Reliability Analysis for Hazardous Waste Treatment Processes

The reliability of a treatment process is addressed in terms of achieving a regulatory effluent concentration standard and the design safety factors associated with the treatment process. This methodology was then applied to two aqueous hazardous waste treatment processes: packed tower aeration and activated sludge (aerobic) biological treatment. The designs achieving 95 percent reliability were compared with those designs based on conventional practice to determine their patterns of conservatism. Scoping-level treatment costs were also related to reliability levels for these treatment processes. The results indicate that the reliability levels for the physical/chemical treatment process (packed tower aeration) based on the deterministic safety factors range from 80 percent to over 99 percent, whereas those for the biological treatment process range from near 0 percent to over 99 percent, depending on the compound evaluated. Increases in reliability per unit increase in treatment costs are most pronounced at lower reliability levels (less than about 80 percent) than at the higher reliability levels (greater than 90 percent, indicating a point of diminishing returns. Additional research focused on process parameters that presently contain large uncertainties may reduce those uncertainties, with attending increases in the reliability levels of the treatment processes.     

保温层的临界热绝缘直径

通过对传热速率随保温层直径增加而变化的讨论,得出结论,保温层直径必须大于保温层的临界热绝缘直径,才能降低热损失。     

扩散硅压力传感器零点温漂补偿的简便方法

从电桥平衡出发,提出对电桥桥臂一串一并的十几种补偿方法归结为两种,从而简化了数学计算和补偿工作的繁杂性,并对导出的两套公式的使用方法,加以说明,理论和实验得到了较好的验证。     

人工智能背景下医疗法律责任的现实困境与完善路径

医疗人工智能利用深度学习和计算机算法等现代化技术,实现辅助诊疗、医学影像、药物发掘、健康诊疗等功能,是人工智能应用最具前景的领域之一。随着人工智能技术发展,具备自主思维和决策能力的智能机器人医生等人工智能将对现有的医疗法律责任制度带来颠覆性影响。在人工智能背景下,医疗人工智能是否具有独立法律主体地位、传统的医疗过错责任归责能否适用、因果关系如何认定等成为现代民事法律责任制度的严峻挑战,也成为实践中的必答之问。因此,在借鉴欧美人工智能领域的探索经验的基础上,在当前阶段通过修正和完善我国民事法律责任制度,提出针对不同的人工智能侵权情形适用差别化归责原则,并尝试建立人工智能强制保险或储备金制度,设置人工智能监管机构及行业标准,以实现既保障患者合法公平权益,又鼓励人工智能技术有效发展之间的平衡。     

Heterogeneous treatment effects on Children's cognitive/non-cognitive skills: A reevaluation of an influential early childhood intervention

The 1962–67 High/Scope Perry Preschool Program, a well-known experimental early childhood intervention study that provided quality preschool education to disadvantaged children, has been shown to have had positive impacts on early child development and on a variety of adulthood outcomes. However, most previous analyses have only examined average treatment effects across all program participants without exploring possible effect heterogeneity by children's background characteristics. We investigated this question by first using the 1964–65 Current Population Survey data in combination with the Perry data to construct a scale of child socioeconomic status based on the estimated propensity for inclusion in the Perry program, then analyzing effect heterogeneity within the Perry sample by strata of our socioeconomic scale. We found that the treatment effects of enrollment in the Perry preschool on cognitive and non-cognitive skills were much larger and more persistent among the most disadvantaged children than among others in the Perry program. Furthermore, among the most disadvantaged children, the treatment (i.e., preschool enrollment) affects later outcomes through a reinforcement mechanism of skill development (i.e., early cognitive gain leads to a non-cognitive gain, which in turn leads to later cognitive gain) and a sequential improvement of cognitive skills over time. These findings have important implications for the evaluation of policy interventions in early child development using experimental data.     

Improved communality estimation in factor analysis

The symmetric treatment of an asymmetric approach to factor analysis is shown to provide accurate communality estimates. In comparison with existing estimates including upper and lower bounds, the present approach is shown to be superior. In one example with known communalities, the present approach perfectly captures those communalities. In two empirical examples, it is shown to produce better communality estimates than any existing method.     

Modelling of connected processes

The problem of comparing, contrasting and combining information from different sets of data is an enduring one in many practical applications of statistics. A specific problem of combining information from different sources arose in integrating information from three different sets of data generated by three different sampling campaigns at the input stage as well as at the output stage of a grey-water treatment process. For each stage, a common process trend function needs to be estimated to describe the input and output material process behaviours. Once the common input and output process models are established, it is required to estimate the efficiency of the grey-water treatment method. A synthesized tool for modelling different sets of process data is created by assembling and organizing a number of existing techniques: (i) a mixed model of fixed and random effects, extended to allow for a nonlinear fixed effect, (ii) variogram modelling, a geostatistical technique, (iii) a weighted least squares regression embedded in an iterative maximum-likelihood technique to handle linear/nonlinear fixed and random effects and (iv) a formulation of a transfer-function model for the input and output processes together with a corresponding nonlinear maximum-likelihood method for estimation of a transfer function. The synthesized tool is demonstrated, in a new case study, to contrast and combine information from connected process models and to determine the change in one quality characteristic, namely pH, of the input and output materials of a grey-water filtering process.     

Is regression adjustment supported by the Neyman model for causal inference?

This paper examines both theoretically and empirically whether the common practice of using OLS multivariate regression models to estimate average treatment effects (ATEs) under experimental designs is justified by the Neyman model for causal inference. Using data from eight large U.S. social policy experiments, the paper finds that estimated standard errors and significance levels for ATE estimators are similar under the OLS and Neyman models when baseline covariates are included in the models, even though theory suggests that this may not have been the case. This occurs primarily because treatment effects do not appear to vary substantially across study subjects.     

Teacher shortages, teacher contracts and their effect on education in Africa

Summary.  To enhance primary enrolment rates, many African countries have launched large teacher recruitment programmes in recent years. Given tight budgetary constraints, teachers are no longer employed in civil service positions, but on the basis of (fixed term) contracts typically implying considerably lower salaries and a sharply reduced amount of professional training. We analyse the effect of this change on educational quality in Niger, Togo and Mali, on the basis of very informative data, which are comparable across these countries. We use a variety of estimation techniques, including a non-parametric estimation of quantile treatment effects. Our results demonstrate that contract teachers tend to reduce inequalities in student outcomes. Overall, the effects are positive in Mali, somewhat mixed in Togo and negative in Niger. This ordering is consistent with theoretical expectations related to the manner in which contract teacher programmes were implemented differently in each of the three countries under study.     

Comparison of causal effect estimators under exposure misclassification

Over the past decades, various principles for causal effect estimation have been proposed, all differing in terms of how they adjust for measured confounders: either via traditional regression adjustment, by adjusting for the expected exposure given those confounders (e.g., the propensity score), or by inversely weighting each subject's data by the likelihood of the observed exposure, given those confounders. When the exposure is measured with error, this raises the question whether these different estimation strategies might be differently affected and whether one of them is to be preferred for that reason. In this article, we investigate this by comparing inverse probability of treatment weighted (IPTW) estimators and doubly robust estimators for the exposure effect in linear marginal structural mean models (MSM) with G-estimators, propensity score (PS) adjusted estimators and ordinary least squares (OLS) estimators for the exposure effect in linear regression models. We find analytically that these estimators are equally affected when exposure misclassification is independent of the confounders, but not otherwise. Simulation studies reveal similar results for time-varying exposures and when the model of interest includes a logistic link.