Investigating the factors affecting the residential satisfaction of new-generation migrants: a case study of Hangzhou in China

This paper examines the residential satisfaction of new-generation migrants in the Chinese context. The study uses the survey data of residential satisfaction of new-generation migrants in Hangzhou. The ordered logit model is employed to examine the factors affecting the residential satisfaction of new-generation migrants, a special group of the workforce in metropolitan cities of China. Unlike the previous studies of residential satisfaction of rural migrant workers in Chinese cities, we find that the residential satisfaction of new-generation migrants does follow the standard patterns identified in the literature; the socio-demographic attributes such as gender, education, income, housing characteristics of size and the quality of kitchen and sanitary facilities and neighbourhood environment and location factors such as the distance to work place, accessibility to employment and other location and the availability of entertaining amenities are significant determinants of residential satisfaction of new-generation migrants. Additionally, the institutional factors such as tenancy agreement signed with landlord and lease length, which provides tenants with residential stability and security have a significant impact on residential satisfaction. This finding has policy implication of regulating the growing rental housing market in China. This finding also complements the existing research on residential satisfaction

Enhanced low C/N nitrogen removal in an innovative microbial fuel cell (MFC) with electroconductivity aerated membrane (EAM) as biocathode

© 2016 Elsevier B.V. A novel microbial fuel cell (MFC) was developed to enhance simultaneous nitrification and denitrification (SND) by employing electrons from the anode. The cathode chamber of the reactor consisted of a membrane aerated biofilm reactor (MABR) which was made of an electroconductivity aerated membrane. The maximum power density of 4.20 ± 0.12 W m−3was obtained at a current density of 4.10 ± 0.11 A m−2(external resistance = 10 Ω). Compared with an open-circuit system, the removal rates of NH4+-N and TN were improved by 9.48 ± 0.33% and 19.80 ± 0.84%, respectively, which could be ascribed to the electrochemical denitrification. The anode (chemical oxygen demand, COD) and cathode (NO3−) chambers reached the maximum coulombic efficiencies (CEs) of 40.67 ± 1.05% and 42.84 ± 1.14%, respectively. It suggested that the electroconductivity MABR has some advantages in controlling aeration intensity, thus improving SND and CEs. Overall, EAM-MFC could successfully generate electricity from wastewater whilst showing high capacity for removing nitrogen at a low COD/N ratio of 2.8 ± 0.07 g COD g−1N

Predicting crash frequency using an optimised radial basis function neural network model

With the enormous losses to society that result from highway crashes, gaining a better understanding of the risk factors that affect traffic crash occurrence has long been a prominent focus of safety research. In this study, we develop an optimised radial basis function neural network (RBFNN) model to approximate the nonlinear relationships between crash frequency and the relevant risk factors. Our case study compares the performance of the RBFNN model with that of the traditional negative binomial (NB) and back-propagation neural network (BPNN) models for crash frequency prediction on road segments in Hong Kong. The results indicate that the RBFNN has better fitting and prediction performance than the NB and BPNN models. After the RBFNN is optimised, its approximation performance improves, although several factors are found to hardly influence the frequency of crash occurrence for the crash data that we use. Furthermore, we conduct a sensitivity analysis to determine the effects of the remaining input variables of the optimised RBFNN on the outcome. The results reveal that there are nonlinear relationships between most of the risk factors and crash frequency, and they provide a deeper insight into the risk factors’ effects than the NB model, supporting the use of the modified RBFNN models for road safety analysis.postprin

Cardiovascular health status in Chinese adults in urban areas: Analysis of the Chinese Health Examination Database 2010

Background: The American Heart Association (AHA) recently developed definitions of cardiovascular health for adults and children based on 7 cardiovascular disease risk factors or health behaviors. We applied this new construct to examine the cardiovascular health status in adult Chinese urban residents. Methods: Data of 1,012,418 subjects aged 20–65 years (55% were men; mean age, 42.4 years) who received health examination at 58 health examination centers across China was analyzed. The AHA ideal health behaviors index and ideal health factor index were evaluated among the subjects. Results: Only 0.6% of male and 2.6% of female subjects met all 7 health components, and only 39.1% of the subjects met 5 or more components of ideal cardiovascular health. The prevalence of “ideal”, “intermediate” and “poor” cardiovascular health was 1.5%, 33.9% and 64.6%, respectively. Conclusion: About two-thirds of the adult Chinese urban population has “poor” cardiovascular health. Comprehensive individual and population-based interventions must be developed to improve cardiovascular health status in China

Bis(4-phenyl­pyridinium) tetra­kis(nitrato-κ2 O,O′)stannate(IV). Retraction

Retraction of Acta Cryst. (2007), E63, m1567

Estimating time-varying effects for overdispersed recurrent events data with treatment switching

In the analysis of multivariate event times, frailty models assuming time-independent regression coefficients are often considered, mainly due to their mathematical convenience. In practice, regression coefficients are often time dependent and the temporal effects are of clinical interest. Motivated by a phase III clinical trial in multiple sclerosis, we develop a semiparametric frailty modelling approach to estimate time-varying effects for overdispersed recurrent events data with treatment switching. The proposed model incorporates the treatment switching time in the time-varying coefficients. Theoretical properties of the proposed model are established and an efficient expectation-maximization algorithm is derived to obtain the maximum likelihood estimates. Simulation studies evaluate the numerical performance of the proposed model under various temporal treatment effect curves. The ideas in this paper can also be used for time-varying coefficient frailty models without treatment switching as well as for alternative models when the proportional hazard assumption is violated. A multiple sclerosis dataset is analysed to illustrate our methodology