Design of a Web Based Underwater Acoustic Communication Testbed and Simulation Platform

Wireless Personal Communications - Underwater Wireless Sensor Networks (UWSNs) are playing a vital role in exploring the unseen underwater (UW) natural resources. However, performance evaluation of...     

A local and global tour on MOMoT

Software and Systems Modeling - Many model transformation scenarios require flexible execution strategies as they should produce models with the highest possible quality. At the same time,...     

Solid state reactions between SiC and Ir

Reactivity between SiC and Ir as a function of SiC-crystallinity was investigated by diffusion bonding technique under a vacuum and over the temperature range of 1200–1450 °C. As reaction products, various Ir-silicides and free unreacted-C were detected. Reactivity is strongly affected by the temperature and SiC-crystallinity involving a series of interactions, from “no reaction” to “massive exothermic reactions”. In particular, interfacial phenomena are more pronounced by the presence of defects and grain boundaries.Solid state reactions result in formation of fine C-precipitates rearranged in a quasi-periodic microstructure. On the contrary, clustering of highly ordered C-precipitates (C-graphitized) occurs after “massive reactions” take place.A relationship between the degree of graphitization (from 1 to multi-layers of graphene), temperature and SiC crystallinity was found by Raman spectroscopy. 2D-layering phenomenon is enhanced in polycrystalline SiC at high temperature.     

On Burst Length Distribution and Preemption Probabilities in OBS Networks with Burst Segmentation

Recently, preemption techniques have attracted considerable attention as a means to provide differentiated quality of service in optical burst switching (OBS) networks. This paper is focused on the analysis of preemption probabilities for bursts within the same priority class. As proposed by Vokkarane and Jue ((2003)* IEEE J Select Areas Commun 21(7): 1198–1209) an incoming burst will preempt the burst in service, within the same priority class, if the residual length of the burst in service is smaller than the incoming burst length. For a general case with wavelength conversion, the preemption probability of contending bursts with a generic service distribution, not necessarily exponential, is analyzed. First, we show that the size distribution for the preempting bursts is shifted to larger values, in comparison to the original burst size distribution. Second, we obtain an upper and lower bound for the preemption probability. Finally, the asymptotic behavior of the OBS switch is analyzed showing that preemption will always occur for a very large number of wavelengths.     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto