Barrios and burbs: residential context and health-risk behaviors among Angeleno adolescents

The increasing size of the Latino immigrant population in the United States underscores the need for a more complete understanding of the role that social context plays in influencing the health of immigrants and their children. This analysis explores the possibility that residential location influences the health-risk behaviors of Latino youth in Los Angeles County, California. The data come from the Los Angeles Family and Neighborhood Survey. We apply multivariate, multilevel Rasch models to two scales of adolescent health-risk behaviors (substance use and delinquency). The findings suggest that residence in Census tracts characterized by above-county-average levels of Latinos and above-county-average levels of poverty is associated with increased odds of health-risk behaviors for Latino adolescents, particularly for those born in the United States. The findings lend support to the contention, put forth in the segmented assimilation literature, that disadvantaged urban contexts increase the risk that U.S.-born children of immigrants will experience downward assimilation.     

Effects of variations in water quantity and quality in the structure and functions of invertebrates’ community of a Mediterranean urban stream

Urban Ecosystems - Urban streams provide important ecosystem services to cities’ population, from the maintenance of urban biodiversity, temperature, humidity and air quality to improving...     

The Changing Practices and Meanings of Grandparenthood. Reflections on the Demographical Trends and Changing Representations of Ageing

Deep demographical shifts in western societies are significantly influencing the patterns of family life including the various forms of solidarity between generations. At the same time, we are facing significant changes in the social representation of ageing, which also influence the ways in which the roles of seniors in society are perceived and how the meaning of old age is constructed. The paper highlights the significance of these changes for the possible (re)definition of grandparenthood. It discusses the ways in which these demographical changes, the current heterogeneity of family life, and changing representations of ageing can affect the practices and meanings of grandparenthood. Simultaneously, it points out the need to analyse grandparenthood as a role which is strongly gendered.     

Semiparametric ARCH Models

This article introduces a semiparametric autoregressive conditional heteroscedasticity (ARCH) model that has conditional first and second moments given by autoregressive moving average and ARCH parametric formulations but a conditional density that is assumed only to be sufficiently smooth to be approximated by a nonparametric density estimator. For several particular conditional densities, the relative efficiency of the quasi-maximum likelihood estimator is compared with maximum likelihood under correct specification. These potential efficiency gains for a fully adaptive procedure are compared in a Monte Carlo experiment with the observed gains from using the proposed semiparametric procedure, and it is found that the estimator captures a substantial proportion of the potential. The estimator is applied to daily stock returns from small firms that are found to exhibit conditional skewness and kurtosis and to the British pound to dollar exchange rate.     

Socioeconomic Risk in Development of Energy Systems

A soft mathematical model, taking stock of the stochastic and cooperative features of the economy of thinking in the decision making, is aggregated to unveil hidden connections between energy policy and the energy technology choices of an establishment. Starting from several assumptions on the nature of the collective decisional behavior, one obtains a probabilistic interpretation of the mechanism of penetration of energy technologies. The probability that the establishment bets on a certain technological profile is given as a solution to a Fokker-Planck equation accounting for the decision game. It provides a topological variety that accomodates possible states of the system and their trajectories, and indicates ways in which different attractors drive the technology choice in the space of the energy policy.
In this framework, a series of concepts (i.e., logistic evolution, resilience, stability, risks of disruption or crisis, energy security) can find in a natural way strikingly intuitive interpretations. Strategic games are possible on this ground, confirming facts of life and also showing predictive power. The complex and difficult to manage interdependence between energy policy and technology appears as a challenge to the long-term planning of alternative energy systems. To meet the challenge, preparedness for changes through a large freedom of choice on the technological options appears as a necessary complement to the faithful observance of the market drives, which stresses the importance of having available perceptive, coherent, reliable, and responsible mechanisms of decision making.     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.     

Modeling Risk‐Related Knowledge in Tunneling Projects

Knowledge on failure events and their associated factors, gained from past construction projects, is regarded as potentially extremely useful in risk management. However, a number of circumstances are constraining its wider use. Such knowledge is usually scarce, seldom documented, and even unavailable when it is required. Further, there exists a lack of proven methods to integrate and analyze it in a cost‐effective way. This article addresses possible options to overcome these difficulties. Focusing on limited but critical potential failure events, the article demonstrates how knowledge on a number of important potential failure events in tunnel works can be integrated. The problem of unavailable or incomplete information was addressed by gathering judgments from a group of experts. The elicited expert knowledge consisted of failure scenarios and associated probabilistic information. This information was integrated using Bayesian belief‐networks‐based models that were first customized in order to deal with the expected divergence in judgments caused by epistemic uncertainty of risks. The work described in the article shows that the developed models that integrate risk‐related knowledge provide guidance as to the use of specific remedial measures.     

Estimation of the Intensity Parameter of the Germ‐Grain Quermass‐Interaction Model when the Number of Germs is not Observed

The Quermass‐interaction model allows to generalize the classical germ‐grain Boolean model in adding a morphological interaction between the grains. It enables to model random structures with specific morphologies, which are unlikely to be generated from a Boolean model. The Quermass‐interaction model depends in particular on an intensity parameter, which is impossible to estimate from classical likelihood or pseudo‐likelihood approaches because the number of points is not observable from a germ‐grain set. In this paper, we present a procedure based on the Takacs–Fiksel method, which is able to estimate all parameters of the Quermass‐interaction model, including the intensity. An intensive simulation study is conducted to assess the efficiency of the procedure and to provide practical recommendations. It also illustrates that the estimation of the intensity parameter is crucial in order to identify the model. The Quermass‐interaction model is finally fitted by our method to P. Diggle's heather data set.