Model-based clustering of multivariate skew data with circular components and missing values

Motivated by classification issues that arise in marine studies, we propose a latent-class mixture model for the unsupervised classification of incomplete quadrivariate data with two linear and two circular components. The model integrates bivariate circular densities and bivariate skew normal densities to capture the association between toroidal clusters of bivariate circular observations and planar clusters of bivariate linear observations. Maximum-likelihood estimation of the model is facilitated by an expectation maximization (EM) algorithm that treats unknown class membership and missing values as different sources of incomplete information. The model is exploited on hourly observations of wind speed and direction and wave height and direction to identify a number of sea regimes, which represent specific distributional shapes that the data take under environmental latent conditions.     

Otherness and empathy—implications of Lévinas ethics for social work education

This paper examines the concept of otherness in Lévinas’ ethics and raises related issues regarding the limits of empathy on philosophical grounds. This study also identifies and discusses a number of linguistic, social and cultural barriers that can establish such limits on practical grounds. This paper raises various considerations for social work educators teaching empathy. Such considerations suggest that social work educators could adopt a stance of ‘understanding and not understanding’ and a dialectical approach in using knowledge and skills. Furthermore, this study challenges the common assumption that empathy leads to compassionate action. Apart from empathy, moral commitment is necessary to engage in such action. This article discusses such moral commitment in relation to Lévinas’ notions of face and responsibility that place ethics before knowledge. Hence, the present study brings the attention of social work educators back to the importance of nourishing virtue and the moral character of students.     

Tracing the roots of mindfulness: Transcendence in Buddhism and Christianity

The field of social work has embraced mindfulness skills in the training of social workers and as an intervention used in practice. However, there is little discussion regarding the theoretical or religious roots of mindfulness. In addition, social workers often employ mindfulness by practicing within a behaviorist frame. Missing from this behaviorist frame are the historic religious definitions, aims, and values of mindfulness, which lead to a fuller understanding of mindfulness in social work practice. This article traces the religious roots of mindfulness to clarify its goals in both Buddhism and Christianity, with an emphasis on the Mahayana branch of Buddhism and the contemplative and mystical traditions within Christianity. In both religions, mindfulness leads one to an experience of transcendence, assisting the follower to realize that the self belongs in relationship with a divine oneness in Buddha nature, that is believed to be intrinsic to all sentient beings (in Buddhism), or a divine God through the person of Jesus Christ (in Christianity) where the self is retained. Social workers uphold the values of mindfulness and the profession itself when being sensitive to root mindfulness practice within its historical and religious contexts, particularly within Buddhist and Christian faith systems.     

General results for the Kumaraswamy-G distribution

For any continuous baseline G distribution [G.M. Cordeiro and M. de Castro, A new family of generalized distributions, J. Statist. Comput. Simul. 81 (2011), pp. 883–898], proposed a new generalized distribution (denoted here with the prefix ‘Kw-G’ (Kumaraswamy-G)) with two extra positive parameters. They studied some of its mathematical properties and presented special sub-models. We derive a simple representation for the Kw-G density function as a linear combination of exponentiated-G distributions. Some new distributions are proposed as sub-models of this family, for example, the Kw-Chen [Z.A. Chen, A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function, Statist. Probab. Lett. 49 (2000), pp. 155–161], Kw-XTG [M. Xie, Y. Tang, and T.N. Goh, A modified Weibull extension with bathtub failure rate function, Reliab. Eng. System Safety 76 (2002), pp. 279–285] and Kw-Flexible Weibull [M. Bebbington, C.D. Lai, and R. Zitikis, A flexible Weibull extension, Reliab. Eng. System Safety 92 (2007), pp. 719–726]. New properties of the Kw-G distribution are derived which include asymptotes, shapes, moments, moment generating function, mean deviations, Bonferroni and Lorenz curves, reliability, Rényi entropy and Shannon entropy. New properties of the order statistics are investigated. We discuss the estimation of the parameters by maximum likelihood. We provide two applications to real data sets and discuss a bivariate extension of the Kw-G distribution.     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

Introduction to the Special Section Value Development from Middle Childhood to Early Adulthood—New Insights from Longitudinal and Genetically Informed Research

Research into values at an early age has only started recently, although it has expanded quickly and dynamically in the past years. The purpose of this article is twofold: First, it provides an introduction to a special section that aims to help fill the gap in value development research. The special section brings together four new longitudinal and genetically informed studies of value development from the beginning of middle childhood through early adulthood. Second, this article reviews recent research from this special section and beyond, aiming to provide new directions to the field. With new methods for assessing children's values and an increased awareness of the role of values in children's and adolescents' development, the field now seems ripe for an in‐depth investigation. Our review of empirical evidence shows that, as is the case with adults, children's values are organized based on compatibilities and conflicts in their underlying motivations. Values show some consistency across situations, as well as stability across time. This longitudinal stability tends to increase with age, although mean changes are also observed. These patterns of change seem to be compatible with Schwartz's (1992) theory of values (e.g., if the importance of openness to change values increases, the importance of conservation values decreases). The contributions of culture, family, peers, significant life events, and individual characteristics to values are discussed, as well as the development of values as guides for behavior.     

黄国雄教授商品流通理论研究回顾

黄国雄教授是中国现代商品流通理论的奠基人.文章通过文献梳理和实际观察,对黄国雄教授的商品流通理论研究进行了回顾.黄国雄教授的商品流通理论主要包括三个方面:在内容方面,聚焦于商品流通理论的研究;在方法方面,多使用经验主义范式;在主要贡献方面,奠定了现代商学理论的根基,具体包括提出了“商业创造价值论”“流通产业基础论”和“商道即人道论”等三大理论,以及建立了现代商学的学科体系.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.     

试论社会主义核心价值构建的人性维度

市场经济负面影响的日益显现及西方所谓普世价值的冲击，使不少国人陷入价值错乱、虚无的状态，亟待对其加强价值引导。社会主义核心价值的提出及构建可谓正当其时。价值与核心价值的构建，必须遵循人性维度。运用理性思维去把握人性，形成科学的人性理论，并将其付之于现实生活予以检验，把握人性的真实，才能为核心价值构建奠定人性的坚实基石。     

Missing value imputation method for disaster decision-making using K nearest neighbor

Due to destructiveness of natural disasters, restriction of disaster scenarios and some human causes, missing data usually occur in disaster decision-making problems. In order to estimate missing values of alternatives, this paper focuses on imputing heterogeneous attribute values of disaster based on an improved K nearest neighbor imputation (KNNI) method. Firstly, some definitions of trapezoidal fuzzy numbers (TFNs) are introduced and three types of attributes (i.e. linguistic term sets, intervals and real numbers) are converted to TFNs. Then the correlated degree model is utilized to extract related attributes to form instances that will be used in K nearest neighbor algorithm, and a novel KNNI method merging with correlated degree model is presented. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and effectiveness.