Identification of tsunami-induced deposits using numerical modeling and rock magnetism techniques: Astudy case of the 1755 Lisbon tsunami in Algarve, Portugal (vol 182, pg 187, 2010)

Storm- and tsunami-deposits are generated by similar depositional mechanisms making their discrimination hard to establish using classic sedimentologic methods. Here we propose an original approach to identify tsunami-induced deposits by combining numerical simulation and rock magnetism. To test our method, we investigate the tsunami deposit of the Boca do Rio estuary generated by the 1755 earthquake in Lisbon which is well described in the literature. We first test the 1755 tsunami scenario using a numerical inundation model to provide physical parameters for the tsunami wave. Then we use concentration (MS. SIRM) and grain size (chi(ARM), ARM, B1/2, ARM/SIRM) sensitive magnetic proxies coupled with SEM microscopy to unravel the magnetic mineralogy of the tsunami-induced deposit and its associated depositional mechanisms. In order to study the connection between the tsunami deposit and the different sedimentologic units present in the estuary, magnetic data were processed by multivariate statistical analyses. Our numerical simulation show a large inundation of the estuary with flow depths varying from 0.5 to 6 m and run up of similar to 7 m. Magnetic data show a dominance of paramagnetic minerals (quartz) mixed with lesser amount of ferromagnetic minerals, namely titanomagnetite and titanohematite both of a detrital origin and reworked from the underlying units. Multivariate statistical analyses indicate a better connection between the tsunami-induced deposit and a mixture of Units C and D. All these results point to a scenario where the energy released by the tsunami wave was strong enough to overtop and erode important amount of sand from the littoral dune and mixed it with reworked materials from underlying layers at least 1 m in depth. The method tested here represents an original and promising tool to identify tsunami-induced deposits in similar embayed beach environments.

A influência da poesia no desenvolvimento da linguagem oral de uma aluna com Trissomia 21

O conceito de poesia é uma forma de arte e traduz-se como um espelho do pensamento do homem realizado na palavra oral e escrita. A poesia poderá ensinar o homem no estudo da palavra, através do seu significado nos valores denotativo e/ou conotativo. A aplicação destes conceitos faz sentido, a partir da ideia de que a palavra no contexto da poesia pode produzir emoções e sensações nas pessoas a quem ela é transmitida. A poesia tem funções multissensoriais que podem definir o input linguístico como forma de desenvolver a linguagem. Vários são os estudos que apontam para as principais competências deficitárias nos indivíduos com Trissomia 21, mas poucas são as investigações que se debruçam sobre a influência da poesia nas várias competências linguísticas. Uma perceção mais ampla e visionária da Arte na voz da poesia pela parte da escola, professores e, em particular, professores de Educação Especial permitirá adotar estratégias de intervenção inovadoras. Pretende este estudo investigar e analisar a forma como a poesia pode influenciar o desenvolvimento da linguagem oral numa aluna com Trissomia 21. Neste projeto, a partir da identificação do caso-problema e da constatação da ausência da poesia no currículo da aluna, procurou-se intervir, no sentido de melhorar as suas competências linguísticas, utilizando para tanto a poesia. A implementação do projeto aconteceu ao longo de seis meses durante quinze sessões de intervenção individuais. Os resultados do projeto mostram que, em todas as competências linguísticas, houve um processo evolutivo, sendo particularmente significativo o desenvolvimento da competência fonológica, aumento de vocabulário e uma maior noção da palavra em contexto. Estes dados levam-nos a crer que a utilização da poesia poderá, também, constituir uma forma de promover a socialização e a autonomia, revelando os efeitos colaterais que poderão decorrer deste tipo de intervenção. - Abstract The concept of poetry is a form of art, showing the man's thought held in the spoken and written word. Poetry can teach man in the study of the word, its meaning through denotative and/or connotative values. The application of these concepts makes sense, from the idea that the word in the context of poetry can stir emotions and feelings in the people to whom it is transmitted. Poetry has multisensory functions that can set the linguistic input as a way to develop language at phonological, lexical, semantics, pragmatic and morfosyntactic skills. There are several studies that point out to the key skills deficit in individuals with Trisomy 21, but there are few investigations that focus on the influence of poetry in various language skills. A broader perception and vision of poetry as art given by school, and teachers, and, particularly teachers of Special Education will allow a more effective intervention strategies. This study aims to investigate and analyze how poetry can influence the development of oral language in a student with Trisomy 21. In this project, we tried to intervene, improving the language skills of the student, using poetry from the identification case-problem and confirmation of the absence of poetry in her curriculum. The implementation of the project took place throughout six months for fifteen individual intervention sessions. The project results show that, in all language skills, there was a progressive process, being particularly significant the development of the phonological skills, increased vocabulary and a greater sense of the word into context. These data lead us to believe that the use of poetry can also be a way to promote socialization and autonomy, revealing the side effects that may result from this type of intervention.

A entrada na profissão do educador de infância: percursos singulares

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção do grau de Mestre em Ciências da Educação - Especialidade Supervisão em Educação

Towards an Academic Social Network for Bologna Process

The Bologna Process aimed to build a European Higher Education Area promoting student's mobility. The adoption of Bologna Declaration directives requires a self management distributed approach to deal with student's mobility, allowing frequent updates in institutions rules or legislation. This paper suggests a computational system architecture, which follows a social network design. A set of structured annotations is proposed in order to organize the user's information. For instance, when the user is a student its annotations are organized into an academic record. The academic record data is used to discover interests, namely mobility interests, among students that belongs the academic network. These ideas have been applied into a demonstrator that includes a mobility simulator to compare and show the student's academic evolution.

A student personal system for Bologna process mobility

The Bologna Process aimed to build a European Higher Education Area with the objective of promoting students mobility. The adoption of Bologna Declaration directives requires a decentralized approach that accelerates student's mobility, based on frequently updated legislation. This paper proposes a student personal system to manage student's academic information. This system is supported by a flexible model that integrates, for instance, knowledge about the student attended courses or about a course that the student wishes to apply. Essentially, this model holds a (i) Student's Academic Record with skills acquired in academic course units, professional experience or training and an (ii) Individual Studies Plan, which places the student in a particular (iii) Course Plan setting the curricular structure that the student wishes to apply.

Distributed process execution in collaborative networks

Processes are a central entity in enterprise collaboration. Collaborative processes need to be executed and coordinated in a distributed Computational platform where computers are connected through heterogeneous networks and systems. Life cycle management of such collaborative processes requires a framework able to handle their diversity based on different computational and communication requirements. This paper proposes a rational for such framework, points out key requirements and proposes it strategy for a supporting technological infrastructure. Beyond the portability of collaborative process definitions among different technological bindings, a framework to handle different life cycle phases of those definitions is presented and discussed. (c) 2007 Elsevier Ltd. All rights reserved.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Mould and yeast identification in archival settings: preliminary results on the use of traditional methods and molecular biology options in Portuguese archives

This project was developed to fully assess the indoor air quality in archives and libraries from a fungal flora point of view. It uses classical methodologies such as traditional culture media – for the viable fungi – and modern molecular biology protocols, especially relevant to assess the non-viable fraction of the biological contaminants. Denaturing high-performance liquid chromatography (DHPLC) has emerged as an alternative to denaturing gradient gel electrophoresis (DGGE) and has already been applied to the study of a few bacterial communities. We propose the application of DHPLC to the study of fungal colonization on paper-based archive materials. This technology allows for the identification of each component of a mixture of fungi based on their genetic variation. In a highly complex mixture of microbial DNA this method can be used simply to study the population dynamics, and it also allows for sample fraction collection, which can, in many cases, be immediately sequenced, circumventing the need for cloning. Some examples of the methodological application are shown. Also applied is fragment length analysis for the study of mixed Candida samples. Both of these methods can later be applied in various fields, such as clinical and sand sample analysis. So far, the environmental analyses have been extremely useful to determine potentially pathogenic/toxinogenic fungi such as Stachybotrys sp., Aspergillus niger, Aspergillus fumigatus, and Fusarium sp. This work will hopefully lead to more accurate evaluation of environmental conditions for both human health and the preservation of documents.

Identification of a 0.4 Kb deletion region in 10q26 associated with endometrial carcinoma

We have identified an allelic deletion common region in the q26 region of chromosome 10 in endometrial carcinomas, which has been reported previously as a potential target of genetic alterations related to this neoplasia. An allelotyping analysis of 19 pairs of tumoral and non-tumoral samples was accomplished using seven microsatellite polymorphic markers mapping in the 10q26 chromosomal region. Loss of heterozygosity for one or more loci was detected in 29% of the endometrial carcinoma samples. The observed pattern of loss enabled the identification of a 3.5 Mb common deleted region located between the D10S587 and D10S186 markers. An additional result from an endometrial sample with evidence of a RER phenotype may suggest a more centromeric region of loss within the above-mentioned interval. This 401.84 Kb interval flanked by the D10S587 and D10S216 markers may be a plausible location for a putative suppressor gene involved in early stage endometrial carcinogenesis.

A loucura e o cinema: representações cinematográficas de personalidade limite

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

Aplicação dos princípios da manutenção Lean na indústria farmacêutica

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Lubrificação de compressores que aspiram fluido frigorigéneo a temperaturas inferiores a -30° C

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Hyperspectral subspace identification

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

Signal Subspace Identification in Hyperspectral Linear Mixtures

Chpater in Book Proceedings with Peer Review Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II