The Impact of Bilingualism on Acquiring Second Language Vocabulary: A Case Study

Trabalho de Projecto apresentado para cumprimento dos requisitos necessários à obtençao do grau de Mestre em Didáctica do Inglês,

Data fusion with computational intelligence techniques: a case study of fuzzy inference for terrain assessment

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Implementação de uma solução open source de Business Intelligence com metodologia Ágil

A importância dos sistemas de data warehousing e business intelligence é cada vez mais pronunciada, no sentido de dotar as organizações com a capacidade de guardar, explorar e produzir informação de valor acrescido para os seus processos de tomada de decisão. Esta realidade é claramente aplicável aos sectores da administração pública portuguesa e, muito em particular, aos organismos com responsabilidades centrais no Ministério da Saúde. No caso dos Serviços Partilhados do Ministério da Saúde (SPMS), que tem como missão prover o SNS de sistemas centrais de business intelligence, o apelo dos seus clientes, para que possam contar com capacidades analíticas nos seus sistemas centrais, tem sido sentido de forma muito acentuada. Todavia, é notório que, tanto os custos, como a complexidade, de grande parte destes projetos têm representado uma séria ameaça à sua adoção e sucesso. Por um lado, a administração pública tem recebido um forte encorajamento para integrar e adotar soluções de natureza open source (modelo de licenciamento gratuito), para os seus projetos de sistemas de informação. Por outro lado, temos vindo a assistir a uma vaga de aceitação generalizada de novas metodologias de desenvolvimento de projetos informáticos, nomeadamente no que diz respeito às metodologias Agéis, que se assumem como mais flexíveis, menos formais e com maior grau de sucesso. No sentido de averiguar da aplicabilidade do open source e das metodologias Ágeis aos sistemas de business intelligence, este trabalho documenta a implementação de um projeto organizacional para a SPMS, com recurso a ferramentas open source de licenciamento gratuito e através de uma metodologia de desenvolvimento de natureza Ágil.

Business Intelligence para apoio à gestão das listas de inscritos para cirurgia em Portugal continental

Em Portugal Continental a problemática das listas de inscritos para cirurgia e os seus tempos de espera são matérias que preocupam a sociedade portuguesa desde o início da década de noventa, do século XX. Atualmente as ferramentas de business intelligence ganham cada vez maior importância nas organizações inseridas num contexto mais complexo, competitivo e que exige respostas rápidas, adequadas e em constante mudança. O projeto desenvolvido consiste na implementação de uma aplicação de business intelligence, na Unidade Central de Gestão de Inscritos para Cirurgia, sedeada na Administração Central do Sistema de Saúde, I.P., que apoie a gestão das listas de inscritos para cirurgia de forma mais atempada, com maior qualidade e rigor, e com benefícios inquestionáveis para os utentes. Este projeto visa a monitorização de indicadores basilares; melhoria do controlo do desempenho dos hospitais; comparação entre os valores estabelecidos para determinados indicadores e os desvios verificados; simulação do impacto de algumas medidas, na lista de inscritos para cirurgia, antes da sua implementação; e facultar informação que permita adequar, a todo o momento, a oferta à procura, em determinadas patologias cirúrgicas. Os objetivos do projeto, definidos à priori, foram concretizados na sua totalidade, tendo sido a aplicação concluída com sucesso. Sugere-se, como ações futuras, acrescer novos indicadores e mais dimensões de análise à aplicação desenvolvida no âmbito deste projeto, alargando a capacidade de análise da Unidade Central de Gestão de Inscritos para Cirurgia, com inerente aumento da sua competência de gestão da Lista de Inscritos para Cirurgia em Portugal Continental.

Evolution of coauthorship networks: worldwide scientific production on leishmaniasis

Introduction Collaboration is one of the defining features of contemporary scientific research, and it is particularly important with regard to neglected diseases that primarily affect developing countries. Methods The present study has identified publications on leishmaniasis in the Medline database from 1945 to 2010, analyzing them according to bibliometric indicators and statistics from social network analysis. Examining aspects such as scientific production, diachronic evolution, and collaboration and configuration of the research groups in the field, we have considered the different types of Leishmania studied and the institutional affiliation and nationality of the authors. Results Seven-hundred and thirty-five authors participate in 154 prominent research clusters or groups. Although the most predominant and consolidated collaborations are characterized by members from the same country studying the same type of Leishmania, there are also notable links between authors from different countries or who study different clinical strains of the disease. Brazil took the lead in this research, with numerous Brazilian researchers heading different clusters in the center of the collaboration network. Investigators from the USA, India, and European countries, such as France, Spain, the United Kingdom, and Italy, also stand out within the network. Conclusions Research should be fostered in countries such as Bangladesh, Nepal, Sudan, and Ethiopia, where there is a high prevalence of different forms of the disease but limited research development with reference authors integrated into the collaboration networks.

The role of the intelligence service in preventing and combating transnational terrorism

Outrora dominado por ameaças provenientes de Estados-nação, o cenário global actual, dominado por uma rápida mudança de poderes que nos apresenta uma interacção complexa entre múltiplos actores, onde inimigos desconhecidos, anteriormente bem identificados, é actualmente controlado por grupos terroristas bem preparados e bem organizados. Hezbollah é reconhecido como um dos grupos terroristas mais capazes, com uma extensa rede fora do Líbano dedicada a tráfico de droga, armas e seres humanos, tal como o branqueamento de capitais para financiar o terrorismo, representando um grande foco de instabilidade à segurança. Como instrumento de Estado, os serviços de informações detêm a capacidade de estar na linha da frente na prevenção e combate ao terrorismo. Todavia, para compreender este fenómeno é necessário analisar os actores desta ameaça. À luz desta conjuntura, esta dissertação está dividida em três capítulos principais que visam responder às seguintes questões fundamentais: O que é o terrorismo? Como opera um grupo terrorista transnacional? Será que os serviços de informações têm as ferramentas necessárias para prevenir e combater estas ameaças?

Bone turnover markers for early detection of fracture healing disturbances: A review of the scientific literature

Imaging techniques are the standard method for assessment of fracture healing processes. However, these methods are perhaps not entirely reliable for early detection of complications, the most frequent of these being delayed union and non-union. A prompt diagnosis of such disorders could prevent prolonged patient distress and disability. Efforts should be directed towards the development of new technologies for improving accuracy in diagnosing complications following bone fractures. The variation in the levels of bone turnover markers (BTMs) have been assessed with regard to there ability to predict impaired fracture healing at an early stage, nevertheless the conclusions of some studies are not consensual. In this article the authors have revised the potential of BTMs as early predictors of prognosis in adult patients presenting traumatic bone fractures but who did not suffer from osteopenia or postmenopausal osteoporosis. The available information from the different studies performed in this field was systematized in order to highlight the most promising BTMs for the assessment of fracture healing outcome.

RoboCup Soccer Leagues

RoboCup was created in 1996 by a group of Japanese, American, and European Artificial Intelligence and Robotics researchers with a formidable, visionary long-term challenge: “By 2050 a team of robot soccer players will beat the human World Cup champion team.” At that time, in the mid 90s, when there were very few effective mobile robots and the Honda P2 humanoid robot was presented to a stunning public for the first time also in 1996, the RoboCup challenge, set as an adversarial game between teams of autonomous robots, was fascinating and exciting. RoboCup enthusiastically and concretely introduced three robot soccer leagues, namely “Simulation,” “Small-Size,” and “Middle-Size,” as we explain below, and organized its first competitions at IJCAI’97 in Nagoya with a surprising number of 100 participants [RC97]. It was the beginning of what became a continously growing research community. RoboCup established itself as a structured organization (the RoboCup Federation www.RoboCup.org). RoboCup fosters annual competition events, where the scientific challenges faced by the researchers are addressed in a setting that is attractive also to the general public. and the RoboCup events are the ones most popular and attended in the research fields of AI and Robotics.RoboCup further includes a technical symposium with contributions relevant to the RoboCup competitions and beyond to the general AI and robotics.

[An editorial to] Recent Advances on Knowledge Discovery and Business Intelligence

Special issue guest editorial, June, 2015.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.

HEP-Frame: A software engineered framework to aid the development and efficient multicore execution of scientific code

This work was supported by FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by LIP (Laboratório de Instrumentação e Física Experimental de Partículas) and by Project Search-ON2 (NORTE-07-0162- FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational in- telligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two il- lustrative Traffic Engineering methods are described, allowing to attain routing con- figurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.