近二十年中国化工翻译研究——以知网收录重点期刊文献为例

选取中国近二十年的化工翻译期刊文献为研究样本,以Excel为统计工具统计了中国化工翻译研究文献的数量,归纳整理了化工翻译的研究角度;然后以知网收录核心期刊文献为分析样本,从理论指导、运用、翻译策略、技巧和方法等角度对其进行对比与分析,归纳总结出当前化工翻译所存在的不足。     

膜传质的统计力学模型研究

本文根据压力驱动膜分离过程的特点,采用统计力学传质模型,通过相关的热力学理论和数学方法,建立了考虑粘性流动在内的适用于低浓度非理想多组分溶液的可实际应用的统计力学膜传质模型,并通过实验对该模型进行了检验,结果表明该模型与实验结果符合较好.     

Statistics on statistics: Measuring research productivity by journal publications between 1985 and 1995

Sixteen international journals publishing statistical theory were surveyed over the 11-year period beginning in 1985. Paper, author and adjusted page counts yield cursory measures of productivity for institutions and countries that contributed to fundamental statistical research during that period. These data clearly identify Canada as one of the main contributors to the development of the discipline in the past decade. They also provide valuable information on the evolution of publication habits, in terms of the volume of research, the length of papers, coauthorship practices, etc.     

Using repeated-prevalence data in multi-state modeling of renal replacement therapy

Multi-state models help predict future numbers of patients requiring specific treatments but these models require exhaustive incidence data. Deriving reliable predictions from repeated-prevalence data would be helpful. A new method to model the number of patients that switch between therapeutic modalities using repeated-prevalence data is presented and illustrated. The parameters and goodness of fit obtained with the new method and repeated-prevalence data were compared to those obtained with the classical method and incidence data. The multi-state model parameters’ confidence intervals obtained with annually collected repeated-prevalence data were wider than those obtained with incidence data and six out of nine pairs of confidence intervals did not overlap. However, most parameters were of the same order of magnitude and the predicted patient distributions among various renal replacement therapies were similar regardless of the type of data used. In the absence of incidence data, a multi-state model can still be successfully built with annually collected repeated-prevalence data to predict the numbers of patients requiring specific treatments. This modeling technique can be extended to other chronic diseases.     

Monitoring means and covariances of multivariate non linear time series with heavy tails

In this paper, the focus is on sequential analysis of multivariate financial time series with heavy tails. The mean vector and the covariance matrix of multivariate non linear models are simultaneously monitored by modifying conventional control charts to identify structural changes in the data. The considered target process is a constant conditional correlation model (cf. Bollerslev, 1990 Bollerslev, T. (1990). Modeling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Rev. Econ. Stat. 72:498–505.[Crossref], [Web of Science ®] , [Google Scholar]), an extended constant conditional correlation model (cf. He and Teräsvirta, 2004 He, C., Teräsvirta, T. (2004). An extended constant conditional correlation GARCH model and its fourth-moment structure. Economet. Theory 20:904–926.[Crossref], [Web of Science ®] , [Google Scholar]), a dynamic conditional correlation model (cf. Engle, 2002 Engle, R.F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. J. Bus. Econ. Stat. 20(3):339–350.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), or a generalized dynamic conditional correlation model (cf. Capiello et al., 2006 Capiello, L., Engle, R., Sheppard, K. (2006). Asymmetric correlations in the dynamics of global equity and bond returns. J. Financial Economet. 4(4):537–572.[Crossref] , [Google Scholar]). For statistical surveillance we use control charts based on residuals. Further, the procedures are constructed for t-distribution. The detection speed of these charts is compared via Monte Carlo simulation. In the empirical study, the procedure with the best performance is applied to log-returns of the stock market indices FTSE and CAC.     

泛珠三角区域多层经济增长理论模型及应用研究——基于社会嵌入性视角的分析

通过有效整合经济增长、社会嵌入性和多层统计模型三个方面的理论,构建了基于嵌入视角的多层经济增长理论模型,解决了经济增长影响因素的作用路径问题以及社会嵌入性变量对经济增长影响的精确性问题。利用泛珠三角区域1996—2013年的面板数据,实证分析了嵌入视角下泛珠三角区域的经济增长。结果表明:在泛珠三角区域经济增长的整体差异中,60.34%的份额是由经济增长的社会嵌入性造成的。资本存量为正向显著直接影响因素,产值结构为负向显著直接影响因素;经济增长百度指数、外资依存度和财政支出规模既能显著直接影响经济增长又能调节资本存量与经济增长之间的正向关系,市场化率对资本存量与经济增长之间的正向关系具有显著的调节作用;经济增长百度指数、外资依存度还能调节人力资本与经济增长之间的正向关系。因此,提高经济增长要同时考虑经济增长基本因素与社会嵌入性因素。     

Examining the Impact of Demographic Factors on Air Pollution

This study adds to the emerging literature examining empirically the link between population size, other demographic factors and pollution. We contribute by using more reliable estimation techniques and examine two air pollutants. By considering sulfur dioxide, we become the first study to explicitly examine the impact of demographic factors on a pollutant other than carbon dioxide at the cross-national level. We also take into account the urbanization rate and the average household size neglected by many prior cross-national econometric studies. For carbon dioxide emissions we find evidence that population increases are matched by proportional increases in emissions while a higher urbanization rate and lower average household size increase emissions. For sulfur dioxide emissions, we find a U-shaped relationship, with the population-emissions elasticity rising at higher population levels. Urbanization and average household size are not found to be significant determinants of sulfur dioxide emissions. For both pollutants, our results suggest that an increasing share of global emissions will be accounted for by developing countries. Implications for the environmental Kuznets curve literature are described and directions for further work identified.     

Statistical Distributions of Daily Breathing Rates for Narrow Age Groups of Infants and Children

Children may be more susceptible to toxicity from some environmental chemicals than adults. This susceptibility may occur during narrow age periods (windows), which can last from days to years depending on the toxicant. Breathing rates specific to narrow age periods are useful to assess inhalation dose during suspected windows of susceptibility. Because existing breathing rates used in risk assessment are typically for broad age ranges or are based on data not representative of the population, we derived daily breathing rates for narrow age ranges of children designed to be more representative of the current U.S. children's population. These rates were derived using the metabolic conversion method of Layton (1993) and energy intake data adjusted to represent the U.S. population from a relatively recent dietary survey (CSFII 1994–1996, 1998). We calculated conversion factors more specific to children than those previously used. Both nonnormalized (L/day) and normalized (L/kg-day) breathing rates were derived and found comparable to rates derived using energy estimates that are accurate for the individuals sampled but not representative of the population. Estimates of breathing rate variability within a population can be used with stochastic techniques to characterize the range of risk in the population from inhalation exposures. For each age and age-gender group, we present the mean, standard error of the mean, percentiles (50th, 90th, and 95th), geometric mean, standard deviation, 95th percentile, and best-fit parametric models of the breathing rate distributions. The standard errors characterize uncertainty in the parameter estimate, while the percentiles describe the combined interindividual and intra-individual variability of the sampled population. These breathing rates can be used for risk assessment of subchronic and chronic inhalation exposures of narrow age groups of children.     

Accounting for Both Random Errors and Systematic Errors in Uncertainty Propagation Analysis of Computer Models Involving Experimental Measurements with Monte Carlo Methods

A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.     

Women's Changing Attitudes Toward Divorce, 1974–2002: Evidence for an Educational Crossover

This article examines trends in divorce attitudes of young adult women in the United States by educational attainment from 1974 to 2002. Women with 4‐year college degrees, who previously had the most permissive attitudes toward divorce, have become more restrictive in their attitudes toward divorce than high school graduates and women with some college education, whereas women with no high school diplomas have increasingly permissive attitudes toward divorce. We examine this educational crossover in divorce attitudes in the context of variables correlated with women's educational attainment, including family attitudes and religion, income and occupational prestige, and family structure. We conclude that the educational crossover in divorce attitudes is associated most strongly with work and family structure variables.