Negotiating Partnerships in Research on Poverty With Community-Based Agencies

This article argues that researchers must negotiate true partnerships with poor women, maintain mutual support systems, and build mutual respect, if they are to have success in challenging poverty through research. The experience of establishing partnerships with community-based agencies is discussed and the rationale for including poor women in partnerships is considered in terms of the benefits and the challenges for researchers. Among the critical issues are problems of difference, including ethnicity, social class, and gender and relationships between researchers and participants. These issues are highlighted using two examples. A negative outcome from noncollaboration is represented by the case of an after-school program in an inner-city school. The failure to establish collaboration leads to disappointing results. A successful case is described by the cooperative efforts between researchers and poor women in a program for substance abuse. Several strategies and recommendations are offered in conclusion.     

A practical approach to the assesment of manual handling equipment for cargo: multinational abrasives in Brazil

The Manual Handling of Loads is an activity present in virtually all production system, when developed without ergonomic principles of design and sizing can generate several instances of discomfort and musculoskeletal disorders. This work was carried out to measure, set limits and risks and guide specifications for the cargo handling carts in a specific company, tracing the anthropometric profile of the productive population and performing a quantitative evaluation of forces and design models of cars used in the company.     

An integer programming framework for critical elements detection in graphs

This study presents an integer programming framework for minimizing the connectivity and cohesiveness properties of a given graph by removing nodes and edges subject to a joint budgetary constraint. The connectivity and cohesiveness metrics are assumed to be general functions of sizes of the remaining connected components and node degrees, respectively. We demonstrate that our approach encompasses, as special cases (possibly, under some mild conditions), several other models existing in the literature, including minimization of the total number of connected node pairs, minimization of the largest connected component size, and maximization of the number of connected components. We discuss computational complexity issues, derive linear mixed integer programming (MIP) formulations, and describe additional modeling enhancements aimed at improving the performance of MIP solvers. We also conduct extensive computational experiments with real-life and randomly generated network instances under various settings that reveal interesting insights and demonstrate advantages and limitations of the proposed framework.     

The resilient frugivorous fauna of an urban forest fragment and its potential role in vegetation enrichment

Urban Ecosystems - Anthropocentric defaunation affects critical ecological processes, such as seed dispersal, putting ecosystems and biomes at risk, and leading to habitat impoverishment. Diverse...     

Histogram estimation of radon-nikodym derivatives for strong mixing data

Nonparametric inference for point processes is discussed by way of histograms, which provide a nice tool for the analysis of on-line data. The construction of histograms depends on a sequence of partitions, which we take tc be nonenibedded to allow partitions with sets of equal measure. This presents some theoretical problems, which are addressed with an assumption on the decomposition of second order moments. In another direction, we drop the usual independence assumption on the sample, replacing it by a strong mixing assumption. Under this setting, we study the convergence of the histogram in probability, which depends on approximation conditions between the distributions of random pairs and the product of their marginal distributions, and^almost completely, which is based on the decomposition of the second order moments. This last convergence is stated on two versions according to the assumption of Laplace transforms or the Cramer moment conditions. These are somewhat stronger, but enable us to recover the usual condition on the decrease rate of sets on each partition. In the final section we prove that the finite dimensional distributions converge in distribution to a Gaussian centered vector with a specified covariance.     

Differential evolutionary algorithm in the construction process of optimal experimental designs

This article shows how a differential evolution algorithm can be used to find statistical designs under several optimality criteria as A, D, and T. The general algorithm of differential evolution is described and then applied on constructing optimal designs for several well-known models and compare them with those already available by other algorithms, in terms of relative efficiency. Moreover, the great effect of population size in the running of this algorithm establishes a precedent in the use of differential evolution algorithms over genetic algorithms.     

The Features of Development in the Pacific Countries of the African,Caribbean and Pacific Group

In this article we present a new proposal for the measurement of development, applied to the Pacific Countries of the African, Caribbean and Pacific Group (ACP), conditional on their insularity, and with privileged relations with the European Union. Our index has been constructed attending to the criteria defined in the Goals of the Millennium Declaration. For this purpose we will use the P₂ distance method for the year 2007, the last year for which data are available. This index integrates socio-economic variables that permit a territorial ordering of the Pacific ACPs, in terms of those partial indicators.     

On risk-averse maximum weighted subgraph problems

In this work, we consider a class of risk-averse maximum weighted subgraph problems (R-MWSP). Namely, assuming that each vertex of the graph is associated with a stochastic weight, such that the joint distribution is known, the goal is to obtain a subgraph of minimum risk satisfying a given hereditary property. We employ a stochastic programming framework that is based on the formalism of modern theory of risk measures in order to find minimum-risk hereditary structures in graphs with stochastic vertex weights. The introduced form of risk function for measuring the risk of subgraphs ensures that optimal solutions of R-MWS problems represent maximal subgraphs. A graph-based branch-and-bound (BnB) algorithm for solving the proposed problems is developed and illustrated on a special case of risk-averse maximum weighted clique problem. Numerical experiments on randomly generated Erdös-Rényi graphs demonstrate the computational performance of the developed BnB.