A PILOT STUDY ON PROBLEMS WITH WOMEN'S ATHLETIC SUPPORTIVE WEAR

INTRODUCTION: A study was conducted to investigate some of the discomfort women experienced with athletic supportive wear (sports bras) while playing soccer. Players from a recreational women's soccer league were surveyed. The objective of the survey was to identify specific biomechanical problems female soccer players encountered during the game with their supportive wear. This investigation was conducted as a pilot study for a full-scale analysis of discomfort caused by the design of women's supportive wear. METHODS: Questionnaires were distributed to 35 players in a recreational women's soccer league in Madison, Wisconsin, U.S.A.. The respondents were asked to identify discomfort they encountered while playing soccer with regular and athletic supportive wear. The return rate was 40 % with 14 returned surveys. The ages, heights, weights, bust sizes, and cup sizes (converted to the difference between bust size and circumference of the trunk beneath the breasts) of the respondents are shown in Table 1. RESULTS: The major discomfort reported by respondents who had played soccer in regular supportive wear were lack of support (50%), heat (33%), excessive perspiration (33%), and friction on the skin (22%). For playing soccer in athletic supportive wear, 5 respondents (25.71%) reported no discomfort; the others listed the following as the major discomfort: heat (57%), excessive perspiration (36%), excessive tightness (28%), and friction on the skin (14%). CONCLUSIONS: Athletic supportive appeared to improve the support of the breasts for female recreational soccer players in this survey. The wearers’ breasts ‘bounced’ less. However, the supportive wear also increased the discomfort caused by the accumulation of body heat. Excessive perspiration and friction on the skin remained problematic. Furthermore, excessive tightness became a new discomfort for some players. The data collected in the study may serve as a source for detailed biomechanical studies and complete analysis of the discomfort caused by athletic supportive wear worn by female athletes in different sports

A quality–time–cost-oriented strategy for product conceptualization

In general, product development companies aim to deliver products of optimal quality while incurring minimal cost in the shortest time possible. In this work, a quality–time–cost-oriented strategy (QTCOS) is proposed to facilitate product concept generation and selection. Firstly, general sorting is employed to elicit an initial product platform. The platform, constructed with a design space framework (DSF), serves as a base for generating a preliminary range of design options. Using the repertory grids elicitation method, designers contribute importance ratings with respect to a set of time and cost criteria for the range of design options. To account for trade-offs between cost and time related concerns, these ratings are employed to reduce the number of the derived design options, and thereby used as input features to a restricted coulomb energy (RCE) neural network. The RCE network function is applied to classify the set of design options into different patterns, i.e. cost–time-pairs. The classification results can subsequently serve as bases for the selection of preferred design options. A case study on wood golf club design is conducted to illustrate the proposed QTCOS

Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery’s degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (Vdis) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using Vdis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients