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Previous research on family structure and child development has largely focused on the disadvantages faced by children who transitioned out of married families. However, we know less about how family structure affects child outcomes for children starting out in single-mother families. In this article, we use the kindergarten cohort of the Early Childhood Longitudinal Study to analyse children’s academic outcomes between kindergarten and eighth grade. We found that living in single-mother or step-families was clearly associated with lower test scores for children starting kindergarten in married biological-parent families, but the same disadvantages associated with living outside a married biological-parent family structure were not found for children starting kindergarten in single-mother families. We also found preliminary evidence of a buffering effect of maternal education in the relationship between family structure and children’s academic outcomes.

This article builds upon a multilevel theory of trust to explore the relationship between general trust in health care systems and general trust in physicians and the social-contextual factors that shape this relationship. We develop a model of trust in physicians emphasizing the embeddedness of individuals in broader social-institutional contexts. We analyze data from 30 countries in the 2011 International Social Survey Program ( N = 38,068) and specify hierarchical linear models with macro-micro level interactions. At the individual level, we find that individuals who trust the health care system are more likely to trust physicians in general. At the country level, we find that respondents from countries with predominately publicly financed health care systems are more likely to trust physicians than their counterparts in countries with less public funding of the health care system. Finally, we find that the greatest predicted probability of trust in physicians is found among individuals who trust their publicly funded health care system and the lowest probability is among individuals who have no confidence in their privately funded health care system. Based on these findings, we call for greater attention to the interaction of micro- and macro-level factors in models of trust in physicians cross-nationally.

In this article, the associations among individual socio-economic characteristics, the institutional set-up of health care systems, and satisfaction with the health care system are investigated. Data from the 2011 International Social Survey Program (30 countries, 34,212 respondents) is used. Multilevel analyses across countries have shown how the state financing context affects satisfaction at the individual level. Consistent with previous research, at individual level, personal experiences with medical providers, age, gender as well as income are significant predictors of satisfaction with the health care system. At the country level, real input indicators such as density of physicians and density of hospital beds are negative predictors of satisfaction with the health care system whereas the percentage of total health expenditures comprised by public sources is a positive predictor of satisfaction with the health care system. However, findings from the cross-level interactions indicate that the negative effect of lower income is more prominent in predominantly publicly-funded health care systems. Specifically, in primarily publicly-funded health care systems, the model-predicted probability of satisfaction with the health care system is higher, but the gap in the probability of satisfaction with the health care system between individuals with lower income and those with higher income is greater than that in mostly privately-financed health care systems. The findings in this study suggest that the future direction of health care system reform should be focused on balancing the distribution of resources between private and public sectors.

Organo-silanes, germanes, and stannanes are considered to be conducive to the development of cross-coupling reactions because they are stable, nontoxic, and easy to handle. Using feedstock toluenes, one-pot direct benzylic C-H silylations, germylations, and stannylations are developed. Simply combining toluenes, LiN(SiMe

The Yellow River Basin is an energy-rich area. The low-carbon development of the Yellow River Basin is one of the ways to achieve ecological protection and high-quality development. Based on panel data from 2007 to 2019, the dynamic and static carbon emission efficiencies of 98 cities in the Yellow River Basin were calculated using the Undesirable Super Slacks-based model and the Global Malmquist-Luengerber index method, respectively, and the spatial effects of influencing factors were analyzed using the spatial Dubin model. Main conclusions are as follows: (1) The carbon emission efficiency of cities in the Yellow River Basin is different, and only a small numbers of cities have reached the effective state. (2) The main factor for the increase in the overall carbon emission efficiency of the whole Basin is technological progress, and the main factors for the improvement of carbon emission efficiency of different cities are various. (3) The direct effects of economic development level and industrial structure upgrading are significantly positive, the direct effects of technological development level, energy intensity, and opening up are significantly negative, and the indirect effects of population density are positive. The conclusions are of great significance for promoting low-carbon development, ecological protection and high-quality development in the Yellow River Basin.

Stable and fully developed gas flow field is crucial for realizing accurate measurement of gas ultrasonic flow meter. To reduce the flow field distortion, a flow conditioner is usually used. However, the traditional monotype flow conditioner can only improve the flow field distribution partly. The measurement accuracy of the transit time ultrasonic flow meter is still affected because of its serious flow field distortion in the complex pipeline conditions. In this paper, to further improve the flow field distribution, a combined conditioner is investigated. The combined flow conditioner is composed of fan-shaped section, turbulent mixing cavity, and honeycomb-shaped section. The effects of fan blade angle and cavity length on the flow field of the DN50 flow meter are studied using computational fluid dynamics (CFD) simulation. Simulation results indicate that compared with the monotype conditioner, the combined conditioner has better performance on effectively reducing the swirl and turbulence and providing more stable and repetitive velocity profiles. Experiments also validate the effectiveness of the combined conditioner. The flow meter with the combined conditioner has better repeatability of less than 0.2%, which is better than those of the monotype conditioners under the same conditions. This work is very useful for accurate measurement of gas ultrasonic flow meter, especially for the complex pipeline conditions.

Shantytown is a type of urban residential space with a long history in populated areas, it is a negative and stark space with a gradual decline in function and poverty. It is also a concentrated reflection of an unbalanced and inadequate development of the urban social space, which restricts the development of a high-quality and sustainable social economy. Taking shantytown reconstruction in Nanjing as an example, based on the information of 434 shantytown plots dating from 2008 to 2020, it combines the two typical cases of state-owned land: Xijie and collective land&mdash, Nanhe, and the questionnaire data regarding the removal and resettlement of residents, the driving mechanism and the effect of social space reconstruction of shantytown. Reconstruction is mainly discussed based on the overall understanding of the space&ndash, time characteristics of shantytown reconstruction in Nanjing. It is found that the top-down policy which transfer from the central government to the local government, the value orientation of urban growth alliance in pursuit of asset appreciation, and the interest demands and game attitude of shantytown residents from the bottom up are all important forces to promote shantytown reconstruction. Shantytown reconstruction plays a key role in improving the housing conditions of residents, it fully taps on the potential land value, thus enhancing the urban function and quality. However, the gentrification reconstruction of the original shantytown space, and the centralized resettlement of the poor groups in the urban fringes, have led to an unbalanced development of the new urban social space, with an insufficient guarantee for the removal and resettlement groups. In view of the social space problems caused by the poor people living in the outer suburbs, this paper puts forward some recommendations on policy optimization and plan adjustment of shantytown reconstruction.

High-precision time-of-flight (ToF) measurement is the key to flow rate measurement of ultrasonic gas flowmeters. At present, the measurement accuracy of cross-correlation method, which is one of ToF measurement methods, depends on the high-speed ADCs. However, these high-speed ADCs, which have high cost, cannot be applied to low-power embedded systems and meet the practical requirement for actual industrial applications. To improve the accuracy and resolution of difference of ToF(dToF) measurement under the premise of satisfying the low-power, this paper proposes a novel signal processing method based on cross-correlation and interpolation. The echo signal is sampled by ADC in low-power embedded system, and the cross-correlation calculation is performed after digital filtering. Cubic spline interpolation is applied to improve the accuracy and resolution. The effectiveness of the proposed cross-correlation combined with interpolation method was verified and discussed. Experiment results show that this proposed method can significantly improve the resolution of dToF and the accuracy of ultrasonic gas flow rate measurement. This method provides a new tool and illuminates a good potential for real-time and high-precision flow rate measurement in low-power embedded systems.

Traditional gas compressibility factor estimation methods such as AGA8-92DC and SGERG-88 usually use overly complex theoretical derivation and corresponding estimation model. This will cost most of the operating memory of the low-power gas flowmeter. Therefore, the previous models are not suitable for application on the flowmeter using the low-power embedded chips. To solve this problem, this paper proposed a novel efficient soft computing model for natural gas compressibility factor based on Group Method of Data Handling(GMDH) neural network. First, the signal of working conditions such as temperature, pressure and gas mole fraction of components are used to calculate pseudo-critical pressure and pseudo-critical temperature. Second, the soft computing model based on GMDH neural network with Corrected Akaike’s Information Criterion (AICc) is utilized by using pseudo-critical pressure and pseudo-critical temperature as training sets. For the four common natural gas types, the estimated results show that the mean absolute percentage error is only 0.0168% and the computing time is effectively reduced. It also proved that the GMDH neural network can significantly reduce the computing time and improve the accuracy of the compressibility factor. Feasibility and effectiveness of this model was verified. Our work provides a very useful way and also make it possible to real-timely estimate the natural gas compressibility factor in low-power flowmeter under the premise of satisfying the accuracy.

With the development of the low-power flowmeters, it is urgent and crucial to calculate the gas compression factor (Z-factor) in real time. However, the traditional estimation methods for Z-factor such as AGA8-92DC, needed a long calculation time and were difficult to be applied to the low-power embedded-based gas flowmeters. The other empirical correlations also had large errors. To solve this problem, we proposed a novel model for quick calculation of gas Z-factor based on Group Method of Data Handling(GMDH) network. The accuracy and reliability of this model were verified under different Tpr and Ppr. Compared with other empirical correlations, our model has the lowest root mean sum of squares of the errors (RMSE) of 0.0066 and mean absolute percentage error (MAPE) of 0.5615%, which is only 1/13–2/5 of MAPE calculated by the other correlations. The results show that our model has higher accuracy. Moreover, our model avoids lots of complex exponential and logarithmic operations, so it is especially useful for real-time Z-factor calculation of the low-power flowmeters