Abstract

Abstract
Coastal zones are naturally dynamic and mobile systems exposed to natural factors (river flows, waves and storms) as well as human interventions that continuously reshape their morphology. Erosion phenomena related to extreme weather events and sediment scarcity are common, threatening buildings and infrastructures, as well as beaches, ecosystems and valuable wetland; conditions that pose challenges to coastal security and defence. Regular monitoring of coastal areas, assessment of their morphodynamics and identification of the processes influencing sediment transport are thus increasingly important for a better understanding of changes and evolutionary trends in coastal systems. This demands a multi-disciplinary approach involving researchers with expertise in coastal processes and state- of-the-art observation technologies. In this paper state-of-the-art surveying methods for an efficient quantification of changes in coastal environments are described and evaluated, and two NW-Portuguese case studies are presented. Survey methods included: topographic surveys based on terrestrial videogrammetric mobile mapping and aerial photogrammetry; sub-tidal bathymetry with sonar imagery using an Autonomous Surface Vehicle (ASV); as well as field observations, with sediment sampling and beach characterisation. In the first case study, erosion/accretion patterns in the Douro estuary sand spit were analysed, considering its breakwater, river flow, wave and wind effects. Prior to the construction of a detached breakwater, the spit's morphodynamics was related to extreme river flow events, wave and wind conditions; afterwards the spit stabilized its shape and increased its area and volume. In the second case study the coast of Vila Nova de Gaia was broadly analysed, including the shoreface, foreshore and dunes, the characterization of major features and a short-period analysis of installed dynamics. Results obtained from field data, topographical surveys and numerical wave models were combined for an erosion risk assessment, using a methodology specifically developed for the study area. Both monitoring programs achieved their proposed objectives and provided valuable information to the local authorities, as gathered and processed information constitutes a valuable database for coastal planning and for ICZM purposes. They demonstrate the potential of several approaches, supported by advanced technologies, for the study of complex coastal morphodynamic processes.

Abstract
Nowadays, manufacturing control system faces the challenge of featuring optimal and reactive mechanisms to respond to volatile environments. In automation domain, hybrid control architectures solve these requirements as it allows coupling predictive/proactive and reactive techniques in manufacturing operations. However, to include dynamic coupling features, it is necessary to characterize the possible new operating modes and visualize its potential when a switching is needed. This paper presents an approach to characterize the operating modes of dynamic hybrid control architectures to support the dynamic switching process. The results, obtained through a simulation in a multi agent platform of flexible manufacturing systems, showed the interest of our approach in terms of including the characterization of operating modes as decisional criteria towards a system switching. © Springer International Publishing Switzerland 2015.

Abstract
In the area of Information Retrieval, the task of automatic text summarization usually assumes a static underlying collection of documents, disregarding the temporal dimension of each document. However, in real world settings, collections and individual documents rarely stay unchanged over time. The World Wide Web is a prime example of a collection where information changes both frequently and significantly over time, with documents being added, modified or just deleted at different times. In this context, previous work addressing the summarization of web documents has simply discarded the dynamic nature of the web, considering only the latest published version of each individual document. This paper proposes and addresses a new challenge - the automatic summarization of changes in dynamic text collections. In standard text summarization, retrieval techniques present a summary to the user by capturing the major points expressed in the most recent version of an entire document in a condensed form. In this new task, the goal is to obtain a summary that describes the most significant changes made to a document during a given period. In other words, the idea is to have a summary of the revisions made to a document over a specific period of time. This paper proposes different approaches to generate summaries using extractive summarization techniques. First, individual terms are scored and then this information is used to rank and select sentences to produce the final summary. A system based on Latent Dirichlet Allocation model (LDA) is used to find the hidden topic structures of changes. The purpose of using the LDA model is to identify separate topics where the changed terms from each topic are likely to carry at least one significant change. The different approaches are then compared with the previous work in this area. A collection of articles from Wikipedia, including their revision history, is used to evaluate the proposed system. For each article, a temporal interval and a reference summary from the article's content are selected manually. The articles and intervals in which a significant event occurred are carefully selected. The summaries produced by each of the approaches are evaluated comparatively to the manual summaries using ROUGE metrics. It is observed that the approach using the LDA model outperforms all the other approaches. Statistical tests reveal that the differences in ROUGE scores for the LDA-based approach is statistically significant at 99% over baseline.

Abstract
Cyber-physical systems are an emergent paradigm to design complex, adaptive and smart systems, combining computational applications with physical hardware devices. Multi-agent systems play an important role in such systems to provide flexibility, robustness and adaptation, but their alignment will require the integration of agents with physical devices. This process is usually complex and time consuming due to the proprietary protocols provided by the hardware automation devices. This paper describes the deployment of an agent-based system in a small-scale flexible production system composed by a set of heterogeneous automation devices, such as programmable logic controllers and robots.

Abstract
Technologies for contextualization and Recognition of people and situations have made great progresses in the last years. Every time we use the Internet, pass by car in motorways, enter inside a commercial shopping, even when we go on street, there are in-build space mechanisms capable of getting our personal data video surveillance, our position through the tracking cell phones position or even when we post a message on some Social Network and we have GPS coordinates associated. Basically we share where we are, what we are doing and with time, we reveal our pattern behaviours. But what we get in return by sharing all this Information? Very few, but it could be a lot. That's the start point of this paper, that review the state of art of contextualization and recognition technologies, discuss is business and social potential and finishes with the presentation of an architecture for supporting a user-centered approach for the definition of a rules-engine capable of translating the users' interests and provide enterprises a source of useful information.