944 resultados para Hazard-Based Models


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Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination (R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes.

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Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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Population dynamics have been attracting interest since many years. Among the considered models, the Richards’ equations remain one of the most popular to describe biological growth processes. On the other hand, Allee effect is currently a major focus of ecological research, which occurs when positive density dependence dominates at low densities. In this chapter, we propose the dynamical study of classes of functions based on Richards’ models describing the existence or not of Allee effect. We investigate bifurcation structures in generalized Richards’ functions and we look for the conditions in the (β, r) parameter plane for the existence of a weak Allee effect region. We show that the existence of this region is related with the existence of a dovetail structure. When the Allee limit varies, the weak Allee effect region disappears when the dovetail structure also disappears. Consequently, we deduce the transition from the weak Allee effect to no Allee effect to this family of functions. To support our analysis, we present fold and flip bifurcation curves and numerical simulations of several bifurcation diagrams.

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Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

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Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

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Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Doutor em Engenharia Civil

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Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

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The aim is to examine the temporal trends of hip fracture incidence in Portugal by sex and age groups, and explore the relation with anti-osteoporotic medication. From the National Hospital Discharge Database, we selected from 1st January 2000 to 31st December 2008, 77,083 hospital admissions (77.4% women) caused by osteoporotic hip fractures (low energy, patients over 49 years-age), with diagnosis codes 820.x of ICD 9-CM. The 2001 Portuguese population was used as standard to calculate direct age-standardized incidence rates (ASIR) (100,000 inhabitants). Generalized additive and linear models were used to evaluate and quantify temporal trends of age specific rates (AR), by sex. We identified 2003 as a turning point in the trend of ASIR of hip fractures in women. After 2003, the ASIR in women decreased on average by 10.3 cases/100,000 inhabitants, 95% CI (− 15.7 to − 4.8), per 100,000 anti-osteoporotic medication packages sold. For women aged 65–69 and 75–79 we identified the same turning point. However, for women aged over 80, the year 2004 marked a change in the trend, from an increase to a decrease. Among the population aged 70–74 a linear decrease of incidence rate (95% CI) was observed in both sexes, higher for women: − 28.0% (− 36.2 to − 19.5) change vs − 18.8%, (− 32.6 to − 2.3). The abrupt turning point in the trend of ASIR of hip fractures in women is compatible with an intervention, such as a medication. The trends were different according to gender and age group, but compatible with the pattern of bisphosphonates sales.

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Over the last 30 years, a number of Vibrio species found in the aquatic environment have been indicated as cause of disease in human beings. Vibrio vulnificus is an emergent pathogen, an invasive and lethal marine bacterium related to wound infection and held accountable for gastroenteritis and primary septicemia. It occurs quite frequently in marine organisms, mainly in mollusks. This study aimed at isolating and identifying strains of V. vulnificus based upon the analysis of twenty samples of seabob shrimp, Xiphopenaeus kroyeri (Heller), purchased at the Mucuripe fish market (Fortaleza, Brazil). TCBS agar was used to isolate suspect strains. Seven of twenty-nine strains isolated from six different samples were confirmed as such by means of biochemical evidence and thus submitted to biological assays to determine their virulence. The susceptibility of the V. vulnificus strains to a number of antibiotics was tested. None of the V. vulnificus strains showed signs of virulence during a 24-hour observation period, possibly due to the shedding of the capsules by the cells. As to the results of the antimicrobial susceptibility tests, the seven above-mentioned V. vulnificus strains were found to be sensitive to nitrofurantoin (NT), ciprofloxacin (CIP), gentamicin (GN) and chloramphenicol (CO) and resistant to clindamycin (CI), penicillin (PN) and ampicillin (AP).

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Dissertação apresentada ao Instituto Superior de Contabilidade e Administração do Porto para obtenção do Grau de Mestre em Gestão das Organizações, Ramo Gestão de Empresas Orientador: Professor Doutor Eduardo Manuel Lopes de Sá e Silva Co-orientador: Mestre Maria de Fátima Mendes Monteiro

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Over the past decades several approaches for schedulability analysis have been proposed for both uni-processor and multi-processor real-time systems. Although different techniques are employed, very little has been put forward in using formal specifications, with the consequent possibility for mis-interpretations or ambiguities in the problem statement. Using a logic based approach to schedulability analysis in the design of hard real-time systems eases the synthesis of correct-by-construction procedures for both static and dynamic verification processes. In this paper we propose a novel approach to schedulability analysis based on a timed temporal logic with time durations. Our approach subsumes classical methods for uni-processor scheduling analysis over compositional resource models by providing the developer with counter-examples, and by ruling out schedules that cause unsafe violations on the system. We also provide an example showing the effectiveness of our proposal.

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While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model’s complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

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The excessive use of pesticides and fertilisers in agriculture has generated a decrease in groundwater and surface water quality in many regions of the EU, constituting a hazard for human health and the environment. Besides, on-site sewage disposal is an important source of groundwater contamination in urban and peri-urban areas. The assessment of groundwater vulnerability to contamination is an important tool to fulfil the demands of EU Directives. The purpose of this study is to assess the groundwater vulnerability to contamination related mainly to agricultural activities in a peri-urban area (Vila do Conde, NW Portugal). The hydrogeological framework is characterised mainly by fissured granitic basement and sedimentary cover. Water samples were collected and analysed for temperature, pH, electrical conductivity, chloride, phosphate, nitrate and nitrite. An evaluation of groundwater vulnerability to contamination was applied (GOD-S, Pesticide DRASTIC-Fm, SINTACS and SI) and the potential nitrate contamination risk was assessed, both on a hydrogeological GIS-based mapping. A principal component analysis was performed to characterised patterns of relationship among groundwater contamination, vulnerability, and the hydrogeological setting assessed. Levels of nitrate above legislation limits were detected in 75 % of the samples analysed. Alluvia units showed the highest nitrate concentrations and also the highest vulnerability and risk. Nitrate contamination is a serious problem affecting groundwater, particularly shallow aquifers, especially due to agriculture activities, livestock and cesspools. GIS-based cartography provided an accurate way to improve knowledge on water circulation models and global functioning of local aquifer systems. Finally, this study highlights the adequacy of an integrated approach, combining hydrogeochemical data, vulnerability assessments and multivariate analysis, to understand groundwater processes in peri-urban areas.

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This study focus on the probabilistic modelling of mechanical properties of prestressing strands based on data collected from tensile tests carried out in Laboratório Nacional de Engenharia Civil (LNEC), Portugal, for certification purposes, and covers a period of about 9 years of production. The strands studied were produced by six manufacturers from four countries, namely Portugal, Spain, Italy and Thailand. Variability of the most important mechanicalproperties is examined and the results are compared with the recommendations of the ProbabilisticModel Code, as well as the Eurocodes and earlier studies. The obtained results show a very low variability which, of course, benefits structural safety. Based on those results, probabilistic modelsfor the most important mechanical properties of prestressing strands are proposed.

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For efficient planning of waste collection routing, large municipalities may be partitioned into convenient sectors. The real case under consideration is the municipality of Monção, in Portugal. Waste collection involves more than 1600 containers over an area of 220 km2 and a population of around 20,000 inhabitants. This is mostly a rural area where the population is distributed in small villages around the 33 boroughs centres (freguesia) that constitute the municipality. In most freguesias, waste collection is usually conducted 3 times a week. However, there are situations in which the same collection is done every day. The case reveals some general and specific characteristics which are not rare, but are not widely addressed in the literature. Furthermore, new methods and models to deal with sectorization and routing are introduced, which can be extended to other applications. Sectorization and routing are tackled following a three-phase approach. The first phase, which is the main concern of the presentation, introduces a new method for sectorization inspired by Electromagnetism and Coulomb’s Law. The matter is not only about territorial division, but also the frequency of waste collection, which is a critical issue in these types of applications. Special characteristics related to the number and type of deposition points were also a motivation for this work. The second phase addresses the routing problems in each sector: new Mixed Capacitated Arc Routing with Limited Multi-Landfills models will be presented. The last phase integrates Sectoring and Routing. Computational results confirm the effectiveness of the entire novel approach.