Abstract
This paper describes an integrated application that automates the procedure for sea outfall discharges data acquisition with an Autonomous Underwater Vehicle (AUV). Since most applications for this type of technology are research related, the used software tends to be more technical, oriented for engineers. This fact, allied with the bad sea conditions usually encountered at the portuguese coast, cause the mission execution to be extremely difficult at times. Before starting operating the AUV, a wide range of operations must be completed: we need to get data to estimate plume position, calculate mission path, transfer the AUV and acoustic buoys to the water, test communications and configure a variety of systems. So clearly there is a need to develop an application that fully automates a monitoring mission, allowing the operator with little to no experience to conclude it efficiently. Ultimately, by automating the procedure, there is the possibility of expanding the use of AUV's across several fields of study since no prior knowledge about the its systems is required. In summary this guides the user through a series of tasks and provides visual and audio information.

Abstract
The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron's classical estimator was used the compute the experimental semivariogram, which was fitted to three theoretical models: spherical, exponential and Gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the nearby beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume's dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

Abstract
Business organizations must pay attention to interesting changes in customer behavior in order to anticipate their needs and act accordingly with appropriated business actions. Tracking customer's commercial paths through the products they are interested in is an essential technique to improve business and increase customer satisfaction. Data warehousing (DW) allows us to do so, giving the basic means to record every customer transaction based on the different business strategies established. Although managing such huge amounts of records may imply business advantage, its exploration, especially in a multi-dimensional space (MDS), is a nontrivial task. The more dimensions we want to explore, the more are the computational costs involved in multi-dimensional data analysis (MDA). To make MDA practical in real world business problems, DW researchers have been working on combining data cubing and mining techniques to detect interesting changes in MDS. Such changes can also be detected through gradient queries. While those studies have provided the basis for future research in MDA, just few of them points to preference query selection in MDS. Thus, not only the exploration of changes in MDS is an essential task, but also even more important is ranking most interesting gradients. In this chapter, the authors investigate how to mine and rank the most interesting changes in a MDS applying a TOP-K gradient strategy. Additionally, the authors also propose a gradient-based cubing method to evaluate interesting gradient regions in MDS. So, the challenge is to find maximum gradient regions (MGRs) that maximize the task of raking gradients in a MDS. The authors' evaluation study demonstrates that the proposed method presents a promising strategy for ranking gradients in MDS. © 2010, IGI Global.

Abstract

Abstract

Abstract
There is an increasing recognition that the competitive advantage of firms depends on their ability to create, transfer, utilize, develop and protect the Organisational knowledge assets, so it is logical to expect that more effective management of the creation and use of knowledge would accelerate a company's natural rate of learning, allow it to outpace competitors and create value for both customers and shareholders. The organisational learning can be seen as a combination of two phases, extraction and dissemination of management lessons, as we go from one project to another. The managers, executives and researchers in project management have to "learn how to learn", and maintain a consistent rate of improvement in learning processes. In a second phase of the organisational learning, it is necessary to learn how to code and disseminate knowledge for additional improvement of the organisation management. The research here reported is the first phase of a twofold objective it intends to identify the key elements that contribute to a learning environment and methodologies, processes and technologies that enhance the Organisational knowledge; and it intends to develop a corporative IT-tool, based on the conclusions and remarks obtained. It is our belief that the opinions gathered from people sensible to this topic, will allow us, among other things, to conclude some important aspects that will defiantly contribute for increasing the awareness given to knowledge acquisition and learning by the different team members.