Explaining the road accident risk: Weather effects

This research aims to highlight the link between weather conditions and road accident risk at an aggregate level and on a monthly basis, in order to improve road safety monitoring at a national level. It is based on some case studies carried out in Work Package 7 on “Data analysis and synthesis” of the EU-FP6 project “SafetyNet – Building the European Road Safety Observatory”, which illustrate the use of weather variables for analysing changes in the number of road injury accidents. Time series analysis models with explanatory variables that measure the weather quantitatively were used and applied to aggregate datasets of injury accidents for France, the Netherlands and the Athens region, over periods of more than 20 years. The main results reveal significant correlations on a monthly basis between weather variables and the aggregate number of injury accidents, but the magnitude and even the sign of these correlations vary according to the type of road (motorways, rural roads or urban roads). Moreover, in the case of the interurban network in France, it appears that the rainfall effect is mainly direct on motorways – exposure being unchanged, and partly indirect on main roads – as a result of changes in exposure. Additional results obtained on a daily basis for the Athens region indicate that capturing the within-the-month variability of the weather variables and including it in a monthly model highlights the effects of extreme weather. Such findings are consistent with previous results obtained for France using a similar approach, with the exception of the negative correlation between precipitation and the number of injury accidents found for the Athens region, which is further investigated. The outlook for the approach and its added value are discussed in the conclusion.     

Ant colony and particle swarm optimization for financial classification problems

Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Such models ought to be as accurate as possible. One important step towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. This is known as the feature selection problem in the machine learning/data mining field. In financial decisions, feature selection is often based on the subjective judgment of the experts. Nevertheless, automated feature selection algorithms could be of great help to the decision-makers providing the means to explore efficiently the solution space. This study uses two nature-inspired methods, namely ant colony optimization and particle swarm optimization, for this problem. The modelling context is developed and the performance of the methods is tested in two financial classification tasks, involving credit risk assessment and audit qualifications.     

Geometrical methods for level set based abdominal aortic aneurysm thrombus and outer wall 2D image segmentation

Abdominal aortic aneurysm (AAA) is a localized dilatation of the aortic wall. Accurate measurements of its geometric characteristics are critical for a reliable estimate of AAA rupture risk. However, current imaging modalities do not provide sufficient contrast to distinguish thrombus from surrounding tissue thus making the task of segmentation quite challenging. The main objective of this paper is to address this problem and accurately extract the thrombus and outer wall boundaries from cross sections of a 3D AAA image data set (CTA). This is achieved by new geometrical methods applied to the boundary curves obtained by a Level Set Method (LSM). Such methods address the problem of leakage of a moving front into sectors of similar intensity and that of the presence of calcifications. The versatility of the methods is tested by creating artificial images which simulate the real cases. Segmentation quality is quantified by comparing the results with a manual segmentation of the slices of ten patient data sets. Sensitivity to the parameter settings and reproducibility are analyzed. This is the first work to our knowledge that utilizes the level set framework to extract both the thrombus and external AAA wall boundaries.     

Incentive Compatible Pricing Strategies for QoS Routing

QoS routing mechanisms allow users identify paths that can accommodate their performance requirements and reserve the necessary resources. An important problem is how to conduct such resource allocation efficiently, not only from the single-connection, but also from the network point of view. We propose the use of pricing mechanisms as a means to regulate the users' decisions in a networkwide efficient manner. Focusing on QoS architectures that employ rate-based schedulers, we formulate a congestion-based pricing scheme. We establish the structure of the corresponding user-optimal response, i.e., a path selection algorithm that satisfies the user's requirements at minimal cost. We show that the underlying noncooperative game among users has a unique equilibrium, for any particular choice of price functions. Then, we establish the existence of incentive compatible price functions, which drive the network into an equilibrium point that coincides with the optimum of a social function. Specifically, these price functions are the derivatives of the social function. We then extend our results to scenarios in which users can identify only sub-optimal paths, as is often the case with multi-constrained path optimization.     

Engineering the xenobiotic substrate specificity of maize glutathione S-transferase I

Glutathione S-transferases (GSTs) are a heterogeneous family of enzymes that catalyse the conjugation of glutathione (GSH) to electrophilic sites on a variety of hydrophobic substrates. In the present study three amino acid residues (Trp12, Phe35 and Ile118) of the xenobiotic binding site (H-site) of maize GST I were altered in order to evaluate their contribution to substrate binding and catalysis. These residues are not conserved and hence may affect substrate specificity and/or product dissociation. The results demonstrate that these residues are important structural moieties that modulate an enzyme's catalytic efficiency and specificity. Phe35 and Ile118 also participate in k(cat) regulation by affecting the rate-limiting step of the catalytic reaction. The effect of temperature on the catalytic activity of the wild-type and mutant enzymes was also investigated. Biphasic Arrhenius and Eyring plots for the wild-type enzyme showed an apparent transition temperature at 35 degrees C, which seems to be the result of a change in the rate-limiting step of the catalytic reaction. Thermodynamic analysis of the activity data showed that the activation energy increases at low temperatures, whereas the entropy change seems to be the main determinant that contributes to the rate-limiting step at high temperatures.     

Thermoregulatory and cardiovascular responses to creatine, glycerol and alpha lipoic acid in trained cyclists

Background

It has been shown that supplementation with creatine (Cr) and glycerol (Gly), when combined with glucose (Glu) necessary for the enhancement of Cr uptake by skeletal muscle, induces significant improvements in thermoregulatory and cardiovascular responses during exercise in the heat.

Purpose

To determine whether Cr/Gly-induced thermoregulatory and cardiovascular responses are maintained when the majority (~75%) of the Glu in the Cr/Gly supplement is replaced with the insulintropic agent alpha lipoic acid (Ala).

Methods

22 healthy endurance trained cyclists were randomly assigned to receive either 20?g/day (4?×?5?g/day) of Cr, 2?g ^.kg^-1 BM per day (4?×?0.5?g ^.kg^-1 BM per day) of Gly and 150?g/day (4?×?37.5?g/day) of Glu or 20?g/day (4?×?5?g/day) of Cr monohydrate, 2?g ^.kg^-1 BM per day (4?×?0.5?g ^.kg^-1 BM per day) of Gly (100?g/day (4?×?25?g/day) of Glu and 1000?mg/day (4?×?250?mg/day) of Ala for 7?days for 7?days. Exercise trials were conducted pre- and post-supplementation and involved 40?min of constant-load cycling exercise at 70% O₂ max by a self-paced 16.1?km time trial at 30°C and 70% relative humidity.

Results

Median and range values of TBW increased significantly by 2.1 (1.3-3.3) L and 1.8 (0.2-4.6) L in Cr/Gly/Glu and Cr/Gly/Glu/Ala groups respectively (P?=?0.03) and of BM not significantly by 1.8 (0.2-3.0) kg and 1.2 (0.5-2.1) kg in Cr/Gly/Glu and in Cr/Gly/Glu/Ala, respectively (P?=?0.75). During constant load exercise, heart rate (HR) and core temperature (Tcore) were significantly lower post-supplementation: HR was reduced on average by 3.3?±?2.1 beats/min and by 4.8?±?3.3 beats/min (mean ± SD) and Tcore by 0.2?±?0.1 (mean ± SD) in the Cr/Gly/Glu and Cr/Gly/Glu/Ala, respectively The reduction in HR and Tcore was not significantly different between the supplementation groups.

Conclusions

In comparison to the established hyper hydrating Cr/Gly/Glu supplement, supplement containing Cr/Gly/Ala and decreased amount of Glu provides equal improvements in thermoregulatory and cardiovascular responses during exercise in the heat.     

Parallel computational strategies for structural optimization

The objective of this paper is to investigate the efficiency of various computational algorithms implemented in the framework of structural optimization methods based on evolutionary algorithms. In particular, the efficiency of parallel computational strategies is examined with reference to evolution strategies (ES) and genetic algorithms (GA). Parallel strategies are implemented both at the level of the optimization algorithm, by exploiting the natural parallelization features of the evolutionary algorithms, as well as at the level of the repeated structural analysis problems that are required by ES and GA. In the latter case the finite element solutions are performed by the FETI domain decomposition method specially tailored to the particular type of problems at hand. The proposed methodology is generic and can be applied to all types of optimization problems as long as they involve large‐scale finite element simulations. The numerical tests of the present study are performed on sizing optimization of skeletal structures. The numerical tests demonstrate the computational advantages of the proposed parallel strategies, which become more pronounced in large‐scale optimization problems. Copyright © 2003 John Wiley & Sons, Ltd.