Abstract
Determining the underlying regulatory mechanism of genetic networks is one of the central challenges of computational biology. Numerous methods have been developed and applied to the important but complex task of reverse engineering regulatory networks from high-throughput gene expression data. However, many challenges remain. In this paper, we are interested in learning rules that will reveal the causal genes for the expression variation from various relational data sources in addition to gene expression data. Following our previous work where we showed that time series gene expression data could potentially uncover causal effects, we describe an application of an inductive logic programming (ILP) system, to the task of identifying important regulatory relationships from discretized time series gene expression data, protein-protein interaction, protein phosphorylation and transcription factor data about the organism. Specifically, we learn rules for predicting gene expression levels at the next time step based on the available relational data and then generalize the learned theory to visualize a pruned network of important interactions. We evaluate and present experimental results on microarray experiments from Gasch et al on Saccharomyces cerevisiae.

Abstract
As logic programming applications grow in size, Prolog systems need to efficiently access larger and larger data sets and the need for any- and multi-argument indexing becomes more and more profound. Static generation of multi-argument indexing is one alternative, but applications often rely on features that are inherently dynamic which makes static techniques inapplicable or inaccurate. Another alternative is to employ dynamic schemes for flexible demand-driven indexing of Prolog clauses. We propose such schemes and discuss issues that need to be addressed for their efficient implementation in the context of WAM-based Prolog systems. We have implemented demand-driven indexing in two different Prolog systems and have been able to obtain non-negligible performance speedups: from a few percent up to orders of magnitude. Given these results, we see very little reason for Prolog systems not to incorporate some form of dynamic indexing based on actual demand. In fact, we see demand-driven indexing as only the first step towards effective runtime optimization of Prolog programs.

Abstract
We describe here a sampled-data Model Predictive Control framework that uses continuous-time models but the sampling of the actual state of the plant as well as the computation of the control laws, are carried out at discrete instants of time. This framework can address a very large class of systems, nonlinear, time-varying, and nonholonomic. As in many others sampled-data Model Predictive Control schemes, Barbalat's lemma has an important role in the proof of nominal stability results. It is argued that the generalization of Barbalat's lemma, described here, can have also a similar role in the proof of robust stability results, allowing also to address a very general class of nonlinear, time-varying, nonholonomic systems, subject to disturbances. The possibility of the framework to accommodate discontinuous feedbacks is essential to achieve both nominal stability and robust stability for such general classes of systems.

Abstract

Abstract
A versatile time-domain optical coherence tomography system is presented that can generate cross-sectional images by using either transverse priority or depth priority scanning. This is made possible by using a transmissive scanning delay line compatible with balance detection operating at a speed similar to that of the transverse scanner used to scan the beam across the target. In vivo images from the retina are generated and shown using the same system switched to either transverse or depth priority scanning regime, by using the scanning delay line either in slow or fast scanning modes, respectively. A comparative analysis of different scanning regimes depending on image size to fit different areas to be imaged is presented. Safety thresholds due to the different continuous irradiation time per transverse pixel in different scanning regimes are also considered. We present the maximum exposure level for a variety of scanning procedures, employing either A scanning (depth priority) or T scanning (transverse priority) when generating cross-sectional images, en face images, or collecting 3D volumes. (c) 2007 Optical Society of America.

Abstract
This paper presents an application of Monte Carlo chronological simulation to evaluate the reserve requirements of generating systems, considering renewable energy sources. The idea is to investigate the behavior of reliability indices, including those from the well-being analysis, when the major portion of the energy sources is renewable. Renewable in this work comprises hydroelectric, mini-hydroelectric, and wind power sources. Case studies on a configuration of the Portuguese Generating System are presented and discussed. Copyright (c) 2007 John Wiley & Sons, Ltd.