Abstract
This paper investigates the cyclical behaviour of the wine production in Douro region during the period 1932-2008. In general, wine production is characterised by large fluctuations which are composed of short-term and/or long-term cycles. The aim of this paper is twofold: firstly, we decompose the wine production's variance in order to find the dominating production cycles, i.e we try to explain whether wine production follows more long-term or short-term cycles. In the next step, we try to explain those cycles using a dependent variable, namely the medium spring temperature (Tm_Sp) for the period 1967-2008. We estimated a Time-Varying Autoregressive Model, which could explain 75% of the production that is characterised by 4.8- and 2.5-year cycles. We use the Short Time Fourier Transform to decompose the link between wine production and temperature. When the temperature was incorporated, the R (2) increased and the Akaike criterion value was lower. Hence, Tm_Sp causes a large amount of these cycles and the wine production variation reflects this relationship. In addition to an upward trend, there is a clearly identifiable cycle around the long-term trend in production. We also show how much of the production cycle and what cycle in particular is explained by the Tm_Sp. There is a stable but not constant link between production and the Tm_Sp. In particular, the temperature is responsible for 5.2- and 2.4-year cycles which has been happening since the 1980s. The Tm_Sp can also be used as an indicator for the 4.8- and 2.5-year cycles of production. The developed model suggests that stationarity is a questionable assumption, and this means that historical distributions of wine production are going to need dynamic updating.

Abstract
The challenge in developing agents for incomplete information games resides in the fact that the maximum utility decision for given information set is not always ascertainable. For large games like Poker, the agents' strategies require opponent modeling, since Nash equilibrium strategies are hard to compute. In light of this, simulation systems are indispensable for accurate assessment of agents' capabilities. Nevertheless, current systems do not accommodate the needs of computer poker research since they were designed mainly as an interface for human players competing against agents. In order to contribute towards improving computer poker research, a new simulation system was developed. This system introduces scientifically unexplored game modes with the purpose of providing a more realistic simulation environment, where the agent must play carefully to manage its initial resources. An evolutionary simulation feature was also included so as to provide support for the improvement of adaptive strategies. The simulator has built-in odds calculation, an agent development API, other platform agents and several variants support and an agent classifier with realistic game indicators including exploitability estimation. Tests and qualitative analysis have proven this simulator to be faster and better suited for thorough agent development and performance assessment. © 2013 Springer-Verlag.

Abstract
We propose a method for creating news context by taking advantage of a folksonomy of web clipping based on online news. We experiment with an ontology-based named entity recognition process and study two different ways of modeling the relationships induced by the coreference of named entities on news clips. We try to establish a context by identifying the community structure for a clip-centric network and for an entity-centric network, based on a small test set from the Breadcrumbs system. Finally, we compare both models, based on the detected news communities, and show the advantages of each network specification.

Abstract
Wireless Sensor Networks (WSNs) consist of small devices with processing, communication and sensing capabilities. These devices interact together to carryout monitoring tasks. An example of such network is a photo-voltaic (PV) power plant where each solar panel has a sensor. The number of interconnected solar panels can become very large, and spread over a large area. Each sensor will sense the output of the panel and send this value to a central node for processing. In this paper we evaluate the performance of a wireless sensor network employing three different data collecting techniques. The study considers different networks, each with a different number of nodes and with different values for the offered load, estimating for each network size and offered load, network throughput, packet loss and end-to-end packet delay. Results show that as the size of the network grows and for higher values of the offered load, the best performance is achieved by using a polling based data collecting technique. © 2012 IEEE.

Abstract
Health care systems around the world are under pressure, the costs are high and rising, and the population is growing and ageing. Health information technology is expected to help improving the health care processes capacity. The aim of this work is to analyze the benefits of the Theory of Constraints (TOC) buffer management implementation in the health care environment concerning the improvement in the patient flow and its management. A literature review was conducted, with an automated search on four databases to identify relevant published articles, written in English language between 2000 and 2010, about the TOC buffer management applied to the health care patient flow. Only three relevant articles were included. The analysis was based on the measurements of the implementations realized in seven different hospitals and for three different purposes: Accident & Emergency department (A&E), admissions and discharge. A statistical analysis conducted in the A&E and admissions post-implementation results demonstrated a significant improvement achieved. Four management control functions improvements were also obtained: prioritize, expedite, escalate and improve. Although few papers were available, TOC buffer management appears to be a good solution to improve performance and management in health care.

Abstract
The Artificial Immune Systems (AIS) constitute an emerging and very promising area of research that historically have been falling within two main theoretical immunological schools of thought: those based on Negative selection (NS) or those inspired on Danger theory (DT). Despite their inherent strengths and well known promising results, both deployed AIS have documented difficulties on dealing with gradual dynamic changes of self behavior through time. In this paper we propose and describe the development of an AIS framework for anomaly detection based on a rather different immunological theory, which is the Grossman's Tunable Activation Thresholds (TAT) theory for the behaviour of T-cells. The overall framework has been tested with artificially generated stochastic data sets based on a real world phenomena and the results thus obtained have been compared with a non-evolutionary Support Vector Machine (SVM) classifier, thus demonstrating TAT's performance and competitiveness for anomaly detection. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.