Kinanthropometric differences between 1997 World championship junior elite and 2011 national junior elite triathletes

OBJECTIVES: In 1997, anthropometry measures were made to determine the body size and shapes of both senior and junior elite triathletes. Since then, the junior event distance has changed and the optimal morphology of participants may have evolved. Thus the objective of this study was to compare the morphology of 1997 World championship junior elite triathlon competitors with junior elite competitors in 2011. DESIGN: Comparative study of junior elite triathlete kinanthropometry. METHODS: Twenty-nine males and 20 females junior elite competitors in the 1997 Triathlon World Championships underwent 26 anthropometric measurements. Results were compared with 28 male and 14 female junior elite triathletes who competed in the 2011 Australian National Junior Series, as qualifying for 2011 Triathlon World Championships. Comparisons were made on the raw scores, as well as somatotype, and body proportional scores. RESULTS: Both male and female junior elite triathletes in the 2011 group were significantly more ectomorphic than their 1997 counterparts. The 2011 triathletes were also proportionally lighter, with significantly smaller flexed arm and thigh girths, and femur breadths. The 2011 males recorded significantly longer segmental lengths and lower endomorphy values than the 1997 junior males. CONCLUSIONS: Junior elite triathlete morphology has evolved during the past 14 years possibly as a result of changing race distance and race tactics, highlighting the importance of continually monitoring and updating such anthropometric data.

What works for Nutrition? Stories of success from Vietnam, Uganda and Kenya

This report discusses the drivers of progress in tackling multiple forms of malnutrition in these three countries: Vietnam, Uganda, and Kenya. It also identifies some of the challenges which pose as barriers to sustaining progress. Finally, it makes recommendations for key stakeholders such as governments of high burden countries, donor governments, development partners, and civil society, on their role to promote further success.

The success of worksite nutrition and physical activity interventions with environmental modifications A systematic review

Background: As obesity increases among U.S. workers, employers are implementing programs to increase physical activity and improve diets. Although programs to address individual determinants of obesity have been evaluated, less is known about the effects of workplace programs that change environmental factors, because most reviews have not isolated environmental programs; the one that did was published in 2005. ^ Objective: To update the 2005 review to determine the effectiveness of workplace environmental interventions. ^ Methods: The Medline database was searched for published English language reports (2003-2011) of randomized controlled (RCTs) or quasi-experimental trials (NRCTs) that evaluated strategies to modify physical activity opportunities or food services, targeting employees at least 18 years, not including retirees and that provided data for at least one physical activity, dietary, or health risk indicator. Three coders independently extracted study characteristics and scored the quality of study methods. Program effectiveness was determined using the 2005 review's best evidence approach. ^ Results: Seven studies represented in nine reports met eligibility criteria; three focused on diet and the remainder targeted diet and physical activity interventions. All but one study received a high quality score for internal validity. The evidence for the effectiveness of workplace environmental interventions was at best, inconclusive for diet and physical activity and limited for health risk indicators. The outcome constructs were inconsistent across the studies. ^ Conclusions: Limitations in the methods of the 2005 review made it challenging to draw conclusions about findings for this review that include: variation in outcome measures, reliance on distal measures without proximal behavior change measures, no distinction between changes at the workplace versus outside the workplace, and inappropriate analyses of cluster designs that biased findings toward statistical significance. The best evidence approach relied on vote-counting, using statistical significance alone rather than effect size and confidence intervals. Future research should address these limitations and use more rigorous methods; systematic reviews should use methods of meta-analysis to summarize study findings. These recommendations will help employers to better understand how environmental modifications in the workplace can support their efforts to combat the effects of obesity among employees.^

Emotional Skills and Promoting School Success in the 3rd Cycle: Students Perception

The perceptions about school play a central role in behavior, performance and learning outcomes. There is evidence that an improvement in emotional skills is associated with a higher school success. The aim of this paper is to know the relationship between internalizing and externalizing behaviors, emotional skills and academic success of students of the 3rd cycle of basic education. In order to promote students social and emotional skills, a pilot study in a School Grouping of the central region (Portugal) was carried out. It was made a diagnosis of disruptive behavior (ASEBA) and identified 6 children aged between 12 and 14 years old and followed by 3 focus groups with students, parents, and teachers, respectively. Six students mostly male were identified, with the predominance of externalizing behaviors and academic failure. They don’t like school and have no motivation for learning. The relationship between parents and teachers is conflictive. All parents have the utmost concern academic success and teachers perceived good practices, but without success. This program is seen in a perspective of empowerment of the educational agents to manage various environments and relationships. The results point to the importance of the systemic intervention program on the improvement of the social and emotional competences and academic achievement.

Emotional skills and promoting School Success in the 3rd cycle: students perception

Problem Statement: The perceptions about school, play a central role in behavior, performance and results. There is evidence that an improvement in emotional skills is associated with a higher success. Research Questions: What is the relationship between internalizing and externalizing behaviors, emotional skills and academic success in the 3rd cycle of basic education? Purpose of Study: To promote social and emotional skills of students, in the 3rd cycle of basic education. Research Methods: A pilot study with groups of 7th year at a school central Portugal. Made diagnosis of disruptive behavior (ASEBA) was identified 6 children aged 12 0s and 14 and followed by 3 focus groups with students, parents, and teachers respectively. Findings: 6 students mostly male were identified (70 %), with the predominance of externalizing behaviors and academic failure. Not like school (80%) and have no motivation for learning. The relationship between parents and teachers is conflituoso. 100 % of parents have the utmost concern academic success and teachers perceptional good practices, but without success. Conclusions: This program is seen in a perspective of empowerment of the various educational agents to manage various environments and relationships. The results point to the importance of the focus group in the awareness of relational problems in schools. Less adjusted change behaviors imply the involvement of all educators.

Critical success factors of process modeling for enterprise systems

The integrated and process oriented nature of Enterprise Systems (ES) has led organizations to use process modeling as an aid in managing these systems. Enterprise Systems success factor studies explicitly and implicitly state the importance of process modeling and its contribution to overall Enterprise System success. However, no empirical evidence exists on how to conduct process modeling successfully and possibly differentially in the main phases of the ES life-cycle. This paper reports on an empirical investigation of the factors that influence process modeling success. An a-priori model with 8 candidate success factors has been developed to this stage. This paper introduces the research context and objectives, describes the research design and the derived model, and concludes by looking ahead to the next phases of the research design.

Marketplace forces and the history of retailing : The cycle of control

This paper traces the history of store (retailer-controlled) and national (manufacture controlled)brands; identifies the key historical characteristics of the past 200 years of marketing history;describes the four main time periods of U.S. retail marketing (1800 - 2000); and comments on the most likely developments within the current phases of brand marketing. Will the future focus on technology and new forms of communications? The Internet exemplifies an unconventional retailing environment, with etailer numbers growing rapidly. The central proposition of this paper is that a "cycle of control" - a pattern of marketing developments within the history of retailing and national marketing communications - Can indicate the success of marketing strategies in the future.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Risk assessment in life-cycle costing for road asset management

Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion annually for road infrastructure asset management. To effectively manage road infrastructure, firstly road agencies not only need to optimise the expenditure for data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. This paper presents the results of case studies in using the probability-based method for an integrated approach (i.e. assessing optimal costs of pavement strength data collection; calibrating deterioration prediction models that suit local condition and assessing risk-adjusted budget estimates for road maintenance and rehabilitation for assessing life-cycle budget estimates). The probability concept is opening the path to having the means to predict life-cycle maintenance and rehabilitation budget estimates that have a known probability of success (e.g. produce budget estimates for a project life-cycle cost with 5% probability of exceeding). The paper also presents a conceptual decision-making framework in the form of risk mapping in which the life-cycle budget/cost investment could be considered in conjunction with social, environmental and political issues.

Good practice for enhancing the engagement and success of commencing students

There is widespread recognition that higher education institutions (HEIs) must actively support commencing students to ensure equity in access to the opportunities afforded by higher education. This role is particularly critical for students who because of educational, cultural or financial disadvantage or because they are members of social groups currently under-represented in higher education, may require additional transitional support to “level the playing field.” The challenge faced by HEIs is to provide this “support” in a way that is integrated into regular teaching and learning practices and reaches all commencing students. The Student Success Program (SSP) is an intervention in operation at the Queensland University of Technology (QUT) designed to identify and support those students deemed to be at risk of disengaging from their learning and their institution. Two sets of evidence of the impact of the SSP are presented: First, its expansion (a) from a one-faculty pilot project (Nelson, Duncan & Clarke, 2009) to all faculties and (b) into a variety of applications mirroring the student life cycle; and second, an evaluation of the impact of the SSP on students exposed to it. The outcomes suggest that: the SSP is an example of good practice that can be successfully applied to a variety of learning contexts and student enrolment situations; and the impact of the intervention on student persistence is sustained for at least 12 months and positively influences student retention. It is claimed that the good practice evidenced by the SSP is dependent on its integration into the broader First Year Experience Program at QUT as an example of transition pedagogy in action.

Alcohol expectancy changes over a 12-week cognitive–behavioral therapy program are predictive of treatment success

This study examines if outcome expectancies (perceived consequences of engaging in certain behavior) and self- efficacy expectancies (confidence in personal capacity to regulate behavior) contribute to treatment outcome for alcohol dependence. Few clinical studies have examined these constructs. The Drinking Expectancy Profile (DEP), a psychometric measure of alcohol expectancy and drinking refusal selfefficacy, was administered to 298 alcohol-dependent patients (207 males) at assessment and on completion of a 12-week cognitive–behavioral therapy alcohol abstinence program. Baseline measures of expectancy and self-efficacy were not strong predictors of outcome. However, for the 164 patients who completed treatment, all alcohol expectancy and self-efficacy factors of the DEP showed change over time. The DEP scores approximated community norms at the end of treatment. Discriminant analysis indicated that change in social pressure drinking refusal self-efficacy, sexual enhancement expectancies, and assertion expectancies successfully discriminated those who successfully completed treatment from those who did not. Future research should examine the basis of expectancies related to social functioning as a possible mechanism of treatment response and a means to enhance treatment outcome.