1,990 research outputs found
based on the empirical analysis of China Health and Retirement Longitudinal Survey(CHARLS)2015 data
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์ฌํ๊ณผํ๋ํ ์ฌํํ๊ณผ, 2021. 2. ๋ฐ๊ฒฝ์.Nowadays, aging is a common global problem. Along with modernization and urbanization, not a few Chinese aging parents tend to support their adult children, which is known as the โanti-breeding modelโ. Most prior research focused on the responsibility of children on aging parents and did not discuss the contribution from aging parents to their adult children. This study intended to discuss both upward and downward direction in financial, instrumental, emotional support and the association between intergenerational support and the mental health(depression) of the Chinese elderly. Data from the 2015 Chinese Health and Retirement Longitudinal Survey was used for analysis. The main results of the study were as follows. Intergenerational support is correlated with the mental health of the Chinese elderly, and differences exist in rural/urban areas. For rural-living elderly, the daily care from their younger generations is negatively associated with their mental health. However, older urban people are more likely to maintain mental health with the emotional support from their adult children. It is imperative to adjust measures to local conditions to ensure all Chinese seniors achieve โsuccessful agingโ or โactive agingโ.์ค๋๋ ๊ณ ๋ นํ๋ ์ ์ธ๊ณ ๊ณตํต์ ๋ฌธ์ ๋ก ๋ ์ค๋ฅด๊ณ ์๋ค. ํ๋ํ ๋ฐ ๋์ํ์ ์ํฅ์ผ๋ก ๋๋ค์์ ์ค๊ตญ ๋
ธ์ธ๋ค์ ์ฑ์ธ์ด ๋ ์๋
๋ฅผ ๋ท๋ฐ๋ผ์งํ๋๋ฐ, ์ด ๊ฐ์ ํ์์ ์ผ์ปฌ์ด '๋ฐํฌ(ๅๅบ)๋ชจํ'์ด๋ผ๊ณ ํ๋ค. ํํธ, ๋๋ค์ ์ ํ ์ฐ๊ตฌ๊ฐ ๋ถ๋ชจ์ ๋ํ ์๋
์ ์ฑ
์์ ์ด์ ์ ๋ง์ถ๊ณ ์๋ ๋ฐ๋ฉด ์ฑ์ธ ์๋
์ ๋ํ ๋์ด๋ ๋ถ๋ชจ์ ํฌ์์ ๊ดํ ๋
ผ์๋ ๋ง์ง ์์ ์ค์ ์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ถ๋ชจ์ ์๋
๊ฐ์ ๊ฒฝ์ , ๋๊ตฌ, ์ ์, ์ํธ์ง์ ๋ฑ ์ธก๋ฉด์์์ ๊ต๋ฅ์ ๋ํด ์ดํด๋ณด๊ณ , ์ด๋ฌํ ์ธ๋ ๊ฐ ์ง์์ด ๋
ธ์ธ์ ์ ์ ๊ฑด๊ฐ์ ๋ฏธ์น๋ ์ํฅ์ ๋ํด ํ ๋ก ํ๊ณ ์ ํ์๋ค. ๋ฐ์ดํฐ๋ ์ค๊ตญ์ ๊ฑด๊ฐ ๋ฐ ๋
ธ์ธ ๋ถ์์ ๊ดํ 2015๋
์ถ์ ์กฐ์ฌ์์ ๋น๋กฏ๋์๋ค. ์ฃผ์ ์ฐ๊ตฌ ๊ฒฐ๊ณผ์ ๋ฐ๋ฅด๋ฉด ์ธ๋ ๊ฐ ์ง์์ ๋
ธ์ธ์ ์ ์ ๊ฑด๊ฐ๊ณผ ๊ด๋ จ์ด ์์ผ๋ฉฐ, ๋์์ ๋์ด ์ง๋จ์ ๋ฐ๋ผ ์ฐจ์ด๋ฅผ ๋ณด์๋ค. ๋์ด ๋
ธ์ธ๋ค์๊ฒ ์์ด์ ์๋
๋ค์ ์ผ์์ ์ธ ๋ณด์ดํ๊ณผ ์ง์์ ์ ์ ๊ฑด๊ฐ์ ๋ถ์ ์ ์ธ ์ํฅ์ ์ฃผ๋ ๊ฒ์ผ๋ก ๋๋ฌ๋ฌ๋ค. ๋ฐ๋ฉด ๋์ ๋
ธ์ธ๋ค์ ์๋
๋ค์ ์ ์์ ์ง์ง ์์ ์ ์ ๊ฑด๊ฐ์ ์ ์งํ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ๋ฐ๋ผ์ ๋ชจ๋ ๋
ธ์ธ์ '์ฑ๊ณต์ ๊ณ ๋ นํ'์ โ๊ธ์ ์ ๊ณ ๋ นํ'๋ฅผ ์ถ์งํ๊ธฐ ์ํด์๋, ๊ตญ๊ฐ๊ฐ ๊ฐ ์ง์ญ์ ๋ง๋ ์ ์ ํ ๊ณ ๋ นํ ์ ์ฑ
์ ์คํํ ํ์๊ฐ ์๋ค.Table of Contents
1. Introduction 1
1.1 Background 1
1.1.1 Population Aging in China 1
1.1.2 Mental Health of the Elderly in China 1
1.1.3 Family Inter-generational Support in the Chinese context 2
1.2 Purpose of the Study 4
1.3 Significance 5
2. Literature Review 6
2.1 The Research on Intergenerational Support 6
2.2 The Research on Factors Affecting Elderly Mental Health 9
2.3 The Research on the Association between Family Inter-generational Support and Mental Health 14
2.4 Summary 16
3. Hypothesis 16
4. Data and Methodology 18
4.1 Methodology 18
4.2 Variable 19
4.2.1Independent Variable 20
4.2.2 Dependent Variable 21
4.2.3 Control Variables 22
5.Results 24
5.1 Descriptive Characteristics of The Sample 24
5.2 Results of logistic Regression on Effect of inter-generational Support on Mental Health of the elderly 31
5.2.1 Full-sample Analysis 31
5.2.2 Rural-urban Comparative Analysis 33
6.Discussion and Conclusion 36
6.1Dicussion 36
6.2 Innovation and Limitation of the study 39
References 42
๊ตญ๋ฌธ์ด๋ก 51
Tables and Figures
4.2 Variable Description 23
5.1Mental Health of observations (N, %) 24
5.2 Inter-generational Support of Observations( N, %) 24
5.3.1 Demographic Factors of Observations(N, %) 26
5.3.2 Other Personal Factors of Observations(N, %) 28
5.3.3 Sleeping Hours of Observations 30
5.2.1 Binary Logistic Regression Analysis of the Association between inter-generational Support and Depressive Mood 31
5.2.2The effect of inter-generational Support on Depressive Mood stratified by residence type 35Maste
Optimal Posted Prices for Online Cloud Resource Allocation
We study online resource allocation in a cloud computing platform, through a
posted pricing mechanism: The cloud provider publishes a unit price for each
resource type, which may vary over time; upon arrival at the cloud system, a
cloud user either takes the current prices, renting resources to execute its
job, or refuses the prices without running its job there. We design pricing
functions based on the current resource utilization ratios, in a wide array of
demand-supply relationships and resource occupation durations, and prove
worst-case competitive ratios of the pricing functions in terms of social
welfare. In the basic case of a single-type, non-recycled resource (i.e.,
allocated resources are not later released for reuse), we prove that our
pricing function design is optimal, in that any other pricing function can only
lead to a worse competitive ratio. Insights obtained from the basic cases are
then used to generalize the pricing functions to more realistic cloud systems
with multiple types of resources, where a job occupies allocated resources for
a number of time slots till completion, upon which time the resources are
returned back to the cloud resource pool
The Expanding Landscape of Alternative Splicing Variation in Human Populations.
Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine
Explainable Multilayer Graph Neural Network for Cancer Gene Prediction
The identification of cancer genes is a critical, yet challenging problem in
cancer genomics research. Recently, several computational methods have been
developed to address this issue, including deep neural networks. However, these
methods fail to exploit the multilayered gene-gene interactions and provide
little to no explanation for their predictions. Results: In this study, we
propose an Explainable Multilayer Graph Neural Network (EMGNN) approach to
identify cancer genes, by leveraging multiple gene-gene interaction networks
and multi-omics data. Compared to conventional graph learning methods, EMGNN
learned complementary information in multiple graphs to accurately predict
cancer genes. Our method consistently outperforms existing approaches while
providing valuable biological insights into its predictions. We further release
our novel cancer gene predictions and connect them with known cancer patterns,
aiming to accelerate the progress of cancer researc
An investigation of the impact of co-existing bacteria on algal culture characteristics and submerged vibrational ultrafiltration performance
The complexity of algal culture characteristics has been noted as a critical challenge to understand and optimise separation efficiency for a sustainable algae harvesting technology. Previous studies have attempted to assess dissolved extracellular organic matter character and concentration, biomass density and their impact on the separation performance, with limited success. This project aims to extend previous research findings by providing an advanced algal culture characterisation that improves understanding of the impact of biomass composition, cell integrity, bioflocculation, and particulate and dissolved organic matter on submerged vibrational ultrafiltration (UF) performance and fouling mechanisms.
During the initial part of this study conducted with four algal species, it was found that different levels of co-existing bacteria were present in the algae culture, which positively correlated to bioflocculation activity. In turn, a better separation performance was observed for cultures with higher floc density during step-flux experiments. The cake layer was found to be the prominent fouling mechanism and was easily removed by simple physical rinsing. Transverse vibration of the membrane effectively alleviated both recoverable and irrecoverable fouling formed during algal harvesting using the submerged UF system.
Through controlling the initial bacterial:algal (B:A) ratio of a Chlorella vulgaris culture, it was found that a high bacteria abundance had an insignificant impact on algae growth, nutrient removal and cellular integrity. Despite different initial B:A ratios, all cultures reached an equilibrium ratio after nine days of cultivation. Nonetheless, the high initial B:A ratio led to algal cultures with a higher biofloc volume density and a higher concentration of organic matter with larger size, hydrophobic, protein and microbial fluorescent properties.
The algal culture with a high initial B:A ratio was further compared against the control in long-term filtration performance and fouling reversibility. Higher fouling rates were found in the culture at the later growth phase and the high initial B:A ratio. Development of a cake layer was again the dominant fouling mechanism, which consisted of a mixture of microbial cells, cell debris, particulate and hydrophobic organic matter, carbohydrate and proteins. Chemical cleaning was able to restore most membrane characteristics in contrast with physical backwashing and rinsing
Towards Robust Graph Incremental Learning on Evolving Graphs
Incremental learning is a machine learning approach that involves training a
model on a sequence of tasks, rather than all tasks at once. This ability to
learn incrementally from a stream of tasks is crucial for many real-world
applications. However, incremental learning is a challenging problem on
graph-structured data, as many graph-related problems involve prediction tasks
for each individual node, known as Node-wise Graph Incremental Learning (NGIL).
This introduces non-independent and non-identically distributed characteristics
in the sample data generation process, making it difficult to maintain the
performance of the model as new tasks are added. In this paper, we focus on the
inductive NGIL problem, which accounts for the evolution of graph structure
(structural shift) induced by emerging tasks. We provide a formal formulation
and analysis of the problem, and propose a novel regularization-based technique
called Structural-Shift-Risk-Mitigation (SSRM) to mitigate the impact of the
structural shift on catastrophic forgetting of the inductive NGIL problem. We
show that the structural shift can lead to a shift in the input distribution
for the existing tasks, and further lead to an increased risk of catastrophic
forgetting. Through comprehensive empirical studies with several benchmark
datasets, we demonstrate that our proposed method,
Structural-Shift-Risk-Mitigation (SSRM), is flexible and easy to adapt to
improve the performance of state-of-the-art GNN incremental learning frameworks
in the inductive setting
- โฆ