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    based on the empirical analysis of China Health and Retirement Longitudinal Survey(CHARLS)2015 data

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    ν•™μœ„λ…Όλ¬Έ (석사) -- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : μ‚¬νšŒκ³Όν•™λŒ€ν•™ μ‚¬νšŒν•™κ³Ό, 2021. 2. λ°•κ²½μˆ™.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 β€˜anti-breeding model’. 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 β€˜successful aging’ or β€˜active aging’.μ˜€λŠ˜λ‚  κ³ λ Ήν™”λŠ” μ „ 세계 κ³΅ν†΅μ˜ 문제둜 λ– μ˜€λ₯΄κ³  μžˆλ‹€. ν˜„λŒ€ν™” 및 λ„μ‹œν™”μ˜ 영ν–₯으둜 λŒ€λ‹€μˆ˜μ˜ 쀑ꡭ 노인듀은 성인이 된 μžλ…€λ₯Ό λ’·λ°”λΌμ§€ν•˜λŠ”λ°, 이 같은 ν˜„μƒμ„ 일컬어 '반포(反哺)λͺ¨ν˜•'이라고 ν•œλ‹€. ν•œνŽΈ, λŒ€λ‹€μˆ˜ μ„ ν–‰ 연ꡬ가 λΆ€λͺ¨μ— λŒ€ν•œ μžλ…€μ˜ μ±…μž„μ— μ΄ˆμ μ„ λ§žμΆ”κ³  μžˆλŠ” 반면 성인 μžλ…€μ— λŒ€ν•œ λ‚˜μ΄λ“  λΆ€λͺ¨μ˜ 희생에 κ΄€ν•œ λ…Όμ˜λŠ” λ§Žμ§€ μ•Šμ€ 싀정이닀. λ³Έ μ—°κ΅¬μ—μ„œλŠ” λΆ€λͺ¨μ™€ μžλ…€ κ°„μ˜ 경제, 도ꡬ, μ •μ„œ, μƒν˜Έμ§€μ› λ“± μΈ‘λ©΄μ—μ„œμ˜ ꡐλ₯˜μ— λŒ€ν•΄ μ‚΄νŽ΄λ³΄κ³ , μ΄λŸ¬ν•œ μ„ΈλŒ€ κ°„ 지원이 λ…ΈμΈμ˜ μ •μ‹  건강에 λ―ΈμΉ˜λŠ” 영ν–₯에 λŒ€ν•΄ ν† λ‘ ν•˜κ³ μž ν•˜μ˜€λ‹€. λ°μ΄ν„°λŠ” μ€‘κ΅­μ˜ 건강 및 노인 뢀양에 κ΄€ν•œ 2015λ…„ μΆ”μ μ‘°μ‚¬μ—μ„œ λΉ„λ‘―λ˜μ—ˆλ‹€. μ£Όμš” 연ꡬ 결과에 λ”°λ₯΄λ©΄ μ„ΈλŒ€ κ°„ 지원은 λ…ΈμΈμ˜ 정신건강과 관련이 있으며, λ„μ‹œμ™€ λ†μ΄Œ 집단에 따라 차이λ₯Ό λ³΄μ˜€λ‹€. λ†μ΄Œ λ…ΈμΈλ“€μ—κ²Œ μžˆμ–΄μ„œ μžλ…€λ“€μ˜ 일상적인 λ³΄μ‚΄ν•Œκ³Ό 지원은 정신건강에 뢀정적인 영ν–₯을 μ£ΌλŠ” κ²ƒμœΌλ‘œ λ“œλŸ¬λ‚¬λ‹€. 반면 λ„μ‹œ 노인듀은 μžλ…€λ“€μ˜ μ •μ„œμ  지지 속에 정신건강을 μœ μ§€ν•˜λŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. λ”°λΌμ„œ λͺ¨λ“  λ…ΈμΈμ˜ '성곡적 κ³ λ Ήν™”'와 β€˜κΈμ •μ  κ³ λ Ήν™”'λ₯Ό μΆ”μ§„ν•˜κΈ° μœ„ν•΄μ„œλŠ”, κ΅­κ°€κ°€ 각 지역에 λ§žλŠ” μ μ ˆν•œ κ³ λ Ήν™” 정책을 μ‹€ν–‰ν•  ν•„μš”κ°€ μžˆλ‹€.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 ꡭ문초둝 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

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    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

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    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

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    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

    Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond

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    Recent advancements in multimodal techniques open exciting possibilities for models excelling in diverse tasks involving text, audio, and image processing. Models like GPT-4V, blending computer vision and language modeling, excel in complex text and image tasks. Numerous prior research endeavors have diligently examined the performance of these Vision Large Language Models (VLLMs) across tasks like object detection, image captioning and others. However, these analyses often focus on evaluating the performance of each modality in isolation, lacking insights into their cross-modal interactions. Specifically, questions concerning whether these vision-language models execute vision and language tasks consistently or independently have remained unanswered. In this study, we draw inspiration from recent investigations into multilingualism and conduct a comprehensive analysis of model's cross-modal interactions. We introduce a systematic framework that quantifies the capability disparities between different modalities in the multi-modal setting and provide a set of datasets designed for these evaluations. Our findings reveal that models like GPT-4V tend to perform consistently modalities when the tasks are relatively simple. However, the trustworthiness of results derived from the vision modality diminishes as the tasks become more challenging. Expanding on our findings, we introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks
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