Fabric liquid moisture transport properties in multidimensions, called moisture management properties, significantly influence human perceptions of moisture sensations. A new method and instrument called the moisture management tester (MMT) is developed to evaluate textile moisture management properties. This new method can be used to quantitatively measure liquid moisture transfer in one step in a fabric in multidirections, where liquid moisture spreads on both surfaces of the fabric and transfers from one surface to the opposite. Ten indexes are introduced to characterize the liquid moisture management properties of fabrics. Eight sets of sportswear are tested with the MMT and the results show that liquid moisture management properties are significantly different for these fabrics. The objective measurements are compared with subjective perceptions of moisture sensations during exercise. A fabric’s one-way-transport capacity and its overall moisture management capacity are significantly correlated with perceptions of clammy and damp sensations with increased exercise time, indicating that subjective perceptions of moisture sensations in sweating such as clammy and damp can be predicted by the measurements of the MMT.

No abstract

Background. The dimensionality of student approaches to learning, inherent in the instruments commonly used to measure them, lacks consistency with research, particularly concerning Asian students, conducted since the original instruments were devised. Aims.The study examined four strands of evidence to examine whether, and if so how, the dimensionality of the instruments should be modi ed. 1. A qualitative study of students' perceptions of the motivational aspects of curricula. 2. A study by con rmatory factor analysis of the SPQ and LPQ which suggested that the surface strategy subscale can be split into two. 3. Recent research, mainly in Asia, which suggests the existence of approaches to learning which combine understanding with memorisation. 4. A review of literature relevant to the motivational dimensions of approaches to learning.Methods and Samples. Item 1 used semi-structured interviews with 55 undergraduate students. Item 2 used con rmatory factor analysis on a sample of 4863 university students from Hong Kong universities who completed the Study Process Questionnaire. Items 3 and 4 were drawn from a review of pertinent literature. Results.The strands of evidence con rm earlier work which suggests that approaches to learning are best described by a model with two main factors, which we labelled meaning and reproducing. Each main factor has a strategy indicator characterised principally by the presence or absence of the intention to understand the material. We also found evidence of four motivation indicators; intrinsic interest, a positive motivator referring to courses with good career preparation, a negative minimising motive and achievement motivation. The nal category needs to take into account the evidence of communal rather than competitive achievement motive. Conclusions.Our suggestion is that the instruments be redeveloped in two forms. A simple two-factor -deep and surface -instrument would be suitable for teaching evaluation and simple research applications. The development and testing of an instrument which took into account all the strategy and motive elements would be a useful exercise in clarifying the dimensionality of approaches to learning. It would also permit a more thorough examination of cultural in uences upon approaches to learning.

The objective of this'paper is to investigate the predictability of clothing sensory comfort from psychological perceptions by using a feed-forward back-propagation net work in an artificial neural network (ANN) system. In order to achieve the objective, a series of wear trials is conducted in which ten sensory perceptions ( clammy, clingy, damp, sticky, heavy, prickly, scratchy, fit, breathable, and thermal) and overall clothing comfort ( comfort) are rated by twenty-two professional athletes in a controlled la ratory. They are asked to wear four different garments in each trial and rate the sensations above during a 90-minute exercising period. The scores are were input into five different eed-forward back-propagation neural network models, consisting of six different numbers of hidden and output transfer neurons. Results showing a good correlation between redicted and actual comfort ratings with a significance of p < 0:001 for all five models indicate overall comfort performance is predictable with neural networks, particularly models with log sigmoid hidden neurons and pure linear output neurons. Models with a single log sigmoid hidden layer with fifteen neurons or three hidden layers, each with ten log sigmoid hidden neurons, are able to produce better predictions than the other models for is particular data set in the study.

This paper investigates the process of human psychological perceptions of clothing- related sensations and comfort to develop an intellectual understanding of and method ology for predicting clothing comfort performance from fabric physical properties. Var ious hybrid models are developed using different modeling techniques by studying human sensory perception and judgement processes. By combining the strengths of statistics (data reduction and information summation), a neural network (self-learning ability), and fuzzy logic (fuzzy reasoning ability), hybrid models are developed to simulate different stages of the perception process. Results show that the TS-TS-NN-FL model has the highest ability to predict overall comfort performance from fabric physical properties. To summarize, the three key elements in predicting psychological perceptions of clothing comfort from fabric physical properties are data reduction and summation, self-learning, and fuzzy reasoning. This paper shows that the model that integrates these three elements can generate the best predictions compared with other hybrid models.