Living in the shadow of occupation: Life satisfaction and positive emotion as protective factors in a group of Palestinian school children

Starting from perspectives on functioning and wellbeing, the aim of the present study was to investigate positive and negative affect, self-perceived life satisfaction and happiness in a group of 74 Palestinian children exposed to political violence. We hypothesized that the children in our sample would display generally satisfactory wellbeing and good functioning in terms of positive affect, life satisfaction and happiness, despite their negative life conditions. Both quantitative and qualitative analysis of the dimensions of participants' wellbeing was carried out. In particular, we explored the ecological dimensions affecting the wellbeing of Palestinian children through content analysis of narratives produced by the group of participants. Positive emotions were found to contribute to wellbeing and life satisfaction in children, acting as protective factors in dealing with daily violence. The children displayed a range of personal resources facilitated above all by the functioning community structure supporting them. Implications for clinical intervention are discussed.     

Cognitively automated assembly processes: a simulation based evaluation of performance

The numerical control of an experimental assembly cell with two robots--termed a cognitive control unit (CCU)--is able to simulate human information processing at a rule-based level of cognitive control. To enable the CCU to work on a large range of assembly tasks expected of a human operator, the cognitive architecture SOAR is used. The CCU can plan assembly processes autonomously and react to ad-hoc changes in assembly sequences effectively. Extensive simulation studies have shown that cognitive automation based on SOAR is especially suitable for random parts supply, which reduces planning effort in logistics. Conversely, a disproportional increase in processing time was observed for deterministic parts supply, especially for assemblies containing large numbers of identical parts. In this contribution, the effect of phase-shifts in deterministic part supply is investigated for assemblies containing maximal different parts. It can be shown that the concept of cognitive automation is as well suitable for these planning problems.     

Climate change and population migration in Brazil’s Northeast: scenarios for 2025–2050

This research contributes to an understanding of the relationship between climate change, economic impacts and migration. We model the long-term relationship (up to 45 years of projection) between demographic dynamics—particularly migration—driven by changes in the performance of the economy due to climate changes in the Northeast region of Brazil. The region is of particular relevance to the study of climate change impacts given its large human population (28% of Brazil’s population) and high levels of impoverishment, having an extensive semi-dry area which will be severely impacted by growing temperatures. Ultimately, the integrated model generates state- and municipal-level migration scenarios based on climate change impacts on the primary economic sectors and their articulations with other sectors. Results suggest that the predicted climate changes will impact severely the agriculture sector in the region, acting as a potential migration push factor to other regions in the country. Finally, we discuss how the increased vulnerability of some groups, particularly migrants, can be factored into Brazilian public policy and planning.     

Model-based clustering with determinant-and-shape constraint

Statistics and Computing - Model-based approaches to cluster analysis and mixture modeling often involve maximizing classification and mixture likelihoods. Without appropriate constrains on the...     

Measuring Multidimensional Poverty According to National Definitions: Operationalising Target 1.2 of the Sustainable Development Goals

Social Indicators Research - The first Sustainable Development Goal (SDG) tasks countries with eradicating poverty in all its dimensions according to national definitions. This presents...     

Frugivory and habitat use by thrushes (Turdus spp.) in a suburban area in south Brazil

Thrushes (Turdus spp., Turdidae) are among the most common frugivorous birds in urban areas around the world, where they disperse the seeds of a variety of plant species. We studied the abundance, habitat use, foraging behavior and diet of four thrush species (Turdus rufiventris, T. amaurochalinus, T. leucomelas, and T. albicollis) in a suburban area in south Brazil. Abundance, habitat use and foraging behavior were based on birds surveyed along a 3,240 m transect crossing open (formed by lawns, streets, and buildings) and forested areas. Diet was based on fecal samples collected from mist-netted birds. Turdus rufiventris was the most abundant species, followed by T. amaurochalinus, T. leucomelas, and T. albicollis. All species used forest fragments more frequently than expected by chance. A total of 91.8% (n = 147) of the fecal samples contained fruit remains, while 42.2% contained only animal matter. Most of the foraging records were on the ground, where birds got mainly invertebrates. Fruits and invertebrates were eaten more frequently in open than in forested areas. A total of 25 seed morfospecies were found in the droppings, including five exotic plant species. Thrushes overlapped widely in the fruit composition of their diets. The high abundance and degree of frugivory, coupled with the frequent use of forest patches, indicate that thrushes are among the great bird contributors to the seed dispersal occurring in urban forest patches, potentially influencing the vegetation dynamics of such habitats so important for the maintenance of the biodiversity in urban areas.     

Choosing monetary sequences: theory and experimental evidence

We formulate and investigate experimentally a model of how individuals choose between time sequences of monetary outcomes. The model assumes that a decision maker uses, sequentially, two criteria to screen options. Each criterion only permits a decision between some pairs of options, while the other options are incomparable according to that criterion. When the first criterion is not decisive, the decision maker resorts to the second criterion to select an alternative. We find that: (1) traditional economic models based on discounting alone cannot explain a significant (almost 30%) proportion of the data no matter how much variability in the discount functions is allowed; (2) our model, despite considering only a specific (exponential) form of discounting, can explain the data much better solely thanks to the use of the secondary criterion; (3) our model explains certain specific patterns in the choices of the “irrational” people. We reject the hypothesis that anomalous behavior is due simply to random “mistakes” around the basic predictions of discounting theories: deviations are not random and there are clear systematic patterns of association between “irrational” choices.     

A weighted polynomial regression method for local fitting of spatial data

Typically, parametric approaches to spatial problems require restrictive assumptions. On the other hand, in a wide variety of practical situations nonparametric bivariate smoothing techniques has been shown to be successfully employable for estimating small or large scale regularity factors, or even the signal content of spatial data taken as a whole.We propose a weighted local polynomial regression smoother suitable for fitting of spatial data. To account for spatial variability, we both insert a spatial contiguity index in the standard formulation, and construct a spatial-adaptive bandwidth selection rule. Our bandwidth selector depends on the Gearys local indicator of spatial association. As illustrative example, we provide a brief Monte Carlo study case on equally spaced data, the performances of our smoother and the standard polynomial regression procedure are compared.This note, though it is the result of a close collaboration, was specifically elaborated as follows: paragraphs 1 and 2 by T. Sclocco and the remainder by M. Di Marzio. The authors are grateful to the referees for constructive comments and suggestions.     

Bayesian networks for imputation

Summary.  Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables ( statistical consistency ) and preservation of logical constraints in data ( logical consistency ). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.