Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

The EPQ with partial backordering and phase-dependent backordering rate

In previous work we established a closed-form optimal stocking strategy for an EPQ model with partial backordering at a constant rate β. Here we extend this work to allow for the possibility that the percentage of demand backordered will increase when production starts again. We show how our previous model can be adapted to find the optimal decision variable values for this new assumption and develop the condition that the initial value of β must meet for partial backordering to be optimal.     

Spectral estimation for locally stationary time series with missing observations

Time series arising in practice often have an inherently irregular sampling structure or missing values, that can arise for example due to a faulty measuring device or complex time-dependent nature. Spectral decomposition of time series is a traditionally useful tool for data variability analysis. However, existing methods for spectral estimation often assume a regularly-sampled time series, or require modifications to cope with irregular or ‘gappy’ data. Additionally, many techniques also assume that the time series are stationary, which in the majority of cases is demonstrably not appropriate. This article addresses the topic of spectral estimation of a non-stationary time series sampled with missing data. The time series is modelled as a locally stationary wavelet process in the sense introduced by Nason et al. (J. R. Stat. Soc. B 62(2):271–292, 2000) and its realization is assumed to feature missing observations. Our work proposes an estimator (the periodogram) for the process wavelet spectrum, which copes with the missing data whilst relaxing the strong assumption of stationarity. At the centre of our construction are second generation wavelets built by means of the lifting scheme (Sweldens, Wavelet Applications in Signal and Image Processing III, Proc. SPIE, vol. 2569, pp. 68–79, 1995), designed to cope with irregular data. We investigate the theoretical properties of our proposed periodogram, and show that it can be smoothed to produce a bias-corrected spectral estimate by adopting a penalized least squares criterion. We demonstrate our method with real data and simulated examples.     

Bayesian inference for pairwise interacting point processes

Pairwise interacting point processes are commonly used to model spatial point patterns. To perform inference, the established frequentist methods can produce good point estimates when the interaction in the data is moderate, but some methods may produce severely biased estimates when the interaction in strong. Furthermore, because the sampling distributions of the estimates are unclear, interval estimates are typically obtained by parametric bootstrap methods. In the current setting however, the behavior of such estimates is not well understood. In this article we propose Bayesian methods for obtaining inferences in pairwise interacting point processes. The requisite application of Markov chain Monte Carlo (MCMC) techniques is complicated by an intractable function of the parameters in the likelihood. The acceptance probability in a Metropolis-Hastings algorithm involves the ratio of two likelihoods evaluated at differing parameter values. The intractable functions do not cancel, and hence an intractable ratio r must be estimated within each iteration of a Metropolis-Hastings sampler. We propose the use of importance sampling techniques within MCMC to address this problem. While r may be estimated by other methods, these, in general, are not readily applied in a Bayesian setting. We demonstrate the validity of our importance sampling approach with a small simulation study. Finally, we analyze the Swedish pine sapling dataset (Strand 1972) and contrast the results with those in the literature.     

气候资本主义:低碳经济的政治学

我们很可能处在亲历气候资本主义出现的初级阶段。它是一种基于逐渐摆脱和远离化石燃料使用来持续经济发展的资本主义新形式,其中,脱碳被看作是一种缓和资本主义积累与抑制气候变化要求之间矛盾的机遇。但是,它依然是一种资本主义——通过市场、私有财产和工人阶级等要素组织起来,而且经济增长是其至高无上的律令。在未来几十年里,无论哪种具体版本的气候资本主义的出现,它实现脱碳的能力将取决于其导向低能源和低碳能源的投资的能力,同时还要处理好合法性的挑战,这些挑战不可避免地来自这种将全球金融作为管理碳排放手段的依赖。就全球气候变化管治的发展而言,无论是在《联合国气候变化框架公约》之下,还是最终超越了这一框架,成功的关键在于它是否能够创造一个有力的环境使全球经济转变为一种与强调气候变化相容的气候资本主义制度,或者,碳市场只是一种其取向与严肃应对气候危机的诸多努力不相容的经济制度中的孤立性存在。     

老龄工人在组织变革期间的管理:三种模型的应用

基于西方相关文献,本文讨论了组织性结构变革与老龄工人的健康之间的关系.通过对三个健康模型的分析—失去工作模型、付出-回报模型和工作控制模型,探讨了组织性结构变革是否会对老龄工人的健康造成影响,并进一步解释了在不同模型下组织性结构变革通过什么样的方式影响老龄工人的身心健康.文章还分析了三种模型之间的关系和应用,描述了政府和雇主在工作场所发生变化的时候,可以采取的减轻老龄员工健康风险的三个不同方法.最后针对中国现状,提出通过工作岗位支持来帮助老龄员工延缓与年龄相关的工作方面的及来自养老金和劳动力市场方面的压力的建议.     

Mad, Bad, or Reasonable? Newspaper Portrayals of the Battered Woman Who Kills

A heated debate about battered women who kill abusive male partners started in the 1970s. In this study, we tracked the public discourse on battered women who kill by coding 250 newspaper articles published between 1978 and 2002. Using four typifying models, we found that leading explanations for why battered women kill medicalized then criminalized their actions; they were mad then bad. We also found that reporters used quotes from claims makers supporting conventional or medical typifications of battered women to a much greater degree than statements from alternative, feminist sources. In conclusion, simplified, sensational and conventional understandings of crime causation drove the social construction of “the battered woman who kills”. She may be mad or bad, but rarely has she been portrayed as reasonable. Suggestions for promoting feminist narrative in the media are also provided.     

Is Valuing Equality Enough? Equality Values, Allophilia, and Social Policy Support for Multiracial Individuals

We conducted a field study to investigate positive intergroup attitudes (i.e., allophilia) and equality values as potential antecedents of social policy support for multiracial individuals. Participants (N = 97) reported their social policy support for multiracial individuals in two ways—support for the recognition of multiracial as a distinct racial category (recognition) and support for multiracial individuals' access to programs and policies (assistance). Results revealed that allophilia motivated those who held equality beliefs to support social policies for multiracial individuals. Implications of these findings for theories of positive intergroup relations, as well as the processes that may underlie progress for multiracial individuals, are discussed.