服务型制造网络的Holon协同需求评价方法

依据复杂网络理论分析服务型制造网络Holon协同需求问题，给出一种考虑以直觉正态模糊数表示且多Holon协同的服务型制造网络协同需求评价方法。首先，在区分Holon复合协同和递归协同基础上构建服务型制造网络协同需求评价指标体系；其次，考虑基于网络结构特征的Holon相似稳定性，根据服务型制造网络节点相似性测度分析Holon协同需求特征，并测算不同相似性测度修正的Holon网络结构熵；再次，建立面向服务型制造网络和Holon的有专家信息双向触动反馈机制，反馈包括评价指标和网络协同状况，反馈Holon信息包括基于服务型制造网络演化方向的Holon间协同需求建议和基于服务型制造网络现有状态的网络连通性Holon协同需求建议；最后由INFCWA（INFCWA_R）算子或INFCWG（INFCWG_R）算子分别对复合协同和递归协同评价信息集结并得出评价结论。方法设计过程通过例证分析说明根据服务型制造网络目标控制的Holon协同需求评价改进了群组评价效率。     

Communicating Geographical Risks in Crisis Management: The Need for Research

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.     

Meningococcal serogroup B outbreak response University of Wisconsin-Madison

A meningococcal serogroup B (MenB) outbreak at a large public university prompted an emergency response to immunize undergraduates. Objective: To report on a successful meningococcal serogroup B (MenB) vaccine clinic response at a large public university. Methods: We assembled the team leaders to write this case report. Results: Activation of the emergency plan and points of dispensing required cooperation of many units on campus under the leadership of university health officials with support from Centers for Disease Control and Prevention, state division of public health and the city-county health department. Significant efforts to provide consistent messages to students and parents regarding the outbreak and the availability of the MenB vaccines were made. Volunteers were recruited to staff the clinics alongside university healthcare providers. Over 22,000 doses of vaccine were administered. Conclusion: We report our experience and lessons learned which may be helpful to universities in preventing and responding to disease outbreaks.     

An improved cluster two-stage randomized response model

In this paper, Abdelfatah and Mazloum's (2015) two-stage randomized response model is extended to unequal probability sampling and stratified unequal probability sampling, both with and without replacement. The extended models result in more efficient estimators than Lee et al.'s (2014) estimators of the proportion of the population having a sensitive attribute.     

Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing

Abstract

A central objective of empirical research on treatment response is to inform treatment choice. Unfortunately, researchers commonly use concepts of statistical inference whose foundations are distant from the problem of treatment choice. It has been particularly common to use hypothesis tests to compare treatments. Wald’s development of statistical decision theory provides a coherent frequentist framework for use of sample data on treatment response to make treatment decisions. A body of recent research applies statistical decision theory to characterize uniformly satisfactory treatment choices, in the sense of maximum loss relative to optimal decisions (also known as maximum regret). This article describes the basic ideas and findings, which provide an appealing practical alternative to use of hypothesis tests. For simplicity, the article focuses on medical treatment with evidence from classical randomized clinical trials. The ideas apply generally, encompassing use of observational data and treatment choice in nonmedical contexts.     

国家级新区义务教育资源供需矛盾及消解路径——基于N市J区12345政务热线数据分析

成立国家级新区对迅速改善欠发达地区经济环境、改变落后的民生状态进而促进城市区域发展具有重要意义。国家级新区应努力推动社会公共服务体系与产业发展体系协同共进。义务教育资源是社会公共服务体系的重要组成部分，不容忽视。国家级新区在初创的关键时期需要打好义务教育资源均衡分配的基础，为后续义务教育资源的优化升级提供坚实保障。本文以N市J区为例，通过对12345政务热线服务平台的数据分析和对关键人物的访谈，探讨国家级新区初创期义务教育资源需求特征，同时发现实际存在着义务教育资源供不应求、供需脱节、配置失衡、提质升级乏力等供需矛盾，并依据新区初创阶段的实际需求，从制定规划、资金支持、供需互动、创新升级等方面进一步探讨国家级新区义务教育资源供需矛盾的消解路径。     

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.     

Response-adaptive trial designs with accelerated Thompson sampling

In a clinical trial, sometimes it is desirable to allocate as many patients as possible to the best treatment, in particular, when a trial for a rare disease may contain a considerable portion of the whole target population. The Gittins index rule is a powerful tool for sequentially allocating patients to the best treatment based on the responses of patients already treated. However, its application in clinical trials is limited due to technical complexity and lack of randomness. Thompson sampling is an appealing approach, since it makes a compromise between optimal treatment allocation and randomness with some desirable optimal properties in the machine learning context. However, in clinical trial settings, multiple simulation studies have shown disappointing results with Thompson samplers. We consider how to improve short-run performance of Thompson sampling and propose a novel acceleration approach. This approach can also be applied to situations when patients can only be allocated by batch and is very easy to implement without using complex algorithms. A simulation study showed that this approach could improve the performance of Thompson sampling in terms of average total response rate. An application to a redesign of a preference trial to maximize patient's satisfaction is also presented.     

中国智能经济的涌现机制

我国智能经济的发展内生于经济转型升级过程中创造的智能化需求。与AI 1.0不同，新一代人工智能(AI 2.0)不再是简单用计算机模拟人的智能，而是基于网络空间发展的数据智能，即智能机器、人和网络相互融合的智能系统。智能经济是以数据和计算为“关键生产要素”的新经济形态，包括人工智能的产业化和产业的智能化过程中创造的新产品、新技术、新模式和新业态。互联网，尤其是移动互联网发展中创造出的数据生态优势是中国人工智能科技产业发展的前提和基础。在拥有数据生态优势的条件下，强烈的需求牵引、产学研协同创新、创新生态系统的高度开放性和核心产业部门与融合产业部门的融合发展，共同构成了中国智能经济发展的关键机制。     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.