Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil

Motivated by a proposal of the local authority for improving the existing healthcare system in the Parana State in Brazil, this article presents an optimization-based model for developing a better system for patients by aggregating various health services offered in the municipalities of Parana into some microregions. The problem is formulated as a multi-objective partitioning of the nodes of an undirected graph (or network) with the municipalities as the nodes and the roads connecting them as the edges of the graph. Maximizing the population homogeneity in the microregions, maximizing the variety of medical procedures offered in the microregions, and minimizing the inter-microregion distances to be traveled by patients are considered as three objective functions of the problem. An integer-coded multi-objective genetic algorithm is adopted as the optimization tool, which yields a significant improvement to the existing healthcare system map of the Parana State. The results obtained may have a strong impact on the healthcare system management in Parana. The model proposed here could be a useful tool to aid the decision-making in health management, as well as for better organization of any healthcare system, including those of other Brazilian States.     

资源分割与产业组织的演化

资源分割理论是组织生态学的重要理论,它从资源分化的角度解释了产业组织之间的竞争共存现象。资源分割理论的核心包括三个部分,一是资源分布的形状,二是通用型组织的行为和演化,三是在既定的资源分布和通用型组织的行为方式下,专业化组织的行为和演化。研究表明,组织生态位宽度的差异和组织生态位的分离是组织之间竞争共存的基础,而组织之间存在的边缘效应、聚群行为和扩散行为等行为机制,能抑制组织种群之间与种群内组织之间的竞争排斥行为。资源分割会通过影响组织设立、组织成长和组织死亡而制约产业组织的演化。     

Univaiuate anu multivariate categorical data analysis for block designs

Analysis for univariate and multivariate categorical data in block designs is given and illustrated through examples. The univariate analysis compares the treatments on the basis of their pooled frequency distributions (pooled over blocks). The test statistic used is called Q after Cochran (1950). The large sample null distribution of Q is a chi-square. Analysis of p-variate categorical data (kth variable having ck classes, K=1,...,p) can be done by treating it as a univariate categorical problem with [d] classes. Very often [d] is large in relation to the size of the experiment. This makes the expected frequencies for some of the cells very small, making the univariate method inapplicable. In these circumstances it may be reasonable to compare the treatments on the basis of marginal distributions up to the mth dimension, 1[d] , which is given in this paper. This method is also illustrated for missing observations     

企业生态位理论研究现状与趋势

企业生态位理论作为组织生态学中的重要组成部分之一仍处于发展之中.目前研究主要集中在企业生态位宽度、重叠密度等角度研究生态位对企业生存与发展的影响,因此有许多尚待扩展的领域.本文主要对企业生态位理论研究主线、属性、主要流派等方面对企业生态位理论进行梳理与综合评述,并指出研究趋势.     

Clustering of Microarray data via Clique Partitioning

Microarrays are repositories of gene expression data that hold tremendous potential for new understanding, leading to advances in functional genomics and molecular biology. Cluster analysis (CA) is an early step in the exploration of such data that is useful for purposes of data reduction, exposing hidden patterns, and the generation of hypotheses regarding the relationship between genes and phenotypes. In this paper we present a new model for the clique partitioning problem and illustrate how it can be used to perform cluster analysis in this setting.     

Gaining insight with recursive partitioning of generalized linear models

Recursive partitioning algorithms separate a feature space into a set of disjoint rectangles. Then, usually, a constant in every partition is fitted. While this is a simple and intuitive approach, it may still lack interpretability as to how a specific relationship between dependent and independent variables may look. Or it may be that a certain model is assumed or of interest and there is a number of candidate variables that may non-linearly give rise to different model parameter values. We present an approach that combines generalized linear models (GLM) with recursive partitioning that offers enhanced interpretability of classical trees as well as providing an explorative way to assess a candidate variable's influence on a parametric model. This method conducts recursive partitioning of a GLM by (1) fitting the model to the data set, (2) testing for parameter instability over a set of partitioning variables, (3) splitting the data set with respect to the variable associated with the highest instability. The outcome is a tree where each terminal node is associated with a GLM. We will show the method's versatility and suitability to gain additional insight into the relationship of dependent and independent variables by two examples, modelling voting behaviour and a failure model for debt amortization, and compare it to alternative approaches.     

Multiple partitioning of multiplex signed networks: Application to European parliament votes

For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset.     

Testing for order among K populations: theory and examples

Testing for stochastic order among K populations is a common and important problem in statistical practice. It arises in the analysis of both planned experiments and observational studies. The authors develop a new nonparametric test for order among K populations that can accommodate any stochastic ordering. The test is based on a maximally selected chi‐bar‐square statistic. The authors find its limiting distribution and use simulations to derive critical values. Three important examples are used to illustrate the applicability of the general method. The authors find that the new tests outperform the existing methods in many practical cases. The Canadian Journal of Statistics 38: 97–115; 2010 © 2009 Statistical Society of Canada     

Partitioning large signed two-mode networks: Problems and prospects

While a substantial amount of attention within social network analysis (SNA) has been given to the study of one-mode networks, there is an increasing consideration of two-mode networks. Recent research on signed networks resulted in the relaxed structural balance (RSB) approach and its subsequent extension to signed two-mode networks involving social actors and social objects. We extend this approach to large signed two-mode networks, and address the methodological issues that arise. We develop tools to partition these types of networks and compare them with other approaches using a recently collected dataset of United Nations General Assembly roll call votes. Although our primary purpose is methodological, we take the first step towards bridging Heider's structural balance theory with recent theorizing in international relations on soft balancing of power processes.     

Preference in flower visits and partitioning in pollen diets of stingless bees in an Asian tropical rain forest

Floral resource partitioning among stingless bees (Trigona, Meliponini, Apidae) in a lowland rain forest in Sarawak, Malaysia, was investigated using tree towers and walkways in a 4-year study that included a general flowering period. We obtained 100 collections of insect visitors to flowers of varying floral location and shape representing 81 plant species. The tendency of 11 species of stingless bees to visit specific flowers with a particular floral location and shape was analyzed by logistic regression analysis. This analysis showed that the proportion of flower visitor collections containing Trigona fuscobalteata and T. melanocephala differed according to floral location. The former was frequently collected at canopy and gap flowers, whereas the latter was most often collected at understory flowers. The analysis also suggested that T. erythrogastra was more rarely collected at shallow flowers than at deep flowers. Analysis of the pollen diets of T. collina, T. fuscobalteata, T. melanocephala, and T. melina revealed that similarity of pollen sources differed among the six permutated pairs of the four species. The lowest mean rank of similarity found was between T. fuscobalteata and T. melanocephala. This result supports the hypothesis that preference in visiting flowers in different locations leads to pollen resource partitioning. Received: May 14, 1997 / Accepted: April 23, 1999