Analysis of the effect of car size on accident injury probability using automobile insurance data

The Highway Loss Data Institute (HLDI) compiles injury and collision claim information from major insurance companies. These data have indicated that a disproportionately large number of injury claims are made for small cars. As a result, it might be concluded that small cars increase injury risk to their occupants. Recent advertisements by a major automobile manufacturer suggest a similar conclusion should be drawn from these data. In this paper it is shown that the apparent higher injury risk attributed to small cars can reflect the behavior of persons driving small cars. The number of injury claim accidents per collision claim accident is examined as an alternative measure of injury risk. Possible problems in interpretation are discussed.     

Analysis of time-dependent flow-sensitive PC-MRI data

Many flow visualization techniques, especially integration-based methods, are problematic when the measured data exhibit noise and discretization issues. Particularly, this is the case for flow-sensitive phase-contrast magnetic resonance imaging (PC-MRI) data sets which not only record anatomic information, but also time-varying flow information. We propose a novel approach for the visualization of such data sets using integration-based methods. Our ideas are based upon finite-time Lyapunov exponents (FTLE) and enable identification of vessel boundaries in the data as high regions of separation. This allows us to correctly restrict integration-based visualization to blood vessels. We validate our technique by comparing our approach to existing anatomy-based methods as well as addressing the benefits and limitations of using FTLE to restrict flow. We also discuss the importance of parameters, i.e., advection length and data resolution, in establishing a well-defined vessel boundary. We extract appropriate flow lines and surfaces that enable the visualization of blood flow within the vessels. We further enhance the visualization by analyzing flow behavior in the seeded region and generating simplified depictions.     

时变滞后Lurie型系统的改进稳定性准则

In this technical note, we present a new stability analysis procedure for ascertaining the delay-dependent stability of a class of Lurie systems with time-varying delay and sector-bounded nonlinearity using Lyapunov-Krasovskii (LK) functional approach. The proposed analysis, owing to the candidate LK functional and tighter bounding of its time-derivative, yields less conservative absolute and robust stability criteria for nominal and uncertain systems respectively. The effectiveness of the proposed criteria over some of the recently reported results is demonstrated using a numerical example.     

Experimental investigations on high temperature transition of asphalt

Asphalt exhibits Newtonian, non-Newtonian and viscoelastic behavior as the temperature is varied. This investigation reports the temperature at which asphalt exhibits non-Newtonian to Newtonian transition. Air blown, blended asphalt and petroleum pitch were tested at different aging conditions. The temperature at which the material exhibited shear rate independent viscosity was taken as the transition temperature. The transition temperature varied with processing method and aging conditions. It was seen that blended asphalt exhibited greater increase in apparent viscosity during aging when compared to air blown asphalt, whereas air blown asphalt showed greater shift in transition temperature for the same aging conditions.     

Particle swarm optimization for scheduling batch processing machines in a permutation flowshop

Batch processing machines are capable of processing several jobs in a batch simultaneously. These machines are used in many real-life applications. This paper presents solution approaches to schedule batch processing machines arranged in a permutation flowshop in order to minimize its makespan (or completion time of the last batch). The processing time of each job on all the machines and their sizes are given. Each machine can process a batch of jobs as long as its capacity is not violated. The batch processing time is equal to the longest processing job in the batch. Since the problem under study is NP-hard, commercial mixed-integer solvers may require prohibitively long run time to solve even modest sized problems. Consequently, a particle swarm optimization (PSO) algorithm is proposed. Three heuristics to update the particle’s positions are also proposed. The effectiveness of the proposed PSO algorithm is compared with a commercial solver (which was used to solve a mathematical model) and several heuristics from the literature. The experimental study conducted indicates that the proposed PSO algorithm outperforms both the commercial solver and the heuristics in terms of solution quality. The commercial solver requires longer run times compared to PSO.