THE ASSOCIATION OF POVERTY WITH CHILD WELFARE SERVICE AND CHILD AND FAMILY CLINICAL OUTCOMES

The influences of poverty, family status and violence in the lives of children and families seen at a large urban children's aid society are investigated. Rates of social assistance dependency of child welfare families in 1995 and 2001 are compared. While the rate of poverty of child welfare families is stable across the two time periods, the ability of families to cope with poverty has dramatically changed, with the percentage of children from impoverished families being admitted into care nearly doubling across the six-year period. Violence as a mitigating factor in the relationship between poverty, the extent of child welfare service and child and family outcomes is also reported. Suggestions for enabling women who are caught in the complexity of choosing between physical safety and poverty are reported.

Les influences de la pauvreté, de la position sociale de la famille et de la violence sur la vie des enfants et des familles vues à une grande société urbaine d'aide à l'enfance sont examinées. Les taux de dépendance des familles vis-à-vis des programmes de protection de l'enfance en 1995 et 2001 sont comparés. Tandis que le taux de pauvreté des familles qui se servent du système de protection de l'enfance est stable à travers les deux périodes de temps, la capacité des familles de faire face à la pauvreté a radicalement changé, le pourcentage d'enfants de familles appauvries qui sont placés dans des familles d'accueil ayant presque doublé à travers la période de six ans. On fait aussi compte-rendu de la violence comme facteur atténuant dans le rapport entre la pauvreté, l’étendue du service de protection des enfants et les résultats pour les enfants et les familles. On résume aussi des suggestions favorisant l'habilitation des femmes qui se trouvent coincées dans la complexité de choisir entre la sûreté physique et la pauvreté.     

Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods

Summary. A Bayesian method for segmenting weed and crop textures is described and implemented. The work forms part of a project to identify weeds and crops in images so that selective crop spraying can be carried out. An image is subdivided into blocks and each block is modelled as a single texture. The number of different textures in the image is assumed unknown. A hierarchical Bayesian procedure is used where the texture labels have a Potts model (colour Ising Markov random field) prior and the pixels within a block are distributed according to a Gaussian Markov random field, with the parameters dependent on the type of texture. We simulate from the posterior distribution by using a reversible jump Metropolis–Hastings algorithm, where the number of different texture components is allowed to vary. The methodology is applied to a simulated image and then we carry out texture segmentation on the weed and crop images that motivated the work.     

How Does Evidence Matter? Understanding ‘What Works’ for Wellbeing

Social Indicators Research - In 2015 the UK launched an independent What Works Centre for Wellbeing, co-funded by government departments and various agencies, which aims to develop a ‘strong...     

Modeling the Airborne Infection Risk of Tuberculosis for a Research Facility in eMalahleni,South Africa

A detailed mathematical modeling framework for the risk of airborne infectious disease transmission in indoor spaces was developed to enable mathematical analysis of experiments conducted at the Airborne Infections Research (AIR) facility, eMalahleni, South Africa. A model was built using this framework to explore possible causes of why an experiment at the AIR facility did not produce expected results. The experiment was conducted at the AIR facility from August 31, 2015 to December 4, 2015, in which the efficacy of upper room germicidal ultraviolet (GUV) irradiation as an environmental control was tested. However, the experiment did not produce the expected outcome of having fewer infections in the test animal room than the control room. The simulation results indicate that dynamic effects, caused by switching the GUV lights, power outages, or introduction of new patients, did not result in the unexpected outcomes. However, a sensitivity analysis highlights that significant uncertainty exists with risk of transmission predictions based on current measurement practices, due to the reliance on large viable literature ranges for parameters.     

Does Size Matter? A Study of Risk Perceptions of Global Population Growth

The global human population now exceeds 7 billion and is projected to reach 10 billion around 2060. While population growth has been associated with certain benefits (e.g., economies of scale, technological advancements), theoretical models, probabilistic projections, and empirical evidence also indicate that this growth could increase the likelihood of many adverse events (e.g., climate change, resource shortages) and the impact of these events, as more people are exposed to the outcomes. While concerns about these issues are well‐documented in the academic literature, there is little evidence concerning the public's perceptions of the risks associated with global population growth (GPG) and how these perceptions are likely to influence related decisions. To address these issues, we conducted a U.K.‐based study that examined respondents’ risk perceptions of GPG, their willingness to embrace mitigation/precautionary behaviors, and reasons for variations in these two factors. We found that GPG is perceived as a moderate‐to‐high risk, with concerns about the increased likelihood of resource shortages, ecological damage, and violent conflict being foremost. Respondents believed that the worst effects of GPG would arrive around 2050 and would be experienced by the world's poorest people. Respondents who perceived greater levels of risk from GPG were generally those who indicated a greater willingness to embrace mitigation behaviors (e.g., reduce resource consumption) and preventative actions (e.g., support political action to limit growth). We discuss how our findings might be utilized to better manage the potential challenges associated with GPG and we suggest several directions for further research.     

Coal demand,market forces,and U.S. coal mine closures

Economic transitions have the potential to displace workers and cause social unrest. Coal mine closures and the resulting employment losses in rural areas have become salient issues. Using data on coal mine and power plant operation, we model closure as a function of expected profits, which allows us to compare the effects on mine closure of specific demand and supply shocks to expected mine profits. Increasing costs of production have had a large impact on closures, but lower natural gas prices and lower electricity demand have played more recently important roles.     

State estimation for nonlinear state-space transmission models of tuberculosis

Given the high prevalence of tuberculosis (TB) and the mortality rate associated with the disease, numerous models, such as the Gammaitoni and Nucci (GN) model, were developed to model the risk of transmission. These models typically rely on a quanta generation rate as a measurement of infectivity. Since the quanta generation rate cannot be measured directly, the unique contribution of this work is to develop state estimators to estimate the quanta generation rate from available measurements. To estimate the quanta generation rate, the GN model is adapted into an augmented single-room GN model and a simplified two-room GN model. Both models are shown to be observable, i.e., it is theoretically possible to estimate the quanta generation rate given available measurements. Kalman filters are used to estimate the quanta generation rate. First, a continuous-time extended Kalman filter is used for both adapted models using a simulation and measurement sampling rate of 60 s. Accurate quanta generate rate estimates are achieved in both cases. A more realistic scenario is also considered with a measurement sampling rate of one day. For these estimates, a hybrid extended Kalman filter (HEKF) is used. Accurate quanta generation rate estimates are achieved for the more realistic scenario. Future work could potentially use the HEKFs, the adapted models, and real-time measurements in a control system feedback loop to reduce the transmission of TB in confined spaces such as hospitals.