Bad sports : the risks associated with not 'fitting in'

This paper is about peer rejection and bullying in schools and the tragic consequences that it can have. The focus of the paper is in the ways schools interact with groups of young males and, more importantly, those who are left out of these groupings. My concern is that schools currently give too little attention to informing and/or shaping the peer group practices of young people. I argue that the current educational theory and practice focus too heavily on individuals and their potential to act independently and overlook the all-important socialization that takes place within and between groups. Drawing on two case-studies of young males who experience peer rejection, I seek to raise concerns about the contemporary socializing practices of young males and the burgeoning need for schools to play a role in bringing about change. Given the continued prominence of sport as a trajectory for defining the dominant masculinity, I believe that it is critical that physical educators engage with the discussions and debates that surround this topic.

Fitting generated aggregation operators to empirical data

This paper treats the problem of fitting general aggregation operators with unfixed number of arguments to empirical data. We discuss methods applicable to associative operators (t-norms, t-conorms, uninorms and nullnorms), means and Choquet integral based operators with respect to a universal fuzzy measure. Special attention is paid to k-order additive symmetric fuzzy measures.

Fitting ST-OWA operators to empirical data

The OWA operators gained interest among researchers as they provide a continuum of aggregation operators able to cover the whole range of compensation between the minimum and the maximum. In some circumstances, it is useful to consider a wider range of values, extending below the minimum and over the maximum. ST-OWA are able to surpass the boundaries of variation of ordinary compensatory operators. Their application requires identification of the weighting vector, the t-norm, and the t-conorm. This task can be accomplished by considering both the desired analytical properties and empirical data.

Fitting aggregation operators to data

Theoretical advances in modelling aggregation of information produced a wide range of aggregation operators, applicable to almost every practical problem. The most important classes of aggregation operators include triangular norms, uninorms, generalised means and OWA operators.
With such a variety, an important practical problem has emerged: how to fit the parameters/ weights of these families of aggregation operators to observed data? How to estimate quantitatively whether a given class of operators is suitable as a model in a given practical setting? Aggregation operators are rather special classes of functions, and thus they require specialised regression techniques, which would enforce important theoretical properties, like commutativity or associativity. My presentation will address this issue in detail, and will discuss various regression methods applicable specifically to t-norms, uninorms and generalised means. I will also demonstrate software implementing these regression techniques, which would allow practitioners to paste their data and obtain optimal parameters of the chosen family of operators.

Bioinformatic workflows : G-PIPE as an implementation

We present G-PIPE, a graphic pipeline generator for PISE that allows the definition of pipelines, parameterization of its component methods, and storage of metadata in XML formats. Our implementation goes beyond macro capacities currently in PISE. As the entire analysis protocol is defined in XML, a complete bioinformatic experiment (linked sets of methods, parameters and results) can be reproduced or shared among users. We also discuss the role of ontologies as as guidance systems in order to provide users with the possibility to define abstract work-flows, and execute them. A relevant baseline ontology is presented. Availability: http://if-web.imb.uq.edu.au

Fitting triangular norms to empirical data

This chapter discusses some specific tools that can be used to build triangular norms based on a finite number of (possibly noisy) observations. Such problem arises in applications, when observed data (e.g., decision patterns of experts) need to be modelled with a special class of functions, such as triangular norms. We show how this problem can be transformed into a constrained regression problem, and then efficiently solved. We also discuss related operators: uninorms, nullnorms and associative copulas.

Fitting fuzzy measures by linear programming. Programming library fmtools

We discuss the problem of learning fuzzy measures from empirical data. Values of the discrete Choquet integral are fitted to the data in the least absolute deviation sense. This problem is solved by linear programming techniques. We consider the cases when the data are given on the numerical and interval scales. An open source programming library which facilitates calculations involving fuzzy measures and their learning from data is presented.

Prediction of chlorine and THMs concentration profile in bulk drinking water distribution systems from laboratory data

Nearly all drinking water distribution systems experience a "natural" reduction of disinfection residuals. The most frequently used disinfectant is chlorine, which can decay due to reactions with organic and inorganic compounds in the water and by liquid/solids reaction with the biofilm, pipe walls and sediments. Usually levels of 0.2-0.5 mg/L of free chlorine are required at the point of consumption to maintain bacteriological safety. Higher concentrations are not desirable as they present the problems of taste and odour and increase formation of disinfection by-products. It is usually a considerable concern for the operators of drinking water distribution systems to manage chlorine residuals at the "optimum level", considering all these issues. This paper describes how the chlorine profile in a drinking water distribution system can be modelled and optimised on the basis of readily and inexpensively available laboratory data. Methods are presented for deriving the laboratory data, fitting a chlorine decay model of bulk water to the data and applying the model, in conjunction with a simplified hydraulic model, to obtain the chlorine profile in a distribution system at steady flow conditions. Two case studies are used to demonstrate the utility of the technique. Melbourne's Greenvale-Sydenham distribution system is unfiltered and uses chlorination as its only treatment. The chlorine model developed from laboratory data was applied to the whole system and the chlorine profile was shown to be accurately simulated. Biofilm was not found to critically affect chlorine decay. In the other case study, Sydney Water's Nepean system was modelled from limited hydraulic data. Chlorine decay and trihalomethane (THM) formation in raw and treated water were measured in a laboratory, and a chlorine decay and THM model was derived on the basis of these data. Simulated chlorine and THM profiles agree well with the measured values available. Various applications of this modelling approach are also briefly discussed.

Prediction of chlorine and trihalomethanes concentration profile in bulk drinking water distribution systems from laboratory data

Nearly all drinking water distribution systems experience a "natural" reduction of disinfection residuals. The most frequently used disinfectant is chlorine, which can decay due to reactions with organic and inorganic compounds in the water and by liquid/solids reaction with the biofilm, pipe walls and sediments. Usually levels of 0.2-0.5 mg/L of free chlorine are required at the point of consumption to maintain bacteriological safety. Higher concentrations are not desirable as they present the problems of taste and odour and increase formation of disinfection by-products. It is usually a considerable concern for the operators of drinking water distribution systems to manage chlorine residuals at the "optimum level", considering all these issues. This paper describes how the chlorine profile in a drinking water distribution system can be modelled and optimised on the basis of readily and inexpensively available laboratory data. Methods are presented for deriving the laboratory data, fitting a chlorine decay model of bulk water to the data and applying the model, in conjunction with a simplified hydraulic model, to obtain the chlorine profile in a distribution system at steady flow conditions. Two case studies are used to demonstrate the utility of the technique. Melbourne's Greenvale-Sydenham distribution system is unfiltered and uses chlorination as its only treatment. The chlorine model developed from laboratory data was applied to the whole system and the chlorine profile was shown to be accurately simulated. Biofilm was not found to critically affect chlorine decay. In the other case study, Sydney Water's Nepean system was modelled from limited hydraulic data. Chlorine decay and trihalomethane (THM) formation in raw and treated water were measured in a laboratory, and a chlorine decay and THM model was derived on the basis of these data. Simulated chlorine and THM profiles agree well with the measured values available. Various applications of this modelling approach are also briefly discussed.

Fitting soft center-near design multifocal lenses

The recent launch of the Air Optix Aqua Multifocal lens (Ciba Vision) means that there are three silicone hydrogel (SiHy) multifocal lenses available. The benefits of SiHy materials have been well documented (Dumbleton, 2006), but until recently they weren't available in multifocal designs. With an aging population, it's likely that a large number of contact lens wearers are approaching presbyopia, which will result in a dramatic increase in demand for multifocal lenses.