Abstract
Reaching the United Nations Millennium Development Goals and the necessary statistical literacy for citizens' understanding of what they entail requires that National Statistical Offices (NSOs) become more involved in the promotion of statistical literacy in cooperation with national statistical societies, international organizations like IASE and ISLP and national education institutions. All these stakeholders share an interest in the promotion of statistical literacy in schools yet for rather different reasons. Sharing the different skills that each possesses could have benefits for the long term goals of each stakeholder, could help improve school curricula, contribute to the debate on what constitutes statistical literacy and what is the best way to help citizens achieve it, and make the path to evidence based decision making easier. In this paper we present examples of what National Statistics Offices can and can not do regarding the promotion of statistical literacy in schools. We illustrate the common and singular aspects of their programs for schools and discuss how they follow the current trends in statistics education while helping the National Statistics Office's goal of promoting their statistics products and creating more users of official statistics. The work of the International Statistical Literacy Project (ISLP) is discussed in the context of drawing together stakeholders and resources.

Abstract
Whenever new sequences of DNA or proteins have been decoded it is almost compulsory to look at similar sequences and papers describing those sequences in order to both collect relevant information concerning the function and activity of the new sequences and/or know what is known already about similar sequences that might be useful in the explanation of the function or activity of the newly discovered ones. In current web sites and data bases of sequences there are, usually, a set of paper references linked to each sequence. Those links are very useful because the papers describe useful information concerning the sequences. They are, therefore, a good starting point to look for relevant information related to a set of sequences. One way is to implement such approach is to do a blast with the new decoded sequences, and collect similar sequences. Then one looks at the papers linked with the similar sequences. Most often the number of retrieved papers is small and one has to search large data bases for relevant papers. In this paper we propose a process of generating a classifier based on the initially set of relevant papers that are directly linked to the similar sequences retrieved and use that classifier to automatically enlarge the set of relevant papers by searching the MEDLINE using the automatically constructed classifier. We have empirically evaluated our proposal and report very promising results. © 2011 IEEE.

Abstract

Abstract
This paper presents the user friendly features of a Web site for Multi-Relational Data Mining (MRDM) problems in Molecular Biology and Drug Design. The purpose of the Web site is to allow any non expert in MRDM to perform a data analysis task using an Inductive Logic Programming (ILP) system without any knowledge of the workings of such systems. With that aim, the site provides an extensive library of predicates for the user to construct in an easy way the required data set's background knowledge. The set of predicates is automatically extended, using web-services technology, by searching, in a user transparent way, web sites of other research groups implementing the same Web site architecture. The site also implements a module that allows the user to execute a series of data analysis experiments without any knowledge of the ILP system's parameters and the Prolog encoding of the induced models. Prolog models are translated to English before being shown to the user and an interface, using a set of menus expressing "qualitative options", allows the user to control the updating of the induced models. The Web site has, so far, received positive feedback from the chemist elements of the project team in a drug design problem were we have applied it. © 2011 IADIS.

Abstract
FP7 ICT4Depression project aims at providing a set of tools to,further improve both patient outcome and increase of access to treatment of the patients suffering from major depression. This article describes the Information Systems (IS) architecture used in the project. ICT4Depression uses a service oriented architecture as means of bringing together different kinds of information concerning the patient, the therapeutic modules he is advised to follow and the sensors used to assess his status.

Abstract
A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0, 1]. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up to 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, from the comparisons made it can be concluded that the results produced improve upon some of the best known solutions.