Effects of Adsorbed Polyaniline on Redox Processes on As(2)O(3) Surfaces

Polyaniline deposited on As(2)O(3) surface resulted in a new material, which was characterized by infrared spectoscopy, thermogravimetry, differential scanning calorimetry, scanning electron microscopy, X-ray diffraction, and cyclic voltammetry. The mass percentage of polymer deposited on oxide surface is approximately 13%. The scanning electron microscopy images as well as the X-ray diffraction patterns provided conclusive evidence that the oxide surface is coated by the polymer. The cyclic voltammograms of the polyaniline adsorbed on As(2)O(3) surface showed that the adsorbate exerts remarkable effects on redox processes on this oxide. The pure oxide exhibited two oxidation/reduction peaks at 0.25/-0.06 and 0.47/-0.25 V attributed tentatively to the processes As(2)O(3)(s)+6H(+)+6e(-)=2As(s)+3H(2)O and As(s)+3H(+)+3e(-)=AsH(3)(g), respectively. The polyaniline-coated sample exhibited a better-defined voltammogram in which the first oxidation peak of the oxide had its intensity increased about four times. Copyright 2000 Academic Press.     

The Third International Timetabling Competition

This paper is the organizers’ report on the Third International Timetabling Competition (ITC2011), run during 2011–2012 with the aim of raising the profile of automated high school timetabling. Its participants tackled 35 instances of the high school timetabling problem, taken from schools in 10 countries. The paper describes the data model used, the competition, and the results.     

The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior

A key to advancing the understanding of molecular biology in the post-genomic age is the development of accurate predictive models for genetic regulation, protein interaction, metabolism, and other biochemical processes. To facilitate model development, simulation algorithms must provide an accurate representation of the system, while performing the simulation in a reasonable amount of time. Gillespie's stochastic simulation algorithm (SSA) accurately depicts spatially homogeneous models with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity. In this work, we examine the performance of different versions of the SSA when applied to several biochemical models. Through our analysis, we discover that transient changes in reaction execution frequencies, which are typical of biochemical models with gene induction and repression, can dramatically affect simulator performance. To account for these shifts, we propose a new algorithm called the sorting direct method that maintains a loosely sorted order of the reactions as the simulation executes. Our measurements show that the sorting direct method performs favorably when compared to other well-known exact stochastic simulation algorithms.     

A simulated annealing hyper-heuristic methodology for flexible decision support

Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.