The influence of pre-departure training on expatriate adjustment: an empirical investigation with portuguese international assignees

This chapter examines the cross-cultural influence of training on the adjustment of international assignees. We focus on the pre-departure training (PDT) before an international assignment. It is an important topic because in the globalized world of today more and more expatriations are needed. The absence of PDT may generate the failure of the expatriation experience. Companies may neglect PDT due to cost reduction practices and ignorance of the need for it. Data were collected through semi-structured interviews to 42 Portuguese international assignees and 18 organizational representatives from nine Portuguese companies. The results suggest that companies should develop PDT programs, particularly when the cultural distance to the host country is bigger and when there is no previous experience of expatriation to that country in the company. The study is original because it details in depth the methods of PDT, its problems, and consequences. Some limitations linked to the research design and detailed in the conclusion should be overcome in future studies.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Depressão : detecção, diagnóstico e tratamento. Estudo de prevalência e despite das perturbações depressivas e tratamento

RESUMO:A depressão clínica é uma patologia do humor, dimensional e de natureza crónica, evoluindo por episódios heterogéneos remitentes e recorrentes, de gravidade variável, correspondendo a categorias nosológicas porventura artificiais mas clinicamente úteis, de elevada prevalência e responsável por morbilidade importante e custos sociais crescentes, calculando-se que em 2020 os episódios de depressão major constituirão, em todo o mundo, a segunda causa de anos de vida com saúde perdidos. Como desejável, na maioria dos países os cuidados de saúde primários são a porta de entrada para o acesso à recepção de cuidados de saúde. Cerca de 50% de todas as pessoas sofrendo de depressão acedem aos cuidados de saúde primários mas apenas uma pequena proporção é correctamente diagnosticada e tratada pelos médicos prestadores de cuidados primários apesar dos tratamentos disponíveis serem muito efectivos e de fácil aplicabilidade. A existência de dificuldades e barreiras a vários níveis – doença, doentes, médicos, organizações de saúde, cultura e sociedade – contribuem para esta generalizada ineficiência de que resulta uma manutenção do peso da depressão que não tem sido possível reduzir através das estratégias tradicionais de organização de serviços. A equipa comunitária de saúde mental e a psiquiatria de ligação são duas estratégias de intervenção com desenvolvimento conceptual e organizacional respectivamente na Psiquiatria Social e na Psicossomática. A primeira tem demonstrado sucesso na abordagem clínica das doenças mentais graves na comunidade e a segunda na abordagem das patologias não psicóticas no hospital geral. Todavia, a efectividade destas estratégias não se tem revelado transferível para o tratamento das perturbações depressivas e outras patologias mentais comuns nos cuidados de saúde primários. Novos modelos de ligação e de trabalho em equipa multidisciplinar têm sido demonstrados como mais eficazes e custo-efectivos na redução do peso da depressão, ao nível da prestação dos cuidados de saúde primários, quando são atinentes com os seguintes princípios estratégicos e organizacionais: detecção sistemática e abordagem da depressão segundo o modelo médico, gestão integrada de doença crónica incluindo a continuidade de cuidados mediante colaboração e partilha de responsabilidades intersectorial, e a aposta na melhoria contínua da qualidade. Em Portugal, não existem dados fiáveis sobre a frequência da depressão, seu reconhecimento e a adequação do tratamento ao nível dos cuidados de saúde primários nem se encontra validada uma metodologia de diagnóstico simples e fiável passível de implementação generalizada. Foi realizado um estudo descritivo transversal com os objectivos de estabelecer a prevalência pontual de depressão entre os utentes dos cuidados de saúde primários e as taxas de reconhecimento e tratamento pelos médicos de família e testar metodologias de despiste, com base num questionário de preenchimento rápido – o WHO-5 – associado a uma breve entrevista estruturada – o IED. Foram seleccionados aleatoriamente 31 médicos de família e avaliados 544 utentes consecutivos, dos 16 aos 90 anos, em quatro regiões de saúde e oito centros de saúde dotados com 219 clínicos gerais. Os doentes foram entrevistados por psiquiatras, utilizando um método padronizado, o SCAN, para diagnóstico de perturbação depressiva segundo os critérios da 10ª edição da Classificação Internacional de Doenças. Apurou-se que 24.8% dos utentes apresentava depressão. No melhor dos cenários, menos de metade destes doentes, 43%, foi correctamente identificada como deprimida pelo seu médico de família e menos de 13% dos doentes com depressão estavam bem medicados com antidepressivo em dose adequada. A aplicação seriada dos dois instrumentos não revelou dificuldades tendo permitido a identificação de pelo menos 8 em cada 10 doentes deprimidos e a exclusão de 9 em cada 10 doentes não deprimidos. Confirma-se a elevada prevalência da patologia depressiva ao nível dos cuidados primários em Portugal e a necessidade de melhorar a capacidade diagnóstica e terapêutica dos médicos de família. A intervenção de despiste, que foi validada, parece adequada para ser aplicada de modo sistemático em Centros de Saúde que disponham de recursos técnicos e organizacionais para o tratamento efectivo dos doentes com depressão. A obtenção da linha de base de indicadores de prevalência, reconhecimento e tratamento das perturbações depressivas nos cuidados de saúde primários, bem como a validação de instrumentos de uso clínico, viabiliza a capacitação do sistema para a produção de uma campanha nacional de educação de grande amplitude como a proposta no Plano Nacional de Saúde 2004-2010.------- ABSTRACT: Clinical depression is a dimensional and chronic affective disorder, evolving through remitting and recurring heterogeneous episodes with variable severity corresponding to clinically useful artificial diagnostic categories, highly prevalent and producing vast morbidity and growing social costs, being estimated that in 2020 unipolar major depression will be the second cause of healthy life years lost all over the world. In most countries, primary care are the entry point for access to health care. About 50% of all individuals suffering from depression within the community reach primary health care but a smaller proportion is correctly diagnosed and treated by primary care physicians though available treatments are effective and easily manageable. Barriers at various levels – pertaining to the illness itself, to patients, doctors, health care organizations, culture and society – contribute to the inefficiency of depression management and pervasiveness of depression burden, which has not been possible to reduce through classical service strategies. Community mental health teams and consultation-liaison psychiatry, two conceptual and organizational intervention strategies originating respectively within social psychiatry and psychosomatics, have succeeded in treating severe mental illness in community and managing non-psychotic disorders in the general hospital. However, these strategies effectiveness has not been replicated and transferable for the primary health care setting treatment of depressive disorders and other common mental pathology. New modified liaison and multidisciplinary team work models have been shown as more efficacious and cost-effective reducing depression burden at the primary care level namely when in agreement with principles such as: systematic detection of depression and approach accordingly to the medical model, chronic llness comprehensive management including continuity of care through collaboration and shared responsibilities between primary and specialized care, and continuous quality improvement. There are no well-founded data available in Portugal for depression prevalence, recognition and treatment adequacy in the primary care setting neither is validated a simple, teachable and implementable recognition and diagnostic methodology for primary care. With these objectives in mind, a cross-sectional descriptive study was performed involving 544 consecutive patients, aged 16-90 years, recruited from the ambulatory of 31 family doctors randomized within the 219 physicians working in eight health centres from four health regions. Screening strategies were tested based on the WHO-5 questionnaire in association with a short structured interview based on ICD-10 criteria. Depression ICD-10 diagnosis was reached according to the gold standard SCAN interview performed by trained psychiatrists. Any depressive disorder ICD-10 diagnosis was present in 24.8% of patients. Through the use of favourable recognition criteria, 43% of the patients were correctly identified as depressed by their family doctor and about 13% of the depressed patients were prescribed antidepressants at an adequate dosage. The serial administration of both instruments – WHO-5 and short structured interview – was feasible, allowing the detection of eight in ten positive cases and the exclusion of nine in ten non-cases. In Portugal, at the primary care level, high depressive disorder prevalence is confirmed as well as the need to improve depression diagnostic and treatment competencies of family doctors. A two-stage screening strategy has been validated and seems adequate for systematic use in health centres where technical and organizational resources for the effective management of depression are made available. These results can be viewed as primary care depressive disorders baseline indicators of prevalence, detection and treatment and, along with clinical useful instruments, the health system is more capacitated for the establishment of a national level large education campaign on depression such as proposed in the National Health Plan 2004-2010.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Data Extraction Tool to Analyse, Transform and Store Real Data from Electricity Markets

The study of electricity markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring process produced. Currently, lots of information concerning electricity markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge to define realistic scenarios, which are essential for understanding and forecast electricity markets behavior. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of electricity markets and of the behaviour of the involved entities. In this paper an adaptable tool capable of downloading, parsing and storing data from market operators’ websites is presented, assuring constant updating and reliability of the stored data.

Automatic Electricity Markets Data Extraction for Realistic Multi-agent Simulations

Electricity markets worldwide suffered profound transformations. The privatization of previously nationally owned systems; the deregulation of privately owned systems that were regulated; and the strong interconnection of national systems, are some examples of such transformations [1, 2]. In general, competitive environments, as is the case of electricity markets, require good decision-support tools to assist players in their decisions. Relevant research is being undertaken in this field, namely concerning player modeling and simulation, strategic bidding and decision-support.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Towards a unified European electricity market: The contribution of data-mining to support realistic simulation studies

Worldwide electricity markets have been evolving into regional and even continental scales. The aim at an efficient use of renewable based generation in places where it exceeds the local needs is one of the main reasons. A reference case of this evolution is the European Electricity Market, where countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. The continuous transformations electricity markets have been experiencing over the years create the need to use simulation platforms to support operators, regulators, and involved players for understanding and dealing with this complex environment. This paper focuses on demonstrating the advantage that real electricity markets data has for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations will bring to the participant countries. A case study using MASCEM (Multi-Agent System for Competitive Electricity Markets) is presented, with a scenario based on real data, simulating the European Electricity Market environment, and comparing its performance when using several different market mechanisms.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Análise dinâmica de composições musicais

A composição musical é um tema de muito interesse para a computação evolucionária dentro da área da inteligência artificial. É uma área que tem sofrido vários desenvolvimentos ao longo dos últimos anos pois o interesse em que hajam computadores que façam obras musicais é deveras aliciante. Este trabalho tem por objectivo realizar mais um passo nesse sentido. Assim, foi desenvolvida uma aplicação informática que realiza composições musicais de dois géneros distintos: Músicas Infantis e Músicas Blues. A aplicação foi implementada com recurso aos Algoritmos Genéticos, que são os algoritmos evolucionários mais populares da área da computação evolucionária. O trabalho foi estruturado em duas fases de desenvolvimento. Na primeira fase, realizou-se um levantamento estatístico sobre as características específicas de cada um dos géneros musicais. Analisaram-se quinze músicas de cada género musical, com o intuito de se chegar a uma proporção do uso que cada nota tem em cada um dos casos. Na segunda fase, desenvolveu-se o software que compõe as músicas com implementação de um algoritmo genético. Além disso, foi também desenvolvida uma interface gráfica que permite ao utilizador a escolha do género musical que pretende compor. O algoritmo genético começa por gerar uma população inicial de potenciais soluções de acordo com a escolha do utilizador, realizando, de seguida, o ciclo que caracteriza o algoritmo genético. A população inicial é constituída por soluções que seguem as regras que foram implementadas de acordo com os dados recolhidos ao longo da primeira fase. Foi também implementada uma interface de avaliação, através da qual, o utilizador pode ouvir cada uma das músicas para posterior avaliação em termos de fitness. O estado de evolução do algoritmo é apresentado, numa segunda interface, a qual facilita a clareza e justiça na avaliação ao longo de todo o processo. Esta última apresenta informação sobre a média das fitness da geração anterior e actual, sendo assim possível ter uma noção da evolução do algoritmo, no sentido de se obterem resultados satisfatórios no que diz respeito às composições musicais.

Improving discovery in the life sciences using semantic Web technologies and linked data: design principles for life sciences knowledge organization systems

Dissertation presented to obtain the Ph.D degree in Bioinformatics