Suchergebnisse

Abstract—Cloud federation is a novel concept that has been drawing attention from research and industry. However, there is a lack of solid proposal that can be widely adopted in practice to guarantee adequate governance of federations, especially in the Public Sector contexts due to legal requirements. In this paper, we propose an innovative governance approach that ensures distributed and democratic control in cloud federations. Starting from FaaS, a recent cloud federation proposal, we propose a blockchain infrastructure for the federation registry that implements the proposed governance approach.

This paper explores the impact of educational mobilities on the ethnic identity construction of minority students in China. Adopting 'temporality' as an analytical tool, this paper highlights the dynamic temporal multiplicity in ethnic identity construction by comparing longitudinal in-depth interviews of a Mongolian and a Tibetan student. This multiplicity of temporality is manifested in three aspects: temporality of ethnic othering, temporality of ethnic identity awakening, and temporality of 'worldly time' and 'ethnic time. The 'worldly time' and the 'ethnic time' entail distinctive understandings about these students' pace and priorities in life. Both students defer their 'permanent' ethnic identity to an imagined future. Yet, adopting the gaze of the dominant others, both students subconsciously constructed an essentialist view of their ethnic cultures as fixed and stable and those of the dominant cultures as alive and fluid. This paper enriches our understanding of the politics of subjectivation through the lens of 'temporality'.

© 2017 Taylor & Francis. Long-term care (LTC) policy is at an experimental stage in China, characterized by various regional pilot programs. The public cost of LTC is difficult to estimate due to a lack of clarity about policy detail from the central government. This article analyzes the current disabled status for vulnerable older people without sufficient financial resources and family supports. It focuses on estimating a safety net public subsidy policy for LTC services in China, both for today and into the future, using China Health and Retirement Longitudinal Survey (CHARLS) data, 2011 wave, with the methods of multinomial logistic regression and simulation. The key contribution is to estimate the future disability trend and LTC public cost based on changes in education, population ageing, and urbanization. Disability prevalence might be decreasing partly due to higher education, urbanization, and better health care, and the overall public LTC costs might be growing by the results of projection.

This paper explores the changes in the prevalence of morbidity, morbidity-free life expectancy and its related factors in China from 2000 to 2010. The analysis uses health status data from three waves of the Sample Survey of the Aged Population in Urban/Rural China (SSAPUR). To explore the changes, three types of morbidity-free life expectancy (MFLE) are estimated using the Sullivan method. To identify the influencing factors of morbidity, we use regression models that consist of social and economic variables to identify these factors. Our results show that between 2000 and 2010, the prevalence of moderate morbidity based on activities of daily living (ADLs) declined across most age groups; however, the prevalence for instrumental activities of daily living (IADLs) and severe morbidity both increased, especially in the oldest old age group. Furthermore, the ratio of the MFLE to the total remaining life expectancy suggests the occurrence of a trend toward morbidity expansion. Our regression results suggest that age is the main factor in morbidity and impairment; however, education, health care, income and urbanisation play important roles in reducing the scores for IADLs and the prevalence of ADLs disability. Our findings imply that health care policy should assume a greater focus on healthy ageing, especially when people are expected to live longer. The findings also suggest that the government should prepare for the increasing demand for long-term care in the near future.

Objective: Economic evaluation of computerised decision-support software intended to assist in the interpretation of a cardiotocography (CTG) during birth. Design: Individual patient level data from the INFANT study (an unmasked randomised controlled trial). Setting: Maternity units in the UK and Ireland Population: Singleton or twin pregnancy women of 35 weeks' gestation or more and receiving continuous electronic fetal monitoring during labour. Intervention: Computerised decision-support software. Methods: Cost-consequence analysis presenting costs and outcomes with a time horizon of two years from a government health care perspective. Unit cost data collected from a combination of primary and secondary sources. Main outcome measures: Primary clinical outcomes were (i) composite 'poor neonatal outcome' and (ii) developmental assessment at age two years in a subset of surviving children. Mean cost per mother and infant dyad from birth to hospital discharge, and from hospital discharge to 24-months follow-up. Maternal health-related quality of life was assessed at 12 and 24 months follow-up using the EQ-5D-3L. Results: Data were analysed for 46,042 women and 46,614 infants. No statistically significant differences were detected between trial arms in any of the primary clinical outcomes or maternal quality of life. No statistically significant differences in costs were detected in maternal or infant costs from trial entry to hospital discharge or overall from hospital discharge to two-year follow-up. Conclusions: Decision-support software during labour is not associated with additional maternal or infant benefits and over a two-year period the software did not lead to additional costs or savings to the NHS.

Purpose The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era. Design/methodology/approach In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value. Findings Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored. Originality/value Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

We investigate organizational performance by considering the influence of goals and governance, both directly and interactively. Specifically, we theorize a linear relationship regarding goal importance and a curvilinear relationship regarding the degree of formalization of operational-level governance. We test our model on 348 Information Technology professionals in a large American government organization. We find support for our hypotheses and we find that goals and governance are substitutes in the condition of low to moderate governance, and are complements in the condition of moderate to high governance. While our theory is constrained to goals with high group-efficacy, our provocative findings contribute to both scholarly literature and managerial practice. Managers should select governance systems based on the characteristics of the organization's goals.

The detection of X-ray emission constitutes a reliable and efficient tool for the selection of active galactic nuclei (AGNs), although it may be biased against the most heavily absorbed AGNs. Simple mid-infrared (IR) broad-band selection criteria identify a large number of luminous and absorbed AGNs, yet again host contamination could lead to non-uniform and incomplete samples. Spectral energy distribution (SED) decomposition is able to decouple the emission from the AGN versus that from star-forming regions, revealing weaker AGN components. We aim to identify the obscured AGN population in the VIMOS Public Extragalactic Redshift Survey in the Canada–France–Hawaii Telescope Legacy Survey W1 field through SED modelling. We construct SEDs for 6860 sources and identify 160 AGNs at a high confidence level using a Bayesian approach. Using optical spectroscopy, we confirm the nature of ∼85 per cent of the AGNs. Our AGN sample is highly complete (∼92 per cent) compared to mid-IR colour-selected AGNs, including a significant number of galaxy-dominated systems with lower luminosities. In addition to the lack of X-ray emission (80 per cent), the SED fitting results suggest that the majority of the sources are obscured. We use a number of diagnostic criteria in the optical, IR, and X-ray regimes to verify these results. Interestingly, only 35 per cent of the most luminous mid-IR-selected AGNs have X-ray counterparts suggesting strong absorption. Our work emphasizes the importance of using SED decomposition techniques to select a population of type II AGNs, which may remain undetected by either X-ray or IR colour surveys. ; EP acknowledges financial support by ESA under the Hubble Catalog of Variables (HCV) programme, contract no. 4000112940. GM acknowledges support by the Agencia Estatal de Investigación, Unidad de Excelencia María de Maeztu, ref. MDM-2017-0765 and by the PROTEAS II project (MIS 5002515), which is implemented under the 'Reinforcement of the Research and Innovation Infrastructure' action, funded by the 'Competitiveness, Entrepreneurship and Innovation' operational programme (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). ; Peer reviewed

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. © 2013 Zhang et al.

Relatively little is known about if or how pain and Veteran status are related to frailty. Our objectives were to determine the association between pain and frailty among community-dwelling older men, and examine how combat experience among Veterans might modify this association. The study sample included 3,136 men from the 2012 Health and Retirement Study, a nationally representative sample of older Americans (mean age 77, range 68–99). The dependent variable was based on a frailty index that included at least three of the following: 1) weight loss of ≥10%; 2) difficulty lifting 10 pounds; 3) no physical activity or being tired all the time; 4) vision impairment,5) cognitive impairment. Key independent variables were reporting trouble with pain, Veteran status, and combat experience (fired a weapon in combat). Multivariable logistic regression was used to calculate the odds of frailty by pain status, and military experience, adjusting for age, race, marital status, education, income, and comorbidities. Twelve percent of the men were frail, one-third reported trouble with pain, 39.3% non-veterans, 6.2% and 54.5% veterans with and without combat experiences respectively. Men reporting pain had twice the odds of frailty as those without pain. In stratified analyses, and non-Veterans showed a similar association between pain and frailty. Veterans trouble with pain increased the odds of frailty by 2.7 times compared to non-Veterans, and there was no association between pain and frailty among Veterans with combat experience. Further research is needed to understand how military experiences are related to frailty in later life.