Drink-driving in community sports clubs: Adopting the Good Sports alcohol management program

Throughout the developed world, community sports clubs are a high-risk setting for alcohol-impaired driving. The Good Sports program accredits community sports clubs to encourage implementation of alcohol-focussed harm-reduction and safe-transport strategies. This study tested for associations between participation in the Good Sports program and reduced rates of drink-driving amongst club members. Multilevel modelling indicated that for each season a club was in the program there was an 8% reduction in the odds of drink-driving. These findings may arise due to clubs with lower rates of alcohol use maintaining longer involvement in the program. However, the findings are also compatible with the intention of the Good Sports program to reduce the risk that club members will drive whilst alcohol impaired.     

Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles

The aim of this paper is to present experimental validation results of an energy management system for hybrid electrical vehicles based on type-2 fuzzy logic. The energy management system (EMS) is designed by extracting knowledge from several experts using surveys. The consideration of interval type-2 fuzzy sets enables modeling the uncertainty in the answers of the experts. The validation of the EMS is performed on a real-scale heavy duty vehicle equipped with different energy sources such as batteries, fuel cell system and ultracapacitors. Experimental results are strong evidence that type-2 fuzzy logic is wide adapted for performing the energy management in hybrid electrical vehicles.     

Exploring hierarchical multidimensional data with unified views of distribution and correlation

Data analysts explore data by inspecting features such as clustering, distribution and correlation. Much existing research has focused on different visualisations for different data exploration tasks. For example, a data analyst might inspect clustering and correlation with scatterplots, but use histograms to inspect a distribution. Such visualisations allow an analyst to confirm prior expectations. For example, a scatterplot may confirm an expected correlation or may show deviations from the expected correlation. In order to better facilitate discovery of unexpected features in data, however, a combination of different perspectives may be needed. In this paper, we combine distributional and correlational views of hierarchical multidimensional data. Our unified view supports the simultaneous exploration of data distribution and correlation. By presenting a unified view, we aim to increase the chances of discovery of unexpected data features, and to provide the means to explore such features in detail. Further, our unified view is equipped with a small number of primitive interaction operators which a user composes to facilitate smooth and flexible exploration.     

On the hierarchy of distribution-sensitive properties for data structures

In this paper new dependencies are added to the hierarchy of the distribution-sensitive properties for data structures. Most remarkably, we prove that the working-set property is equivalent to the unified-bound property; a fact that had gone unnoticed since the introduction of such bounds in the Eighties by Sleator and Tarjan.     

The effects of applying electric fields on the mass spectrometric sampling of positive and negative ions from a flame at atmospheric pressure

Flames are plasmas, because they contain free electrons and both positive and negative ions. The concentrations of ions in a flat flame, burning at 1 bar, have been measured by continuously sampling the hot (2400 K) gas into a mass spectrometer at low pressure. The voltage, Δ?, between the metallic burner and the plate holding the metallic sampling nozzle was varied; also, the flame was seeded with an alkali metal and doped with much larger quantities (mole fraction ?1.7%) of chlorine. Currents of ions such as K⁺ and Cl⁻ were measured with the mass spectrometer for different Δ? and indicated that the sampling nozzle repels free electrons, when it is at a negative potential with respect to the burner (Δ? < 0); consequently the nozzle is then covered by a cathodic sheath of positive ions. Likewise, when Δ? ? 0, the inlet orifice is covered by charged species from the plasma, forming an anodic sheath, from which some electrons reach the nozzle; also some positive and negative ions follow them and so leave the sample. Because the sampled gas is accelerated to a Mach number of unity on entering the inlet orifice, some ions have enough momentum to pass through both a sheath and the entrance hole into the mass spectrometer. The measurements enabled the non-uniform, electric potential between the burner and the plate housing the sampling nozzle to be sketched. The thicknesses of the sheaths were also measured; a cathodic sheath of positive ions is much thicker than an anodic plasma sheath. Also, for Δ? between zero and ∼+30 V, the sheath around the inlet orifice is at its thinnest and the current detected for positive ions a maximum. This is when quantitative measurements of concentrations should be made for positive or negative ions. This study reveals the importance of the electron concentration, the diameter of the inlet orifice, the presence of a halogen, and Δ?, for determining the thicknesses of these sheaths, which do affect the sampling of ions. With chlorine in the flame, the equilibrium: H + C1⁻ = e⁻ + HC1 is sufficiently fast to be maintained, whilst the sampled gas passes through the inlet orifice. This equilibrium usually freezes at some point during the sample’s subsequent, supersonic expansion into the first vacuum chamber; freezing temperatures were deduced. Also the additional cooling of a sample by heat transfer to the sampling nozzle was estimated. It can be difficult to measure accurately the concentration of a negative ion in a flame, because negative ions, unlike positive ones, are often lost during sampling by participating with free electrons in such a chemical equilibrium, which shifts while the sample is cooled.     

Mathematical models of the transitions between endocrine therapy responsive and resistant states in breast cancer

Endocrine therapy, targeting the oestrogen receptor pathway, is the most common treatment for oestrogen receptor-positive breast cancers. Unfortunately, these tumours frequently develop resistance to endocrine therapies. Among the strategies to treat resistant tumours are sequential treatment (in which second-line drugs are used to gain additional responses) and intermittent treatment (in which a ‘drug holiday’ is imposed between treatments). To gain a more rigorous understanding of the mechanisms underlying these strategies, we present a mathematical model that captures the transitions among three different, experimentally observed, oestrogen-sensitivity phenotypes in breast cancer (sensitive, hypersensitive and independent). To provide a global view of the transitions between these phenotypes, we compute the potential landscape associated with the model. We show how this oestrogen response landscape can be reshaped by population selection, which is a crucial force in promoting acquired resistance. Techniques from statistical physics are used to create a population-level state-transition model from the cellular-level model. We then illustrate how this population-level model can be used to analyse and optimize sequential and intermittent oestrogen-deprivation protocols for breast cancer. The approach used in this study is general and can also be applied to investigate treatment strategies for other types of cancer.