Análise técnica - económica da produção de energia em sistemas de abastecimento de água: caso do aproveitamento de fins múltiplos dos socorridos

Dissertação de mestrado integrado em Engenharia Civil

Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.

The real economic effects of cross-delistingfrom the united states: evidence on post-operating performance, financial constraints and stock crash risk

Tese de Doutoramento em Ciências Empresariais.

Do market conditions affect gift exchange? Evidence from experimental markets with excess supply and excess demand

We study whether people's behavior in unbalanced gift exchange markets with repeated interaction are affected by whether they are on the excess supply side or the excess demand side of the market. Our analysis is based on the comparison of behavior between two types of experimental gift exchange markets, which vary only with respect to whether first or second movers are on the long side of the market. The direction of market imbalance could influence subjects' behavior, as second movers (workers) might react differently to favorable actions by first movers (firms) in the two cases. While our data show strong deviations from the standard game-theoretic prediction, we find mainly secondary treatment effects. Wage offers are not higher when there is an excess supply of firms, and workers do not respond more favorably to a given wage when there is an excess supply of labor. The state of competition does not appear to have strong effects in our data. We also present data from single-period sessions that show substantial gift exchange even without repeated interactions.

Demand elasticity and market power in the spanish electricity market

In this paper we check whether generator's bid behavior at the Spanish whosale electricity market is consistent with the hypothesis of profit maximization on their residual demands. Using OMEL data, we find the arc-elacticity of the residual demand around the system marginal price. The results suggest thet the larger firms are not actually profit-msximization. We argue how the regulatory environment may drive these results. Finally, we repeat the analysis for the first session of the intra-day market where presumably firms may not have the same incentives as in the day-ahead market.

Evaluating active labor markets in Romania

We evaluate the presence of effects from joining one of four active labour market programs in Romania in the late 1990s compared to the no-program state. Using rich follow-up survey data and propensity score matching, we find that three programs (training and retraining, self-employment assistance, and employment and relocation services) had success in improving participants' economic outcomes and were cost-beneficial from society's perspective. In contrast, public employment was found detrimental for the employment prospects of its participants.

Debt and deficit fluctuations and the structure of bond markets

We analyse the implications of optimal taxation for the stochastic behaviour of debt. We show that when a government pursues an optimal fiscal policy under complete markets, the value of debt has the same or less persistence than other variables in the economy and it declines in response to shocks that cause the deficit to increase. By contrast, under incomplete markets debt shows more persistence than other variables and it increases in response to shocks that cause a higher deficit. Data for US government debt reveals diametrically opposite results from those of complete markets and is much more supportive of bond market incompleteness.

Influenza surveillance using data from a telemedicine centre.

OBJECTIVES: This study aimed at investigating whether data from medical teleconsultations may contribute to influenza surveillance. METHODS: International Classification of Primary Care 2nd Edition (ICPC-2) codes were used to analyse the proportion of teleconsultations due to influenza-related symptoms. Results were compared with the weekly Swiss Sentinel reports. RESULTS: When using the ICPC-2 code for fever we could reproduce the seasonal influenza peaks of the winter seasons 07/08, 08/09 and 09/10 as depicted by the Sentinel data. For the pandemic influenza 09/10, we detected a much higher first peak in summer 2009 which correlated with a potential underreporting in the Sentinel system. CONCLUSIONS: ICPC-2 data from medical teleconsultations allows influenza surveillance in real time and correlates very well with the Swiss Sentinel system.

Three essays on the psychology of investment and financial markets

Préface My thesis consists of three essays where I consider equilibrium asset prices and investment strategies when the market is likely to experience crashes and possibly sharp windfalls. Although each part is written as an independent and self contained article, the papers share a common behavioral approach in representing investors preferences regarding to extremal returns. Investors utility is defined over their relative performance rather than over their final wealth position, a method first proposed by Markowitz (1952b) and by Kahneman and Tversky (1979), that I extend to incorporate preferences over extremal outcomes. With the failure of the traditional expected utility models in reproducing the observed stylized features of financial markets, the Prospect theory of Kahneman and Tversky (1979) offered the first significant alternative to the expected utility paradigm by considering that people focus on gains and losses rather than on final positions. Under this setting, Barberis, Huang, and Santos (2000) and McQueen and Vorkink (2004) were able to build a representative agent optimization model which solution reproduced some of the observed risk premium and excess volatility. The research in behavioral finance is relatively new and its potential still to explore. The three essays composing my thesis propose to use and extend this setting to study investors behavior and investment strategies in a market where crashes and sharp windfalls are likely to occur. In the first paper, the preferences of a representative agent, relative to time varying positive and negative extremal thresholds are modelled and estimated. A new utility function that conciliates between expected utility maximization and tail-related performance measures is proposed. The model estimation shows that the representative agent preferences reveals a significant level of crash aversion and lottery-pursuit. Assuming a single risky asset economy the proposed specification is able to reproduce some of the distributional features exhibited by financial return series. The second part proposes and illustrates a preference-based asset allocation model taking into account investors crash aversion. Using the skewed t distribution, optimal allocations are characterized as a resulting tradeoff between the distribution four moments. The specification highlights the preference for odd moments and the aversion for even moments. Qualitatively, optimal portfolios are analyzed in terms of firm characteristics and in a setting that reflects real-time asset allocation, a systematic over-performance is obtained compared to the aggregate stock market. Finally, in my third article, dynamic option-based investment strategies are derived and illustrated for investors presenting downside loss aversion. The problem is solved in closed form when the stock market exhibits stochastic volatility and jumps. The specification of downside loss averse utility functions allows corresponding terminal wealth profiles to be expressed as options on the stochastic discount factor contingent on the loss aversion level. Therefore dynamic strategies reduce to the replicating portfolio using exchange traded and well selected options, and the risky stock.

Deconstructing Shocks and Persistence in OECD Real Exchange Rates

This paper analyzes the persistence of shocks that affect the real exchange rates for a panel of seventeen OECD developed countries during the post-Bretton Woods era. The adoption of a panel data framework allows us to distinguish two different sources of shocks, i.e. the idiosyncratic and the common shocks, each of which may have di¤erent persistence patterns on the real exchange rates. We first investigate the stochastic properties of the panel data set using panel stationarity tests that simultaneously consider both the presence of cross-section dependence and multiple structural breaks that have not received much attention in previous persistence analyses. Empirical results indicate that real exchange rates are non-stationary when the analysis does not account for structural breaks, although this conclusion is reversed when they are modeled. Consequently, misspecification errors due to the non-consideration of structural breaks leads to upward biased shocks' persistence measures. The persistence measures for the idiosyncratic and common shocks have been estimated in this paper always turn out to be less than one year.

Recruitment and retention incentives in health labour markets: an analysis of participation in NHS Scotland following Dental Vocational

This paper uses a unique individual level administrative data set to analyse the participation of health professionals in the NHS after training. The data set contains information on over 1,000 dentists who received Dental Vocational Training in Scotland between 1995 and 2006. Using a dynamic nonlinear panel data model, we estimate the determinants of post-training participation. We nd there is signi cant persistence in these data and are able to show that the persistence arises from state dependence and individual heterogeneity. This finding has implications for the structure of policies designed to increase participation rates. We apply this empirical framework to assess the accuracy of predictions for workforce forecasting, and to provide a preliminary estimate of the impact of one of the recruitment and retention policies available to dentists in Scotland.

Common and Idiosyncratic Factors of the Exchange Risk Premium in Emerging European Markets

Existing empirical evidence suggests that the Uncovered Interest Rate Parity (UIRP) condition may not hold due to an exchange risk premium. For a panel data set of eleven emerging European economies we decompose this exchange risk premium into an idiosyncratic (country-specific) elements and a common factor using a principal components approach. We present evidence of a stationary idiosyncratic component and nonstationary common factor. This result leads to the conclusion of a nonstationary risk premium for these countries and a violation of the UIRP in the long-run, which is in contrast to previous studies often documenting a stationary premium in developed countries. Furthermore, we report that the variation in the premium is largely attributable to a common factor influenced by economic developments in the United States.