy conservation constraints, the CDNNs utilize simple fully connected layers and a custom loss function to perform the model training process as well as the physical property of the exact solution. The approach can be beneficial for the following reasons: Firstly, we sample randomly and only input spatial coordinates without being restricted by the nature of samples.Secondly, our method is meshfree, which makes it more efficient than the traditional methods. Finally, the method is parallel and can solve multiple variables independently at the same time. We present the theoretical results to guarantee the convergence of the loss function and the convergence of the neural networks to the exact solution. Some numerical experiments are performed and discussed to demonstrate performance of the proposed method.展开更多
We propose the meshfree-based physics-informed neural networks for solving the unsteady Oseen equations.Firstly,based on the ideas of meshfree and small sample learning,we only randomly select a small number of spatio...We propose the meshfree-based physics-informed neural networks for solving the unsteady Oseen equations.Firstly,based on the ideas of meshfree and small sample learning,we only randomly select a small number of spatiotemporal points to train the neural network instead of forming a mesh.Specifically,we optimize the neural network by minimizing the loss function to satisfy the differential operators,initial condition and boundary condition.Then,we prove the convergence of the loss function and the convergence of the neural network.In addition,the feasibility and effectiveness of the method are verified by the results of numerical experiments,and the theoretical derivation is verified by the relative error between the neural network solution and the analytical solution.展开更多
文摘Background: China is facing with a crisis of the aging population. After the implementation of the latest fertility policy, the research on fertility related issues is urgent. Objective: The objective of this study is to explore the fertility values among women of childbearing age and the socio-demographic factors associated with it under the background of three-child policy, which is helpful to cope with the aging of the population. Methods: This study was conducted among 383 women of childbearing age who met the inclusion criteria using a general information questionnaire and the fertility values questionnaire from May to August 2021 in Hunan Province, China. Data were collected on the women’s socio-demographic characteristics and fertility values. The descriptive statistics, t-test and analysis of variance were used for data analysis. Results: The total mean score of the positive values was 43.55 ± 10.10, and that of the negative values was 50.87 ± 13.85. There were significant differences in the scores of the overall positive and negative values, as well as scores of each dimension (p The item mean score of the overall negative values (3.38 ± 0.93) was higher than that of the overall positive values (2.90 ± 0.67). Among the positive values, “emotional value” (4.26 ± 0.93) scored the highest, while “worrying about life changes” (3.88 ± 1.10) scored the highest among the negative values. There were significant differences in both the positive and negative values in terms of age, marital status, and “only-child” women or not (p Conclusion: The fertility values among women of childbearing age in Hunan Province were relatively negative, especially, excessive worries about life change since having a child, which may lead to further declines in fertility levels. Relevant support measures are urgently needed from the government to adapt to the three-child policy.
基金Project supported in part by the National Natural Science Foundation of China (Grant No.11771259)the Special Support Program to Develop Innovative Talents in the Region of Shaanxi Province+1 种基金the Innovation Team on Computationally Efficient Numerical Methods Based on New Energy Problems in Shaanxi Provincethe Innovative Team Project of Shaanxi Provincial Department of Education (Grant No.21JP013)。
文摘We present an efficient deep learning method called coupled deep neural networks(CDNNs) for coupling of the Stokes and Darcy–Forchheimer problems. Our method compiles the interface conditions of the coupled problems into the networks properly and can be served as an efficient alternative to the complex coupled problems. To impose energy conservation constraints, the CDNNs utilize simple fully connected layers and a custom loss function to perform the model training process as well as the physical property of the exact solution. The approach can be beneficial for the following reasons: Firstly, we sample randomly and only input spatial coordinates without being restricted by the nature of samples.Secondly, our method is meshfree, which makes it more efficient than the traditional methods. Finally, the method is parallel and can solve multiple variables independently at the same time. We present the theoretical results to guarantee the convergence of the loss function and the convergence of the neural networks to the exact solution. Some numerical experiments are performed and discussed to demonstrate performance of the proposed method.
基金Project supported in part by the National Natural Science Foundation of China(Grant No.11771259)Shaanxi Provincial Joint Laboratory of Artificial Intelligence(GrantNo.2022JCSYS05)+1 种基金Innovative Team Project of Shaanxi Provincial Department of Education(Grant No.21JP013)Shaanxi Provincial Social Science Fund Annual Project(Grant No.2022D332)。
文摘We propose the meshfree-based physics-informed neural networks for solving the unsteady Oseen equations.Firstly,based on the ideas of meshfree and small sample learning,we only randomly select a small number of spatiotemporal points to train the neural network instead of forming a mesh.Specifically,we optimize the neural network by minimizing the loss function to satisfy the differential operators,initial condition and boundary condition.Then,we prove the convergence of the loss function and the convergence of the neural network.In addition,the feasibility and effectiveness of the method are verified by the results of numerical experiments,and the theoretical derivation is verified by the relative error between the neural network solution and the analytical solution.
