50,137 research outputs found
Do older adults become more depressed with age in Taiwan? The role of social position and birth cohort
No full textStudies on the optimum models of the dairy products kou woan lao using response surface methodology.
No full textStudy on the formation defect of nano imprinted optical waveguide devices and nano-indentation detection
No full textSocial participation reduces depressive symptoms among older adults: An 18-year longitudinal analysis in Taiwan
No full textAbstract Background Relatively little empirical attention has focused on the association between social participation and depressive symptoms amongst older adults in Asian nations, where persons over the age of 65 represent a rapidly growing segment of the population. This study explores the dynamic relationship between participation in social activities and trajectories of depressive symptomatology among older Taiwanese adults surveyed over 18 years. Methods Data are from a nationally representative sample of 1,388 adults aged 60-64 first surveyed in 1989 and followed over an 18-year time period for a total of six waves. Individual involvement in social activities was categorized into continuous participation, ceased participation before age 70, initiating participation in older adulthood, never participated, and dropped out before age 70. Two domains of depressive symptoms--negative affect and lack of positive affect--were measured using a 10-item version of the Center for Epidemiologic Studies-Depression Scale. Results Analyses using growth curve modeling showed that continuously participating or initiating participation in social activities later life is significantly associated with fewer depressive symptoms among older Taiwanese adults, even after controlling for the confounding effects of aging, individual demographic differences, and health status. Conclusions These findings suggest that maintaining or initiating social participation in later life benefits the mental health of older adults. Facilitating social activities among older adults is a promising direction for programs intended to promote mental health and successful aging among older adults in Taiwan.</p
Hypersurfaces with capillary boundary evolving by volume preserving power mean curvature flow
No full textIn this paper, we introduce a volume- or area-preserving curvature flow for hypersurfaces
with capillary boundary in the half-space, with speed given by a positive power of the mean
curvature with a non-local averaging term. We demonstrate that for any convex initial hyper-
surface with a capillary boundary, the flow exists for all time and smoothly converges to a
spherical cap as t → +∞
Multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network for cross-domain, poor thermal information prediction in cloud-edge control services
No full textThermal error, which has spatiotemporal behavior, severely reduces machining accuracy of high-accuracy machine tools and should be controlled in real time. The deep learning is used to establish spatiotemporal thermal error model with large-sample thermal information as input. But the acquisition of large-sample thermal information is extremely difficult and costly. So, in the actual application, thermal error is predicted with poor thermal information as input, and then the robustness is weak because the cross-domain and poor spatiotemporal thermal information prediction is still a severe challenge. In this study, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed without deepening network depth, instead, its width is expanded. Specifically, the adjacency matrix is constructed to consider the spatial information by defining the distance between each two sensors, and then the graph convolutional network is integrated into long short-term memory and gated recurrent unit to propose graph convolutional long short-term memory and graph convolutional gated recurrent unit with constructed adjacency matrix as input, respectively. The graph convolutional long short-term memory and graph convolutional gated recurrent unit are superimposed to propose the multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network. To improve the robustness, the adjacency matrix is retrained, and the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed and applied to forecast thermal error, and its prediction accuracy is 94.926%. Spatiotemporal characteristics of temperature and thermal error are fully captured by transfer learning model of graph convolutional long short-term memory and graph convolutional gated recurrent unit. Finally, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is embedded into thermal error control service architecture based on cloud-edge collaboration. With implementation of thermal error control service architecture based on cloud-edge collaboration, machining error of machine tools is reduced by more than 80%
Economic strain and well-being in late life: Findings from an 18-year population-based longitudinal study of older Taiwanese adults
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