Abstract
Optimisation problems in Healthcare have received considerable attention for more than three decades. More recently, however, with decreasing birth rates in nearly all of the developed countries and increasing average longevity globally, optimisation issues in Healthcare have become noticeably important and attract keen interest from the Operations Research community. Over the years, attention has gradually expanded from resource allocation and strategic planning to include operational issues such as resource scheduling and treatment planning. This paper surveys several applications of Operations Research in the domain of Healthcare. In particular, the paper reviews key contributions addressing contemporary optimisation issues in this area. It highlights current research activities, focusing on a variety of optimisation problems as well as solution techniques used for solving the optimisation problems.

Abstract

Abstract
A topic of current interest in the analysis of sea-level states is to investigate the occurrence of future rare events which is essential for the prediction of flooding risks, coastal management and in the design of coastal defences and offshore structures. Nowadays, it is widely believed that the frequency of such rare events is increasing as a result of climatic and other changes, although they are hard to predict and their effects are, yet, poorly understood. Recent developments in multivariate statistical techniques for discrimination, clustering and dimension reduction for time series, have the potential to aid on the construction of new tools and models for forecasting the occurrence and impact of such future rare events. In studies of regional sea-level variability, tidal measurements are often analyzed individually for characterizing sea-level variability at each location. Marginal analysis, however, is in itself insufficient to come with an accurate description of regional sea-level variability. An alternative approach is to consider simultaneously the whole data set of sea-level records from a given region, and characterize regional variability in terms of locations exhibiting similar behavior through clustering techniques. Cluster analysis is a useful approach for characterizing regional variability of locations exhibiting similar behavior in terms of, for example, short-term or long-term predictions of extreme values. In this work, time series clustering is applied to the analysis of long tide gauge records from the Baltic Sea. In order to describe the regional variability of Baltic sea-level, tide gauge measurements are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-years return values. This is relevant for the design of marine systems and coastal structures, which requires a good knowledge of the most severe sea-level conditions that they need to withstand during their lifetime, and also for describing and understanding the variability of extreme sea heights in a climate change context.

Abstract
Purpose: The purpose of this paper is to compare the internet and annual reports as media of intellectual capital disclosure (ICD) and analyse its determinants. Design/methodology/approach: The paper examines ICD on the internet in 2009 and compares the internet and 2008 annual reports as disclosure media using content analysis. Non-parametric statistical methods are used to analyse some factors which influence disclosure. Findings: Portuguese companies attribute greater importance to the internet. The analysis showed that size is significant in explaining ICD only in the case of annual reports. The results also indicated that industrial affiliation is only partially a factor explaining ICD. Research limitations/implications: The sample is small. There may be content analysis issues associated with subjectivity in the coding process and the use of a limited content analysis method. Originality/value: The paper adds to the scarce research on ICD by Portuguese companies by providing new empirical data. This paper is one of the first to investigate ICD both in annual reports and on the internet. © Emerald Group Publishing Limited.

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.

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We present a novel dynamic graphical representation of the interactions between students and teachers in online forums available in Moodle. By defining the relationships between the users as a graph, it is possible to apply techniques of social network analysis. This system brings up new possibilities to e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation and to find the implicit relations between forums participants. © 2011 IADIS.