Abstract
This paper describes a methodology for the application of hierarchical clustering methods to the task of outlier detection. The methodology is tested on the problem of cleaning Official Statistics data. The goal is to detect erroneous foreign trade transactions in data collected by the Portuguese Institute of Statistics (INE). These transactions are a minority, but still they have an important impact on the statistics produced by the institute. The detectiong of these rare errors is a manual, time-consuming task. This type of tasks is usually constrained by a limited amount of available resources. Our proposal addresses this issue by producing a ranking of outlyingness that allows a better management of the available resources by allocating them to the cases which are most different from the other and, thus, have a higher probability of being errors. Our method is based on the output of standard agglomerative hierarchical clustering algorithms, resulting in no significant additional computational costs. Our results show that it enables large savings by selecting a small subset of suspicious transactions for manual inspection, which, nevertheless, includes most of the erroneous transactions. In this study we compare our proposal to a state of the art outlier ranking method (LOF) and show that our method achieves better results on this particular application. The results of our experiments are also competitive with previous results on the same data. Finally, the outcome of our experiments raises important questions concerning the method currently followed at INE concerning items with small number of transactions.

Abstract
This paper reports new results in adopting information theoretic learning concepts in the training of neural networks to perform wind power forecasts. The forecast "goodness" is discussed under two paradigms: one is only concerned in measuring the deviation between the forecasted and realized values, the other is related with the value of the forecast in the electricity market for different agents. The results and conclusions are supported by a real case example.

Abstract
In this paper, we propose the VOA algorithm (Variable Offset Algorithm) to deal with the optimization of communication efficiency in dense WSN s with star topologies. The use of the VOA algorithm has been assessed by means of an experimental setup. The results highlight that the use of the VOA algorithm, implemented as a light middleware at the application layer, clearly enhances the communication efficiency in a WSN with star topology. © 2010 IEEE.

Abstract
This paper describes a method for the assessment of retail store performance based on Data Envelopment Analysis (DEA). The assessment considers the stores as complex organizations that aggregate several subunits, corresponding to sections with management autonomy. This structure motivated an analysis at two different levels: the section level and the store level. The performance assessment of the sections envolves a comparison among similar sections located in different stores, and evaluates efficiency spread. This is followed by an analysis at the store level to define targets for the sections. This analysis takes into account the interdependencies of the sections composing a store, as they share limited resources such as the floor area. This is achieved using a Network DEA model, which determines the maximum store sales allowing for reallocations of area among the sections within a store. The method developed is illustrated using a case study consisting of a Portuguese chain of supermarkets.

Abstract
This article presents the characterization of a shock absorber embedded sensor (SAES) for real-time monitoring of the condition of vehicle shock absorbers in everyday use. A prototype system was built using a custom designed monolithic silicon combined accelerometer, pressure and temperature sensors. The characterization of the SAES was performed and the obtained results meet and even outperform the specification requirements. The SAES was installed in a shock absorber, with adjustable dampling properties, and submitted to road tests. Results show that the condition of a shock absorber can be effectively assessed with the presented SAES. Ensuring that shock absorbers are replaced before reach unacceptable condition, this system will increase onboard comfort and vehicle safety. (C) 2010 Published by Elsevier Ltd.

Abstract