Large deviations analysis of the generalized processor sharing policy

In this paper we consider a stochastic server (modeling a multiclass communication switch) fed by a set of parallel buffers. The dynamics of the system evolve in discrete-time and the generalized processor sharing (GPS) scheduling policy of [25] is implemented. The arrival process in each buffer is an arbitrary, and possibly autocorrelated, stochastic process. We obtain a large deviations asymptotic for the buffer overflow probability at each buffer. In the standard large deviations methodology, we provide a lower and a matching (up to first degree in the exponent) upper bound on the buffer overflow probabilities. We view the problem of finding a most likely sample path that leads to an overflow as an optimal control problem. Using ideas from convex optimization we analytically solve the control problem to obtain both the asymptotic exponent of the overflow probability and a characterization of most likely modes of overflow. These results have important implications for traffic management of high-speed networks. They extend the deterministic, worst-case analysis of [25] to the case where a detailed statistical model of the input traffic is available and can be used as a basis for an admission control mechanism. This revised version was published online in June 2006 with corrections to the Cover Date.     

Multidimensional scaling and visualization of large molecular similarity tables

Multidimensional scaling (MDS) is a collection of statistical techniques that attempt to embed a set of patterns described by means of a dissimilarity matrix into a low‐dimensional display plane in a way that preserves their original pairwise interrelationships as closely as possible. Unfortunately, current MDS algorithms are notoriously slow, and their use is limited to small data sets. In this article, we present a family of algorithms that combine nonlinear mapping techniques with neural networks, and make possible the scaling of very large data sets that are intractable with conventional methodologies. The method employs a nonlinear mapping algorithm to project a small random sample, and then “learns” the underlying transform using one or more multilayer perceptrons. The distinct advantage of this approach is that it captures the nonlinear mapping relationship in an explicit function, and allows the scaling of additional patterns as they become available, without the need to reconstruct the entire map. A novel encoding scheme is described, allowing this methodology to be used with a wide variety of input data representations and similarity functions. The potential of the algorithm is illustrated in the analysis of two combinatorial libraries and an ensemble of molecular conformations. The method is particularly useful for extracting low‐dimensional Cartesian coordinate vectors from large binary spaces, such as those encountered in the analysis of large chemical data sets. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 488–500, 2001     

A Mathematical Radiobiological Model (MRM) to Predict Complex DNA Damage and Cell Survival for Ionizing Particle Radiations of Varying Quality

Predicting radiobiological effects is important in different areas of basic or clinical applications using ionizing radiation (IR); for example, towards optimizing radiation protection or radiation therapy protocols. In this case, we utilized as a basis the ‘MultiScale Approach (MSA)’ model and developed an integrated mathematical radiobiological model (MRM) with several modifications and improvements. Based on this new adaptation of the MSA model, we have predicted cell-specific levels of initial complex DNA damage and cell survival for irradiation with ¹¹Β, ¹²C, ¹⁴Ν, ¹⁶Ο, ²⁰Νe, ⁴⁰Αr, ²⁸Si and ⁵⁶Fe ions by using only three input parameters (particle’s LET and two cell-specific parameters: the cross sectional area of each cell nucleus and its genome size). The model-predicted survival curves are in good agreement with the experimental ones. The particle Relative Biological Effectiveness (RBE) and Oxygen Enhancement Ratio (OER) are also calculated in a very satisfactory way. The proposed integrated MRM model (within current limitations) can be a useful tool for the assessment of radiation biological damage for ions used in hadron-beam radiation therapy or radiation protection purposes.     

Effect of the type of organic modifier on the polymerization kinetics and the properties of poly(methyl methacrylate)/organomodified montmorillonite nanocomposites

A series of organomodified montmorillonite clays (OMMTs) such as intercalated modified montmorillonite with alkylammonium or alkylphosphonium salts (AA-MMT or AP-MMT) and double modified MMT with alkylammonium or alkylphosphonium salts and silane coupling agent (SAA-MMT or SAP-MMT) was successfully prepared in this study. The effect of the amount of nanofiller and type of organic modifier of the OMMT on poly(methyl methacrylate) (PMMA)/OMMT nanocomposites synthesized by in situ bulk polymerization was investigated. The structural and morphological characteristics of the obtained nanocomposites were studied by means of X-ray diffraction and transmission electron microscopy, indicating that exfoliation is more likely to occur in case of nanocomposites with small amounts of AA-MMT and SAA-MMT. The kinetic study results showed that the presence of AA-MMT enhances polymerization kinetics, while AP-MMT acts rather as a reaction retarder. The presence of the nanofiller and the augmentation of the OMMT content increased the thermal stability of all nanocomposites, as measured by thermogravimetric analysis, as well as their average molecular weight measured by gel permeation chromatography. Measurements of the tensile properties revealed that the Young’s modulus increased for all nanocomposites along with a decrease of the ultimate strain, while the tensile strength varied regardless of the extent of exfoliation.