Social Determinants of Community Health Services Utilization among the Users in China: A 4-Year Cross-Sectional Study

Background To identify social factors determining the frequency of community health service (CHS) utilization among CHS users in China. Methods Nationwide cross-sectional surveys were conducted in 2008, 2009, 2010, and 2011. A total of 86,116 CHS visitors selected from 35 cities were interviewed. Descriptive analysis and multinomial logistic regression analysis were employed to analyze characteristics of CHS users, frequency of CHS utilization, and the socio-demographic and socio-economic factors influencing frequency of CHS utilization. Results Female and senior CHS clients were more likely to make 3–5 and ≥6 CHS visits (as opposed to 1–2 visits) than male and young clients, respectively. CHS clients with higher education were less frequent users than individuals with primary education or less in 2008 and 2009; in later surveys, CHS clients with higher education were the more frequent users. The association between frequent CHS visits and family income has changed significantly between 2008 and 2011. In 2011, income status did not have a discernible effect on the likelihood of making ≥6 CHS visits, and it only had a slight effect on making 3–5 CHS visits. Conclusion CHS may play an important role in providing primary health care to meet the demands of vulnerable populations in China. Over time, individuals with higher education are increasingly likely to make frequent CHS visits than individuals with primary school education or below. The gap in frequency of CHS utilization among different economic income groups decreased from 2008 to 2011

Are long working hours associated with weight-related outcomes? A meta-analysis of observational studies

The relationship between long working hours and body weight outcomes remains inconclusive; thus, we conducted a meta-analysis to assess the effect of long working hours on weight-related outcomes. PubMed and Embase databases were searched from their inception to June 2019. A random-effects model was used to assess the pooled odds ratio (OR) and corresponding confidence interval (CI). Subgroup analyses and sensitivity analyses were conducted to explore sources of heterogeneity. Publication bias was evaluated by the Begg's and Egger's tests. A total of 29 articles involving 374 863 participants were included. The pooled OR of long working hours on weight-related outcomes was 1.13 (95% CI, 1.07-1.19). In subgroup analysis stratified by definition of outcomes, the pooled ORs of long working hours on “weight gain/BMI increase,” “BMI ≥ 25 kg/m 2,” and “BMI ≥ 30 kg/m 2” were 1.19 (95% CI, 1.02-1.40), 1.07 (95% CI, 1.00-1.14), and 1.23 (95% CI, 1.09-1.39), respectively. We found evidence of publication bias, but correction for this bias using the trim-and-fill method did not alter the combined OR substantially. There was evidence to suggest that long working hours are associated with adverse weight-related outcomes. Preventative interventions such as improved flexibility and healthy working schedules should be established for employees

Is There an Optimal Strategic Oil Reserve for Each Country? A Study Based on the Game Theory

In generally, there is a phenomenon of “free rider” in the establishment of national oil reserves for different countries, which means that they have the tendency of underestimating the strategic oil reserves. This paper attempts to explain this phenomenon from the perspective of non-cooperative game theory. It also analyzes the establishment of strategic oil reserve among different countries based on the coalition game theory and presents the core solution for it. The results show that based on a certain constraint mechanism, it is feasible for different countries to establish their own suitable strategic oil reserves in theory and practice

Correlates of loneliness in older adults in Shanghai, China: does age matter?

Abstract Background Loneliness is a public health concern with serious health consequences in older adults. Despite a large body of research on the correlates of loneliness, little is known about the age group differences in the correlates. Given that the older adult population is heterogeneous, this study aims to examine the correlates of loneliness in older adults in Shanghai, and to explore how the correlates vary across different age groups. Methods We used the Shanghai Urban Neighborhood Survey (SUNS) which was conducted in 2016 and 2017. The total sample size of older adults included in the analysis was 2770. Loneliness was measured using the sum of the 6 items derived from the De Jong Gierveld Loneliness Scale. Correlates include demographic variables, health conditions, social factors, and new media use. Regression analysis was used to examine the correlates of loneliness first in the whole sample, and then in the young old (60–79 years old) and the old old (80+ years old) separately. Results The mean of loneliness score was 18.48 (SD = 5.77). The old old reported a higher level of loneliness than the young old. Variables, including age, living arrangement, marital status, education, health, family functioning, volunteering, square dancing, and new media use were found to be significant in the whole sample. Most of the significant correlates observed in the young old were identical to the findings reported for the total sample, with an exception for living arrangement. Self-rated health (SRH) and family functioning were two important correlates for the old old. Conclusions Correlates of loneliness vary for the young old and the old old. The older adults at higher risk of loneliness deserve more attention and concern. Future interventions should be tailored to the young old and the old old to better help older adults alleviate loneliness and enhance their well-being

Relational prompt-based single-module single-step model for relational triple extraction

The relational triple extraction is a fundamental and essential information extraction task. The existing approaches of relation triple extraction achieve considerable performance but still suffer from 1) treating the relation between entities as a meaningless label while ignoring the relational semantic information of the relation itself and 2) ignoring the interdependence and inseparability of three elements of the triple. To address these problems, this paper proposes a Relational Prompt approach, based on which constructs a Single-module Single-step relational triple extraction model (RPSS). In particular, the proposed relational prompt approach consist of a relational hard-prompt and a relational soft-prompt, while provide take into account different level of relational semantic information, covering both the token-level and the feature-level relational prompt information. Then, we jointly encode entities and relational prompts to obtain a unified global representation. We mine deep correlations between different embeddings through attention mechanism and then construct a triple interaction matrix. Then, all triples could be directly extracted from a single module in a single step. Experiments demonstrate the effectiveness of the relational prompt approach, as well as relational semantics and triple integrity are essential for relation extraction. Experimental results on two benchmark datasets demonstrate our model outperforms current state-of-the-art models

Specific Emitter Identification Model Based on Improved BYOL Self-Supervised Learning

Specific emitter identification (SEI) is extracting the features of the received radio signals and determining the emitter individuals that generate the signals. Although deep learning-based methods have been effectively applied for SEI, their performance declines dramatically with the smaller number of labeled training samples and in the presence of significant noise. To address this issue, we propose an improved Bootstrap Your Own Late (BYOL) self-supervised learning scheme to fully exploit the unlabeled samples, which comprises the pretext task adopting contrastive learning conception and the downstream task. We designed three optimized data augmentation methods for communication signals in the former task to serve the contrastive concept. We built two neural networks, online and target networks, which interact and learn from each other. The proposed scheme demonstrates the generality of handling the small and sufficient sample cases across a wide range from 10 to 400, being labeled in each group. The experiment also shows promising accuracy and robustness where the recognition results increase at 3-8% from 3 to 7 signal-to-noise ratio (SNR). Our scheme can accurately identify the individual emitter in a complicated electromagnetic environment

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR