经济时间序列的非线性组合建模与预测方法研究

基于模糊系统在紧立集中能够任意逼近非线性连续函数的特性,本文提出了一种基于Takagi-Sugeno模糊规则基的非线性组合预测新方法,以克服线性组合预测方法在解决非平稳时间序列组合建模问题所遇到的困难和存在的不足,并采用相应的遗传算法确定模糊系统的参数及模糊子集的划分。理论分析和大量的应用实例表明:该方法具有很强的学习与泛化能力,在处理诸如经济时间序列这种具有一定程度不确性的非线性系统的组合建模与预测方面有很好的应用价值。     

What Are the Sources of Exposure to Eight Frequently Used Phthalic Acid Esters in Europeans?

Phthalic acid esters (phthalates) are used as plasticizers in numerous consumer products, commodities, and building materials. Consequently, phthalates are found in human residential and occupational environments in high concentrations, both in air and in dust. Phthalates are also ubiquitous food and environmental contaminants. An increasing number of studies sampling human urine reveal the ubiquitous phthalate exposure of consumers in industrialized countries. At the same time, recent toxicological studies have demonstrated the potential of the most important phthalates to disturb the human hormonal system and human sexual development and reproduction. Additionally, phthalates are suspected to trigger asthma and dermal diseases in children. To find the important sources of phthalates in Europeans, a scenario-based approach is applied here. Scenarios representing realistic exposure situations are generated to calculate the age-specific range in daily consumer exposure to eight phthalates. The scenarios demonstrate that exposure of infant and adult consumers is caused by different sources in many cases. Infant consumers experience significantly higher daily exposure to phthalates in relation to their body weight than older consumers. The use of consumer products and different indoor sources dominate the exposure to dimethyl, diethyl, benzylbutyl, diisononyl, and diisodecyl phthalates, whereas food has a major influence on the exposure to diisobutyl, dibutyl, and di-2-ethylhexyl phthalates. The scenario-based approach chosen in the present study provides a link between the knowledge on emission sources of phthalates and the concentrations of phthalate metabolites found in human urine.     

Can oral toxicity data for PFAS inform on toxicity via inhalation?

Per- and poly-fluoroalkyl substances (PFAS) are ubiquitous in the environment and are detected in wildlife and humans. With respect to human exposure, studies have shown that ingestion is the primary route of exposure; however, in certain settings, exposure via inhalation could also be a significant source of exposure. While many studies examined toxicity of PFAS via ingestion, limited information is available for PFAS toxicity via the inhalation route, translating into a lack of exposure guidelines. Consequently, this article examined whether route-to-route extrapolation to derive guidelines for inhalation exposure is appropriate for PFAS. Perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) were used as exemplary PFAS given the abundance of toxicity data for these two compounds. Our evaluation determined that available toxicity and toxicokinetic data support route-to-route extrapolation for PFAS in order to derive inhalation-based standards. Results from this analysis suggest that an air concentration of 7.0 × 10⁻⁵ mg/m³ (or 0.07 μg/m³) would be an appropriate RfC for PFOA and PFOS assuming the 2016 EPA RfD of 0.00002 mg/kg-day, whereas use of the interim RfDs proposed in 2022 of 1.5 × 10⁻⁹ and 7.9 × 10⁻⁹ mg/kg would yield much lower RfCs of 5.25 × 10⁻⁹ and 2.77 × 10⁻⁸ mg/m³ (or 5.25 × 10⁻⁶ and 2.77 × 10⁻⁵ μg/m³) for PFOA and PFOS, respectively.     

A Comparison of Regulatory Implications of Traditional and Exact Two-Stage Dose–Response Models

We compare the regulatory implications of applying the traditional (linearized) and exact two-stage dose–response models to animal carcinogenic data. We analyze dose–response data from six studies, representing five different substances, and we determine the goodness-of-fit of each model as well as the 95% confidence lower limit of the dose corresponding to a target excess risk of 10^–5 (the target risk dose TRD). For the two concave datasets, we find that the exact model gives a substantially better fit to the data than the traditional model, and that the exact model gives a TRD that is an order of magnitude lower than that given by the traditional model. In the other cases, the exact model gives a fit equivalent to or better than the traditional model. We also show that although the exact two-stage model may exhibit dose–response concavity at moderate dose levels, it is always linear or sublinear, and never supralinear, in the low-dose limit. Because regulatory concern is almost always confined to the low-dose region extrapolation, supralinear behavior seems not to be of regulatory concern in the exact two-stage model. Finally, we find that when performing this low-dose extrapolation in cases of dose–response concavity, extrapolating the model fit leads to a more conservative TRD than taking a linear extrapolation from 10% excess risk. We conclude with a set of recommendations.     

径向基函数网络结构确定的新算法

讨论了对于径向基函数网络的一种结构确定双向回归新算法．在这种算法中,采用正交化手段和“新息-贡献”准则,可同时确定径向基函数网络的结构（即中心点）和参数估计,不仅克服了传统算法的从一大组输入数据中任意选择中心点常出现的数值病态和选择结果依赖于数据排列顺序等问题,而且大大减少了计算量且保证能得到唯一的最优网络描述．最后用仿真例子说明了这种新算法的正确性和有效性．     

Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety

Anatomic pathology (AP) laboratories provide critical diagnostic information that help determine patient treatments and outcomes, but the risks of AP operations and their impact on patient safety and quality of care remain poorly recognized and undermanaged. Hospital-based laboratories face an operational and risk management challenge because clinical work of unknown quantity and complexity arrives with little advance notice, which results in fluctuations in workload that can push operations beyond planned capacity, leading to diagnostic delays and potential errors. Modeling the dynamics of workload and complexity in AP offers the opportunity to better use available information to manage risks. We developed a stock-and-flow model of a typical AP laboratory operation and identified key exogenous inputs that drive AP work. To test the model, we generated training and validations data sets by combining data from the electronic medical records and laboratory information systems over multiple years. We demonstrate the implementation of 10-day AP work forecast generated on a daily basis, and show its performance in comparison with actual work. Although the model somewhat underpredicts work as currently implemented, it provides a framework for prospective management of resources to ensure quality during workload surges. Although full implementation requires additional model development, we show that AP workload largely depends on few and accessible clinical inputs. Recognizing that level loading of work in a hospital is not practical, predictive modeling of work can empower laboratories to triage, schedule, or mobilize resources more effectively and better manage risks that reduce the quality or timeliness of diagnostic information.     

Systems‐Based Guiding Principles for Risk Modeling,Planning, Assessment,Management, and Communication

This article is grounded on the premise that the complex process of risk assessment, management, and communication, when applied to systems of systems, should be guided by universal systems‐based principles. It is written from the perspective of systems engineering with the hope and expectation that the principles introduced here will be supplemented and complemented by principles from the perspectives of other disciplines. Indeed, there is no claim that the following 10 guiding principles constitute a complete set; rather, the intent is to initiate a discussion on this important subject that will incrementally lead us to a more complete set of guiding principles. The 10 principles are as follows: First Principle: Holism is the common denominator that bridges risk analysis and systems engineering. Second Principle: The process of risk modeling, assessment, management, and communication must be systemic and integrated. Third Principle: Models and state variables are central to quantitative risk analysis. Fourth Principle: Multiple models are required to represent the essence of the multiple perspectives of complex systems of systems. Fifth Principle: Meta‐modeling and subsystems integration must be derived from the intrinsic states of the system of systems. Sixth Principle: Multiple conflicting and competing objectives are inherent in risk management. Seventh Principle: Risk analysis must account for epistemic and aleatory uncertainties. Eighth Principle: Risk analysis must account for risks of low probability with extreme consequences. Ninth Principle: The time frame is central to quantitative risk analysis. Tenth Principle: Risk analysis must be holistic, adaptive, incremental, and sustainable, and it must be supported with appropriate data collection, metrics with which to measure efficacious progress, and criteria on the basis of which to act. The relevance and efficacy of each guiding principle is demonstrated by applying it to the U.S. Federal Aviation Administration complex Next Generation (NextGen) system of systems.     

公仆型领导、员工集体和个人的工作态度对服务质量的影响

作者在13家宾馆和餐馆进行了一次实证研究,探讨饭店管理人员的公仆型领导风格、员工集体和个人的工作态度对员工的服务质量的影响。多层次线性模型分析结果表明,部门的公仆型领导氛围会直接影响员工集体的工作满意感,并通过集体情感性归属感,间接影响员工的服务质量。部门的公仆型领导氛围会调节员工感知的部门负责人的公仆型领导风格对他们的服务质量的影响;员工集体的情感性归属感会调节员工个人的工作满意感和情感性归属感对服务质量的影响。     

Preparation of chemotherapy drugs: Planning policy for reduced waiting times

This study investigates the impact of pharmacy policies on patient waiting time in the Chemotherapy Day Unit of the Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital. The project evaluated whether a reduction in waiting time resulting from medication orders being prepared in advance of patient appointments was justified, given that medications prepared in advance are wasted when patients arrive too sick for treatment. Within this context, we derive analytic expressions to approximate patient waiting times and wastage costs, allowing management to see the tradeoff between these two metrics for different policies. Using a case study and a simulation model, the approximations are evaluated. The use of analytic expressions allows the analysis to be easily repeated when medication costs change or when new medications/protocols are introduced. In the same vein, other hospitals with different patient case mixes can easily complete the analysis in their settings. Finally, the outcome from this study resulted in a new policy at the cancer center which is expected to decrease the waiting time by half, while only increasing pharmacy's costs by 1-2%.     

Uncertainties in Pharmacokinetic Modeling for Perchloroethylene. I. Comparison of Model Structure, Parameters, and Predictions for Low-Dose Metabolism Rates for Models Derived by Different Authors

In recent years physiologically based pharmacokinetic models have come to play an increasingly important role in risk assessment for carcinogens. The hope is that they can help open the black box between external exposure and carcinogenic effects to experimental observations, and improve both high-dose to low-dose and interspecies projections of risk. However, to date, there have been only relatively preliminary efforts to assess the uncertainties in current modeling results. In this paper we compare the physiologically based pharmacokinetic models (and model predictions of risk-related overall metabolism) that have been produced by seven different sets of authors for perchloroethylene (tetrachloroethylene). The most striking conclusion from the data is that most of the differences in risk-related model predictions are attributable to the choice of the data sets used for calibrating the metabolic parameters. Second, it is clear that the bottom-line differences among the model predictions are appreciable. Overall, the ratios of low-dose human to bioassay rodent metabolism spanned a 30-fold range for the six available human/rat comparisons, and the seven predicted ratios of low-dose human to bioassay mouse metabolism spanned a 13-fold range. (The greater range for the rat/human comparison is attributable to a structural assumption by one author group of competing linear and saturable pathways, and their conclusion that the dangerous saturable pathway constitutes a minor fraction of metabolism in rats.) It is clear that there are a number of opportunities for modelers to make different choices of model structure, interpretive assumptions, and calibrating data in the process of constructing pharmacokinetic models for use in estimating "delivered" or "biologically effective" dose for carcinogenesis risk assessments. We believe that in presenting the results of such modeling studies, it is important for researchers to explore the results of alternative, reasonably likely approaches for interpreting the available data--and either show that any conclusions they make are relatively insensitive to particular interpretive choices, or to acknowledge the differences in conclusions that would result from plausible alternative views of the world.