The development and optimisation of a quantitative physical fitness scoring system for use amongst Naval Service personnel

Background: A lack of research currently exists in relation to the current physical fitness testing system that is used within the Irish Naval Service, not only in relation to the tests that are used but also in relation to the scores that should be achieved in order to pass the test. As such the aim of this study was to select tests for various components of physical fitness and create a scoring system that could be used to assess individuals more comprehensively. Materials and methods: Seventy-five individuals took part in the study (71 males, 4 females). Each participant completed a battery of physical tests analysing the following physical fitness components: flexibility, power, agility, strength, speed, anaerobic conditioning and aerobic conditioning. The mean score ± 0.67 and ± 1 standard deviations were used for the selection of categories. Results: A six category scoring system was produced for each component of physical fitness. Scores were assigned to each category allowing a total cumulative score and an overall percentage of the total to be calculated. The categories are as follows: Score 5, Score 10, Score 15, Score 20, Score 25, Score 30. Conclusions: A quantitative scoring system has been produced that allows comprehensive physical fitness testing to be conducted. In order to achieve a complete picture of a participant’s physical fitness, all tests outlined should be included in the testing process. However, the flexible nature of this system allows for tests to be included or excluded to suit the needs of an individual or organisation. The fact that the scoring system is quantitative, the time involved is relatively short, multiple participants can be tested simultaneously and the pass rates can be decided upon by the host organisation makes this system versatile and comparable across multiple jurisdictions.

Using CBR to select solution strategies in constraint programming

Abstract. Constraint programming is a powerful paradigm that offers many different strategies for solving problems. Choosing a good strategy is difficult; choosing a poor strategy wastes resources and may result in a problem going unsolved. We show how Case-Based Reasoning can be used to select good strategies. We design experiments which demonstrate that, on two problems with quite different characteristics, CBR can outperform four other strategy selection techniques.

Using case-based reasoning to write constraint programs

Abstract. Constraint programming offers powerful problem solving capabilities, but experienced and skilled developers are needed to maximise the benefits it offers. We introduce a method to enable less experienced developers write efficient constraint programs. We describe how Case-Based Reasoning can be used to capture the expertise of skilled constraint programmers and then reuse that knowledge to write new programs.