妇产科教学如何应对新的医疗环境

近年来，患者维权意识增强，医患关系紧张，妇产科教学面临着缺乏实践教学对象，教学任务不能落实的尴尬局面。只有在教学中渗透医德教育，提高医疗风险意识，加强教师素质培训，尽力协调好医患关系，应用先进科学的教学方法，才能保证教学质量，从而使妇产科教学落到实处。     

A Repetitive Branch-and-Bound Procedure for Minimum Within-Cluster Sums of Squares Partitioning

Minimization of the within-cluster sums of squares (WCSS) is one of the most important optimization criteria in cluster analysis. Although cluster analysis modules in commercial software packages typically use heuristic methods for this criterion, optimal approaches can be computationally feasible for problems of modest size. This paper presents a new branch-and-bound algorithm for minimizing WCSS. Algorithmic enhancements include an effective reordering of objects and a repetitive solution approach that precludes the need for splitting the data set, while maintaining strong bounds throughout the solution process. The new algorithm provided optimal solutions for problems with up to 240 objects and eight well-separated clusters. Poorly separated problems with no inherent cluster structure were optimally solved for up to 60 objects and six clusters. The repetitive branch-and-bound algorithm was also successfully applied to three empirical data sets from the classification literature.     

A branch-and-bound algorithm for fitting anti-robinson structures to symmetric dissimilarity matrices

The seriation of proximity matrices is an important problem in combinatorial data analysis and can be conducted using a variety of objective criteria. Some of the most popular criteria for evaluating an ordering of objects are based on (anti-) Robinson forms, which reflect the pattern of elements within each row and/or column of the reordered matrix when moving away from the main diagonal. This paper presents a branch-and-bound algorithm that can be used to seriate a symmetric dissimilarity matrix by identifying a reordering of rows and columns of the matrix optimizing an anti-Robinson criterion. Computational results are provided for several proximity matrices from the literature using four different anti-Robinson criteria. The results suggest that with respect to computational efficiency, the branch-and-bound algorithm is generally competitive with dynamic programming. Further, because it requires much less storage than dynamic programming, the branch-and-bound algorithm can provide guaranteed optimal solutions for matrices that are too large for dynamic programming implementations.     

中国经济伦理学研究中的若干问题——兼论经济伦理学的学科性质

中国经济伦理学的研究虽然成果显著,但还存在着需要进一步澄清的三个相互关联的重要问题,即“理论性”与“应用性”问题、“伦理学”与“经济学”问题、以及“理想性”与“现实性”问题。这三个问题的正确理解有助于关于经济伦理学学科性质的合理界定,也有助于中国经济伦理学研究的继续深入。     

An evaluation of exact methods for the multiple subset maximum cardinality selection problem

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch‐and‐bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch‐and‐bound approach.     

'Little Hans': From his phobic episode to becoming an opera director

‘Little Hans’ is one of the most highly commented cases in the psychoanalytic literature. His work as an opera director from 1925 in Europe and then in the United States of America is much less well known. This may seem especially surprising given that Freud very soon detects Hans’s emerging interest in this subject. Yet Freud does not mention it either in 1909 when he reports the case, or when Hans visits him in 1922, even though Hans had already decided to become an opera director at this point. The author of this article endeavours to show how this artistic choice could be understood as a way of accommodating, in a double transference relationship with Freud and with his father, the unanalysed residue of the ‘Krawall’ (a term invented by Hans) and ‘the black thing’, both of which appeared during the phobic period.     

Payback: Effects of relationship and cultural norms on reciprocity

This experimental investigation explores differences in reciprocal norms between friends and strangers and the effects of culture on reciprocity. Based on altruistic and strong reciprocity theories, a hybrid trust‐dictator game tested the influence of relationship (i.e. friends vs. strangers), treatment (i.e. positive vs. negative) and culture (i.e. collectivistic vs. individualistic) on reciprocation. The results show that participants reciprocated more positively when treated positively in general. However, the results demonstrate intercultural differences in reciprocal norms, specifically in the negative treatment condition. Participants from the individualistic culture provided stronger punishment to the norm violator, compared to participants from the collectivistic culture. We discuss implications of the impact of relationship and culture on reciprocation with respect to the olive branch response.     

依恋视角下自传体记忆提取的差异

自传体记忆是关于个人生活的记忆, 依恋理论为自传体记忆的理解提供重要的理论框架。本文以个体依恋风格的差异为视角, 根据Conway和Pleydell-Pearce’s的自我记忆系统(Self-Memory System)总结出在自传体记忆提取的过程中, 依恋系统具有目标导向、防御排斥和情绪调节的作用。安全型相较于非安全型依恋的个体在自传体记忆提取的数量、具体性、准确性、通达性和情绪强度、情绪唤醒度上具有优势。未来研究可以从依恋系统的激活, 焦虑型、回避型依恋在记忆提取中具体成分的差异, 依恋与不随意自传体记忆提取的关系上来探讨。     

Optimal Partitioning of a Data Set Based on the p-Median Model

Although the K-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The p-median model is an especially well-studied clustering problem that requires the selection of p objects to serve as cluster centers. The objective is to choose the cluster centers such that the sum of the Euclidean distances (or some other dissimilarity measure) of objects assigned to each center is minimized. Using 12 data sets from the literature, we demonstrate that a three-stage procedure consisting of a greedy heuristic, Lagrangian relaxation, and a branch-and-bound algorithm can produce globally optimal solutions for p-median problems of nontrivial size (several hundred objects, five or more variables, and up to 10 clusters). We also report the results of an application of the p-median model to an empirical data set from the telecommunications industry.     

浅谈科主任非权力性影响力的培养

科主任的非权力性影响力是由本人的素质和行为造成的,也可以说是个人的影响、威望和威信,这主要靠领导者具有良好的品格、素质和能力。本文就科主任应具备的良好的职业道德和人格魅力、丰富的才能和渊博的知识、良好的自制能力和人际关系、严谨的工作作风和高超的专业技术等这些非权力影响力进行了分析,并对如何提高这些素质进行了阐述。