ISSN exercise & sport nutrition review: research & recommendations

Sports nutrition is a constantly evolving field with hundreds of research papers published annually. For this reason, keeping up to date with the literature is often difficult. This paper is a five year update of the sports nutrition review article published as the lead paper to launch the JISSN in 2004 and presents a well-referenced overview of the current state of the science related to how to optimize training and athletic performance through nutrition. More specifically, this paper provides an overview of: 1.) The definitional category of ergogenic aids and dietary supplements; 2.) How dietary supplements are legally regulated; 3.) How to evaluate the scientific merit of nutritional supplements; 4.) General nutritional strategies to optimize performance and enhance recovery; and, 5.) An overview of our current understanding of the ergogenic value of nutrition and dietary supplementation in regards to weight gain, weight loss, and performance enhancement. Our hope is that ISSN members and individuals interested in sports nutrition find this review useful in their daily practice and consultation with their clients.     

Support vector machines for urban growth modeling

This paper presents a novel method to model urban land use conversion using support vector machines (SVMs), a new generation of machine learning algorithms used in the classification and regression domains. This method derives the relationship between rural-urban land use change and various factors, such as population, distance to road and facilities, and surrounding land use. Our study showed that SVMs are an effective approach to estimating the land use conversion model, owing to their ability to model non-linear relationships, good generalization performance, and achievement of a global and unique optimum. The rural-urban land use conversions of New Castle County, Delaware between 1984–1992, 1992–1997, and 1997–2002 were used as a case study to demonstrate the applicability of SVMs to urban expansion modeling. The performance of SVMs was also compared with a commonly used binomial logistic regression (BLR) model, and the results, in terms of the overall modeling accuracy and McNamara’s test, consistently corroborated the better performance of SVMs.     

Effect of seed coat fragments on cotton yarn structure: impact of fragment size,attached fibres and position within yarn core

Seed coat fragments are a major source of cotton yarn imperfections. This article discusses the factors influencing the disruption caused by seed coat fragments in ring-spun yarn structure with a focus on three characteristics: the fragment's size, the amount of fibres attached to it and its position relative to the yarn core. The three characteristics were investigated through two experiments involving various types of yarn defects and a range of ring-spun yarn counts. Results presented in this article indicate that the three factors considered have a significant influence on the intensity of the disruption the seed coat fragment causes in the yarn structure, and are determinant of the type of defect that this disruption engenders (short/long defect).     

Estimating life cycle plant maintenance costs

The financial success or otherwise of a maintenance strategy can be determined realistically only when actual costs are compared with target ‘predicted’ values. This is because, in the absence of accurate cost control mechanisms, maintenance expenditure can exceed capital cost. In order to address this problem, a methodology is presented for predicting life cycle maintenance expenditure over the useful life of tracked hydraulic excavators. First, time series analysis (using a moving centred average) illustrates that the trend in maintenance cost expenditure is difficult to isolate due to large perturbations in periodic unscheduled maintenance. Such expenditure accounts for 92% of total maintenance cost. Second, cumulative costs of maintenance are modelled using a cubic equation, with time (in hours) as the predictor (x_n) variable. Model performance is robust with a coefficient of determination (R²) of 0.99 and a mean percentage error (MPE) of minus 3.94. The paper concludes with direction for future research in this field, particularly with respect to expanding the model's application to machines of various weights, manufacturers and machines operating in other industries, and determination of criteria with which to predict when a plant item will reach the end of its useful life.     

Factors affecting clients' project dispute resolution satisfaction in Hong Kong

The use of project dispute resolution satisfaction (DRS) as a project success measurement responds to the increasing concern over the spread of the dispute epidemic within the construction industry in Hong Kong. The DRS of a project is considered as favourable where disputes are resolved within the site level. With the use of 48 project data sets and the technique of multivariate discriminant analysis, a discriminant model function was derived to classify projects into favourable DRS and adverse DRS. The multivariate discriminant analysis model function identified eight discriminating variables, among which the degree of design changes offered the highest separation power. The reliability of the discrminating variables was reinforced by a separate relative importance index study. Through the use of principal component factor analysis, these variables were grouped into three factors generically described as substantive influence, facilitation and indirect influence.     

Control of translation initiation: a model-based analysis from limited experimental data

We have built a detailed kinetic model of translation initiation in yeast and have used a novel approach to determine the flux controlling steps based on limited experimental data. An efficient parameter estimation method was adapted in order to fit the most uncertain parameters (rate constants) to in vivo measurements in yeast. However, it was found that there were many other sets of plausible parameter values that also gave a good fit of the model to the data. We therefore used random sampling of this uncertain parameter space to generate a large number of diverse fitted parameter sets. A compact characterization of these parameter sets was provided by considering flux control. In particular, we suggest that the rate of translation initiation is most strongly influenced by one of two reactions: either the guanine nucleotide exchange reaction involving initiation factors eIF2 and eIF2B or the assembly of the multifactor complex from its constituent protein/tRNA containing complexes. It is hoped that the approach presented in this paper will add to our understanding of translation initiation pathway and can be used to identify key system-level properties of other biochemical processes.