An Integrated Approach for Assessing the Impact of Large-Scale Future Floods on a Highway Transport System

The negative impact of climate change continues to escalate flood risk. Floods directly and indirectly damage highway systems and disturb the socioeconomic order. In this study, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. The approach has three parts: (1) a multi-agent simulation model to represent traffic, heterogeneous user demand, and route choice in a highway network; (2) a flood simulator using future runoff scenarios generated from five global climate models, three representative concentration pathways (RCPs), and the CaMa-Flood model; and (3) an impact analyzer, which superimposes the simulated floods on the highway traffic simulation system, and quantifies the flood impact on a highway system based on car following model. This approach is illustrated with a case study of the Chinese highway network. The results show that (i) for different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years; (ii) floods in different years have variable impacts on regional connectivity; and (iii) extreme flood impacts can cause huge damages in highway networks; that is, in 2030, the estimated 84.5% of routes between provinces cannot be completed when the highway system is disturbed by a future major flood. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. The approach can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.     

Efficient accuracy evaluation for multi-modal sensed data

Data accuracy is an important aspect in sensed data quality. Thus one necessary task for data quality management is to evaluate the accuracy of sensed data. However, to our best knowledge, neither measure nor effective methods for the accuracy evaluation are proposed for multi-typed sensed data. To address the problem for accuracy evaluation, we propose a systematic method. With MSE, a parameter to measure the accuracy in statistics, we design the accuracy evaluation framework for multi-modal data. Within this framework, we classify data types into three categories and develop accuracy evaluation algorithms for each category in cases of in presence and absence of true values. Extensive experimental results show the efficiency and effectiveness of our proposed framework and algorithms.     

Integrating Entropy‐Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net‐water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics‐based entropy method. The weighted indices were input into the WNB‐based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image‐based sampling and validation, cell‐by‐cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood‐related environmental hazard studies.     

Major Factors Influencing the Health Impacts from Controlling Air Pollutants with Nonlinear Chemistry: An Application to China

Predicting the human‐health effects of reducing atmospheric emissions of nitrogen oxide (NO_x) emissions from power plants, motor vehicles, and other sources is complex because of nonlinearity in the relevant atmospheric processes. We estimate the health impacts of changes in fine particulate matter (PM_2.5) and ozone concentrations that result from control of NO_x emissions alone and in conjunction with other pollutants in and outside the mega‐city of Shanghai, China. The Community Multiscale Air Quality (CMAQ) Modeling System is applied to model the effects on atmospheric concentrations of emissions from different economic sectors and geographic locations. Health impacts are quantified by combining concentration‐response functions from the epidemiological literature with pollutant concentration and population distributions. We find that the health benefits per ton of emission reduction are more sensitive to the location (i.e., inside vs. outside of Shanghai) than to the sectors that are controlled. For eastern China, we predict between 1 and 20 fewer premature deaths per year per 1,000 tons of NO_x emission reductions, valued at $300–$6,000 per ton. Health benefits are sensitive to seasonal variation in emission controls. Policies to control NO_x emissions need to consider emission location, season, and simultaneous control of other pollutants to avoid unintended consequences.     

Exploring Factors Influencing the Risky Cycling Behaviors of Young Cyclists Aged 15–24 Years: A Questionnaire-Based Study in China

Road traffic crashes are the leading cause of death for young people, among whom cyclists account for a higher percentage of injuries and deaths than any other road users. This study aimed to examine the factor structure of the Young Cyclist Behavior Questionnaire (YCBQ) and investigate the relationships among demographic characteristics, cycling use-related variables, perceived risk, perceived cycling skills, and risky cycling behaviors among young people. A sample of 448 cyclists (mean age of 20.37 years) completed the questionnaire. Exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were utilized. The YCBQ had a clear factorial structure, items with high factor loadings, and good internal consistency. The five-factor structure included traffic violations, impulsive behaviors, ordinary violations, distractions, and errors. Risky cycling behaviors could be explained by gender, age, perceived risk, and perceived cycling skills, with the model explaining 37% of the variance. Gender had the greatest impact on risky cycling behaviors; male individuals were more likely to engage in risky behaviors. Young cyclists with higher levels of perceived risk had lower probabilities of engaging in risky cycling behaviors. Cyclists with lower scores on perceived cycling skills were more likely to report engaging in risky cycling behaviors. Age significantly explained risky behaviors; the younger the cyclist was, the higher his or her risky behaviors score. This research provides a theoretical foundation for the prevention of risky behaviors among young cyclists. Regarding intervention design, attention to the identified gender differences, the need to strengthen the ability to perceive risk, and the importance of road safety education for young cyclists may promote safer cycling.     

机构投资者持股比例与上市公司盈余管理的实证研究

本文分析了机构投资者在完善上市公司治理结构、约束管理层盈余管理中的作用。首先通过管理者与机构投资者的博弈分析发现,当机构持股比例很低时,机构不存在监管意愿,只有当机构持股比例较高时,才会积极地监管盈余管理。其次,以2004-2006年沪深两市的上市公司为样本,通过实证研究发现盈余管理与机构投资者持股之间存在倒U型关系,持股比例13.07%是机构投资者是否有效抑制盈余管理行为的临界值;盈余管理程度与机构股东的数量呈正相关关系,即随着上市公司中机构股东数量的增加,机构的监管效率会下降。     

制度压力如何影响企业社会责任:基于旅游企业的实证研究

本文以制度理论为视角,通过对404家旅游企业高管的调研,对制度压力(规制、规范和认知)作为影响企业社会责任的重要前因变量开展了实证研究.结果表明,不同的制度压力对企业的社会责任影响程度不尽相同,规范压力最大,认知压力次之,规制压力影响的统计结果不显著.政治关联在制度压力和企业社会责任之间起部分中介作用,规范压力和认知压力越大,越容易导致企业政治关联行为,而规制压力正相反.最后,从中国情境下企业行为的制度前因和“关系”文化以及政治关联与企业社会责任关系等方面进行了讨论.     

服务补救方式对消费者情绪和行为意向的影响

本文采用情景模拟方法,以饭店服务失误和补救为例,分别测评了主动补救和被动补救对消费者情绪和行为意向的影响,结果发现,服务补救方式对消费者情绪和行为意向的影响存在显著差别,其中主动补救对消费者正面情绪、口碑传播和重购意向的影响显著高于被动补救,而对消费者负面情绪的影响显著低于被动补救;获得服务补救消费者的正面情绪与口碑传播和重购意向呈显著正相关,而消费者负面情绪与口碑传播和重购意向呈显著负相关。     

Application of Design for Six Sigma tools in telecom service improvement

Abstract

Six Sigma is a systematic methodology aiming at operational excellence. Under the general umbrella of Six Sigma, DMAIC and Design for Six Sigma are usually be implemented to improve product and process quality. Facing quality bottlenecks in traditional improvement projects, tools in Design for Six Sigma is an efficient way to achieve better results. In this paper, to meet the high-quality requirement of key clients, a telecom service system improvement project is illustrated by redesigning troubleshooting and service delivery processes in company Shanghai Telecom Co. Ltd. (ST) with the aid of some Design for Six Sigma tools. Company ST improved its service system performance and obtained significant economic benefits from this project implementation. Four key factors that affect the success of Design for Six Sigma tools application in improvement projects are discussed at last.     

政治关联视角的民营企业行业进入选择与绩效研究:基于2005-2010年民营上市企业的实证检验

近年来国家不断推出产业政策进行产业结构调整以引导民营经济的健康发展,而民营企业能否响应产业政策,并调整其行业选择是本文关注的重点问题。本文首次基于政治关联的视角,实证分析2005年颁布的产业政策对民营企业进入鼓励性行业的影响,进而深入探讨民营企业进入政府鼓励性行业的程度及进入后民营企业绩效的变化。结果表明,短期来看民营企业在政府鼓励性行业投资没有显著增加,但从长期来看民营企业的行业进入选择受政府产业政策影响较大,而选择进入政府鼓励性行业的民营企业绩效短期内劣于未进入鼓励性行业的民营企业;政治关联在民营企业行业选择中有较显著的负向影响;具有政治关联的民营企业普遍选择不进入鼓励性行业,其绩效也较好。本文的结论不仅在理论方面拓宽了市场进入、政治关联等研究领域,而且在实践方面为我国产业政策的实施效果提供了数据支持和决策依据。