Mobile robot navigation as a planar visual servoing problem

This paper demonstrates some interesting connections between the hitherto disparate fields of mobile robot navigation and image-based visual servoing. A planar formulation of the well-known image-based visual servoing method leads to a bearing-only navigation system that requires no explicit localization and directly yields desired velocity. The well known benefits of image-based visual servoing such as robustness apply also to the planar case. Simulation results are presented.

Mobile robot navigation as a planar visual servoing problem

This paper demonstrates some interesting connections between the hitherto disparate fields of mobile robot navigation and image-based visual servoing. A planar formulation of the well-known image-based visual servoing method leads to a bearing-only navigation system that requires no explicit localization and directly yields desired velocity. The well known benefits of image-based visual servoing such as robustness apply also to the planar case. Simulation results are presented.

The Validity of the Brief Michigan Alcohol Screening Test (bMAST) as a Problem Drinking Severity Measure

Objective: The Brief Michigan Alcoholism Screening Test (bMAST) is a 10-item test derived from the 25-item Michigan Alcoholism Screening Test (MAST). It is widely used in the assessment of alcohol dependence. In the absence of previous validation studies, the principal aim of this study was to assess the validity and reliability of the bMAST as a measure of the severity of problem drinking. Method: There were 6,594 patients (4,854 men, 1,740 women) who had been referred for alcohol-use disorders to a hospital alcohol and drug service who voluntarily participated in this study. Results: An exploratory factor analysis defined a two-factor solution, consisting of Perception of Current Drinking and Drinking Consequences factors. Structural equation modeling confirmed that the fit of a nine-item, two-factor model was superior to the original one-factor model. Concurrent validity was assessed through simultaneous administration of the Alcohol Use Disorders Identification Test (AUDIT) and associations with alcohol consumption and clinically assessed features of alcohol dependence. The two-factor bMAST model showed moderate correlations with the AUDIT. The two-factor bMAST and AUDIT were similarly associated with quantity of alcohol consumption and clinically assessed dependence severity features. No differences were observed between the existing weighted scoring system and the proposed simple scoring system. Conclusions: In this study, both the existing bMAST total score and the two-factor model identified were as effective as the AUDIT in assessing problem drinking severity. There are additional advantages of employing the two-factor bMAST in the assessment and treatment planning of patients seeking treatment for alcohol-use disorders. (J. Stud. Alcohol Drugs 68: 771-779,2007)

Children, Obesity and Exercise: Prevention, Treatment and Management of Childhood and Adolescent Obesity

Throughout the developed world there is an increasing prevalence of childhood obesity. Because of this increase, and awareness of the risks to long term health that childhood obesity presents, the phenomena is now described by many as a global epidemic. Children, Obesity and Exercise provides sport, exercise and medicine students and professionals with an accessible and practical guide to understanding and managing childhood and adolescent obesity. It covers: overweight, obesity and body composition; physical activity, growth and development; psycho-social aspects of childhood obesity; physical activity behaviours; eating behaviours; measuring childrens behaviour; interventions for prevention and management of childhood obesity. Children, Obesity and Exercise addresses the need for authoritative advice and innovative approaches to the prevention and management of this chronic problem.

Longing to belong : social inclusion and wellbeing among youth with refugee backgrounds in the first three years in Melbourne, Australia

For young people with refugee backgrounds, establishing a sense of belonging to their family and community, and to their country of resettlement is essential for wellbeing. This paper describes the psychosocial factors associated with subjective health and wellbeing outcomes among a cohort of 97 refugee youth (aged 11-19) during their first three years in Melbourne, Australia. The findings reported here are drawn from the Good Starts Study, a longitudinal investigation of settlement and wellbeing among refugee youth conducted between 2004 and 2008. The overall aim of Good Starts was to identify the psychosocial factors that assist youth with refugee backgrounds in making a good start in their new country. A particular focus was on key transitions: from pre-arrival to Australia, from the language school to mainstream school, and from mainstream school to higher education or to the workforce. Good Starts used a mix of both method and theory from anthropology and social epidemiology. Using standardized measures of wellbeing and generalised estimating equations to model the predictors of wellbeing over time, this paper reports that key factors strongly associated with wellbeing outcomes are those that can be described as indicators of belonging e the most important being subjective social status in the broader Australian community, perceived discrimination and bullying. We argue that settlement specific policies and programs can ultimately be effective if embedded within a broader socially inclusive society - one that offers real opportunities for youth with refugee backgrounds to flourish.

A cooperative coevolutionary algorithm for the composite SaaS Placement Problem in the Cloud

Cloud computing has become a main medium for Software as a Service (SaaS) hosting as it can provide the scalability a SaaS requires. One of the challenges in hosting the SaaS is the placement process where the placement has to consider SaaS interactions between its components and SaaS interactions with its data components. A previous research has tackled this problem using a classical genetic algorithm (GA) approach. This paper proposes a cooperative coevolutionary algorithm (CCEA) approach. The CCEA has been implemented and evaluated and the result has shown that the CCEA has produced higher quality solutions compared to the GA.