866 resultados para Robust Policymaking


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This article presents a research work, the goal of which was to achieve a model for the evaluation of data quality in institutional websites of health units in a broad and balanced way. We have carried out a literature review of the available approaches for the evaluation of website content quality, in order to identify the most recurrent dimensions and the attributes, and we have also carried out a Delphi method process with experts in order to reach an adequate set of attributes and their respective weights for the measurement of content quality. The results obtained revealed a high level of consensus among the experts who participated in the Delphi process. On the other hand, the different statistical analysis and techniques implemented are robust and attach confidence to our results and consequent model obtained.

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In daily cardiology practice, assessment of left ventricular (LV) global function using non-invasive imaging remains central for the diagnosis and follow-up of patients with cardiovascular diseases. Despite the different methodologies currently accessible for LV segmentation in cardiac magnetic resonance (CMR) images, a fast and complete LV delineation is still limitedly available for routine use. In this study, a localized anatomically constrained affine optical flow method is proposed for fast and automatic LV tracking throughout the full cardiac cycle in short-axis CMR images. Starting from an automatically delineated LV in the end-diastolic frame, the endocardial and epicardial boundaries are propagated by estimating the motion between adjacent cardiac phases using optical flow. In order to reduce the computational burden, the motion is only estimated in an anatomical region of interest around the tracked boundaries and subsequently integrated into a local affine motion model. Such localized estimation enables to capture complex motion patterns, while still being spatially consistent. The method was validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. The proposed approach proved to be robust and efficient, with an average distance error of 2.1 mm and a correlation with reference ejection fraction of 0.98 (1.9 ± 4.5%). Moreover, it showed to be fast, taking 5 seconds for the tracking of a full 4D dataset (30 ms per image). Overall, a novel fast, robust and accurate LV tracking methodology was proposed, enabling accurate assessment of relevant global function cardiac indices, such as volumes and ejection fraction.

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ABSTRACTStudies that measure the brand equity of destination brands by using the Customer-Based Brand Equity (CBBE) model in a developing country context are scarce. The present study investigates the destination brand equity of the Lahore Fort by employing the CBBE model in a developing country context of Pakistan. Following the positivist tradition, we adopted a survey-based approach to collect data from 237 tourists visiting the Lahore Fort. Data were collected through a questionnaire developed to explain the relationship of brand awareness, brand image, brand association, and brand loyalty with Lahore Fort’s overall brand equity. We used various robust statistical techniques such as correlation, regression and confirmatory factor analysis (using PLS method) to reach meaningful conclusions and found that brand image and brand associations positively contribute to brand loyalty. Furthermore, brand loyalty significantly contributes towards overall brand equity. Pragmatically, this study measures the customer based brand equity of the Lahore Fort, a destination brand. The results are useful as they suggest a few strategies that can help policy makers to enhance Lahore Fort’s brand performance.

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O trabalho apresentado por este documento aborda os problemas que advêm da necessidade de integração de aplicações, desenvolvidas em diferentes instantes no tempo, por diferentes equipas de trabalho, que para enriquecer os processos de negócio necessitam de comunicar entre si. A integração das aplicações tem de ser feita de forma opaca para estas, sendo disponibilizada por uma peça de software genérica, robusta e sem custos para as equipas desenvolvimento, na altura da integração. Esta integração tem de permitir que as aplicações comuniquem utilizando os protocolos que desejarem. Este trabalho propõe um middleware orientado a mensagens como solução para o problema identificado. A solução apresentada por este trabalho disponibiliza a comunicação entre aplicações que utilizam diferentes protocolos, permite ainda o desacoplamento temporal, espacial e de sincronismo na comunicação das aplicações. A implementação da solução tem base num sistema publish/subscribe orientado ao conteúdo e tem de lidar com as maiores exigências computacionais que este tipo de sistema acarta, sendo que a utilização deste se justifica com o enriquecimento da semântica de subscrição de eventos. Esta implementação utiliza uma arquitectura semi-distribuída, com o objectivo de aumentar a escalabilidade do sistema. A utilização da arquitectura semi-distribuída implica que a implementação da solução tem de lidar com o encaminhamento de eventos e divulgação das subscrições, pelos vários servidores de eventos. A implementação da solução disponibiliza garantias de persistência, processamento transaccional e tolerância a falhas, assim como transformação de eventos entre os diversos protocolos. A extensibilidade da solução é conseguida à custa de um sistema de pluggins que permite a adição de suporte a novos protocolos de comunicação. Os protocolos suportados pela implementação final do trabalho são RestMS e TCP.

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Tese de Doutoramento em Biologia apresentada à Faculdade de Ciências da Universidade do Porto, 2015.

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This paper analyzes the risk-return trade-off in European equities considering both temporal and cross-sectional dimensions. In our analysis, we introduce not only the market portfolio but also 15 industry portfolios comprising the entire market. Several bivariate GARCH models are estimated to obtain the covariance matrix between excess market returns and the industrial portfolios and the existence of a risk-return trade-off is analyzed through a cross-sectional approach using the information in all portfolios. It is obtained evidence for a positive and significant risk-return trade-off in the European market. This conclusion is robust for different GARCH specifications and is even more evident after controlling for the main financial crisis during the sample period.

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We investigate whether firms’ economic and financial situation influence the Quality of their Financial Reports (FRQ). FRQ is fundamental for investors and it affects the international capital movements [Bradshaw et al. (2004)] and Gelos and Wei (2005)]. Following Schipper and Vicent (2003) we use two issues to access earnings quality: abnormal accruals and earnings persistence. For seventeen European countries, we find evidence that the economic performance affects FRQ. Big firms and those with high current earnings exhibit better financial information. These results are robust since they don’t depend on FRQ proxy and we have the same evidence when we estimate regression with economical and financial factors separately or together. About financial situation, it seems not to affect FRQ. However, in high leveraged firms, the capital structure becomes determinant.

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We analyse whether the quality of firms’ Financial Reports (FRQ) produces any effect on their performance. Bradshaw et al. (2004) and Gelos and Wei (2005) call attention to the fact that the international capital movements is affected by FRQ. Following Schipper and Vicent (2003) we use the abnormal accruals to access earnings quality. For seventeen European countries, we found evidence that FRQ produces a positive impact on firm’s performance. This finding indicates that mangers are not opportunists and tends to make decisions to defend the firm’s best interests. This result is robust since it does not depend on the accounting firms’ performance proxy (ROA/ROE). In addition, it is also consistent when we use data in time series and in cross-sectional and when we estimate regression with lagged or the current year information about abnormal accruals.

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This paper presents a step-up micro-power converter for solar energy harvesting applications. The circuit uses a SC voltage tripler architecture, controlled by an MPPT circuit based on the Hill Climbing algorithm. This circuit was designed in a 0.13 mu m CMOS technology in order to work with an a-Si PV cell. The circuit has a local power supply voltage, created using a scaled down SC voltage tripler, controlled by the same MPPT circuit, to make the circuit robust to load and illumination variations. The SC circuits use a combination of PMOS and NMOS transistors to reduce the occupied area. A charge re-use scheme is used to compensate the large parasitic capacitors associated to the MOS transistors. The simulation results show that the circuit can deliver a power of 1266 mu W to the load using 1712 mu W of power from the PV cell, corresponding to an efficiency as high as 73.91%. The simulations also show that the circuit is capable of starting up with only 19% of the maximum illumination level.

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Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

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Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.

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Tese de Doutoramento, Matemática (Investigação Operacional), 23 de Setembro de 2006, Universidade dos Açores.

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27th Annual Conference of the European Cetacean Society. Setúbal, Portugal, 8-10 April 2013.

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Jornadas "Ciência nos Açores - que futuro?", Biblioteca Pública e Arquivo Regional de Ponta Delgada, Largo do Colégio, Ponta Delgada, 7-8 de junho.

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Dissertação para obtenção do Grau de Mestre em Auditoria Orientação científica do Professor Coordenador Rodrigo Mário Oliveira Carvalho