981 resultados para predictive modelling


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Hydraulic systems are dynamically susceptible in the presence of entrapped air pockets, leading to amplified transient reactions. In order to model the dynamic action of an entrapped air pocket in a confined system, a heuristic mathematical formulation based on a conceptual analogy to a mechanical spring-damper system is proposed. The formulation is based on the polytropic relationship of an ideal gas and includes an additional term, which encompasses the combined damping effects associated with the thermodynamic deviations from the theoretical transformation, as well as those arising from the transient vorticity developed in both fluid domains (air and water). These effects represent the key factors that account for flow energy dissipation and pressure damping. Model validation was completed via numerical simulation of experimental measurements.

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This paper describes the implementation of a distributed model predictive approach for automatic generation control. Performance results are discussed by comparing classical techniques (based on integral control) with model predictive control solutions (centralized and distributed) for different operational scenarios with two interconnected networks. These scenarios include variable load levels (ranging from a small to a large unbalance generated power to power consumption ratio) and simultaneously variable distance between the interconnected networks systems. For the two networks the paper also examines the impact of load variation in an island context (a network isolated from each other).

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Dissertação apresentada para obtenção do grau de Doutor em Bioquímica,especialidade Bioquímica-Física, pela Universidade Nova de Lisboa, Faculdade de Cincias e Tecnologia

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The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response.With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.

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Coal contains trace elements and naturally occurring radionuclides such as 40K, 232Th, 238U. When coal is burned, minerals, including most of the radionuclides, do not burn and concentrate in the ash several times in comparison with their content in coal. Usually, a small fraction of the fly ash produced (2-5%) is released into the atmosphere. The activities released depend on many factors (concentration in coal, ash content and inorganic matter of the coal, combustion temperature, ratio between bottom and fly ash, filtering system). Therefore, marked differences should be expected between the by-products produced and the amount of activity discharged (per unit of energy produced) from different coal-fired power plants. In fact, the effects of these releases on the environment due to ground deposition have been received some attention but the results from these studies are not unanimous and cannot be understood as a generic conclusion for all coal-fired power plants. In this study, the dispersion modelling of natural radionuclides was carried out to assess the impact of continuous atmospheric releases from a selected coal plant. The natural radioactivity of the coal and the fly ash were measured and the dispersion was modelled by a Gaussian plume estimating the activity concentration at different heights up to a distance of 20 km in several wind directions. External and internal doses (inhalation and ingestion) and the resulting risk were calculated for the population living within 20 km from the coal plant. In average, the effective dose is lower than the ICRP’s limit and the risk is lower than the U.S. EPA’s limit. Therefore, in this situation, the considered exposure does not pose any risk. However, when considering the dispersion in the prevailing wind direction, these values are significant due to an increase of 232Th and 226Ra concentrations in 75% and 44%, respectively.

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The aim of this work was to simulate the radionuclides dispersion in the surrounding area of a coal-fired power plant, operational during the last 25 years. The dispersion of natural radionuclides (236Ra, 232Th and 40K) was simulated by a Gaussian plume dispersion model with three different stability classes estimating the radionuclides concentration at ground level. Measurements of the environmen-tal activity concentrations were carried out by γ-spectrometry and compared with results from the air dispersion and deposition model which showed that the stabil-ity class D causes the dispersion to longer distances up to 20 km from the stacks.

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Several popular Ansatze of lepton mass matrices that contain texture zeros are confronted with current neutrino observational data. We perform a systematic chi(2) analysis in a wide class of schemes, considering arbitrary Hermitian charged-lepton mass matrices and symmetric mass matrices for Majorana neutrinos or Hermitian mass matrices for Dirac neutrinos. Our study reveals that several patterns are still consistent with all the observations at the 68.27% confidence level, while some others are disfavored or excluded by the experimental data. The well-known Frampton-Glashow-Marfatia two-zero textures, hybrid textures, and parallel structures (among others) are considered.

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OBJECTIVE: To evaluate the predictive value of genetic polymorphisms in the context of BCG immunotherapy outcome and create a predictive profile that may allow discriminating the risk of recurrence. MATERIAL AND METHODS: In a dataset of 204 patients treated with BCG, we evaluate 42 genetic polymorphisms in 38 genes involved in the BCG mechanism of action, using Sequenom MassARRAY technology. Stepwise multivariate Cox Regression was used for data mining. RESULTS: In agreement with previous studies we observed that gender, age, tumor multiplicity and treatment scheme were associated with BCG failure. Using stepwise multivariate Cox Regression analysis we propose the first predictive profile of BCG immunotherapy outcome and a risk score based on polymorphisms in immune system molecules (SNPs in TNFA-1031T/C (rs1799964), IL2RA rs2104286 T/C, IL17A-197G/A (rs2275913), IL17RA-809A/G (rs4819554), IL18R1 rs3771171 T/C, ICAM1 K469E (rs5498), FASL-844T/C (rs763110) and TRAILR1-397T/G (rs79037040) in association with clinicopathological variables. This risk score allows the categorization of patients into risk groups: patients within the Low Risk group have a 90% chance of successful treatment, whereas patients in the High Risk group present 75% chance of recurrence after BCG treatment. CONCLUSION: We have established the first predictive score of BCG immunotherapy outcome combining clinicopathological characteristics and a panel of genetic polymorphisms. Further studies using an independent cohort are warranted. Moreover, the inclusion of other biomarkers may help to improve the proposed model.

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This paper studies the dynamical properties of systems with backlash and impact phenomena. This type of non-linearity can be tackled in the perspective of the fractional calculus theory. Fractional and integer order models are compared and their influence upon the emerging dynamics is analysed. It is demonstrated that fractional models can memorize dynamical effects due to multiple micro-collisions.

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Railway vehicle homologation, with respect to running dynamics, is addressed via dedicated norms. The results required, such as, accelerations and/or wheel-rail contact forces, obtained from experimental tests or simulations, must be available. Multibody dynamics allows the modelling of railway vehicles and their representation in real operations conditions, being the realism of the multibody models greatly influenced by the modelling assumptions. In this paper, two alternative multibody models of the Light Rail Vehicle 2000 (LRV) are constructed and simulated in a realistic railway track scenarios. The vehicle-track interaction compatibility analysis consists of two stages: the use of the simplified method described in the norm "UIC 518-Testing and Approval of Railway Vehicles from the Point of View of their Dynamic Behaviour-Safety-Track Fatigue-Running Behaviour" for decision making; and, visualization inspection of the vehicle motion with respect to the track via dedicated tools for understanding the mechanisms involved.

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Mecânica Especialização em Concepção e Produção

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A correlation and predictive scheme for the viscosity and self-diffusivity of liquid dialkyl adipates is presented. The scheme is based on the kinetic theory for dense hard-sphere fluids, applied to the van der Waals model of a liquid to predict the transport properties. A "universal" curve for a dimensionless viscosity of dialkyl adipates was obtained using recently published experimental viscosity and density data of compressed liquid dimethyl (DMA), dipropyl (DPA), and dibutyl (DBA) adipates. The experimental data are described by the correlation scheme with a root-mean-square deviation of +/- 0.34 %. The parameters describing the temperature dependence of the characteristic volume, V-0, and the roughness parameter, R-eta, for each adipate are well correlated with one single molecular parameter. Recently published experimental self-diffusion coefficients of the same set of liquid dialkyl adipates at atmospheric pressure were correlated using the characteristic volumes obtained from the viscosity data. The roughness factors, R-D, are well correlated with the same single molecular parameter found for viscosity. The root-mean-square deviation of the data from the correlation is less than 1.07 %. Tests are presented in order to assess the capability of the correlation scheme to estimate the viscosity of compressed liquid diethyl adipate (DEA) in a range of temperatures and pressures by comparison with literature data and of its self-diffusivity at atmospheric pressure in a range of temperatures. It is noteworthy that no data for DEA were used to build the correlation scheme. The deviations encountered between predicted and experimental data for the viscosity and self-diffusivity do not exceed 2.0 % and 2.2 %, respectively, which are commensurate with the estimated experimental measurement uncertainty, in both cases.

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

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The most effective therapeutic option for managing nonmuscle invasive bladder cancer (NMIBC), over the last 30 years, consists of intravesical instillations with the attenuated strain Bacillus Calmette-Gu´erin (the BCG vaccine). This has been performed as an adjuvant therapeutic to transurethral resection of bladder tumour (TURBT) and mostly directed towards patients with highgrade tumours, T1 tumours, and in situ carcinomas. However, from 20% to 40% of the patients do not respond and frequently present tumour progression. Since BCG effectiveness is unpredictable, it is important to find consistent biomarkers that can aid either in the prediction of the outcome and/or side effects development. Accordingly, we conducted a systematic critical review to identify themost preeminent predictive molecular markers associated with BCG response. To the best of our knowledge, this is the first review exclusively focusing on predictive biomarkers for BCG treatment outcome. Using a specific query, 1324 abstracts were gathered, then inclusion/exclusion criteria were applied, and finally 87 manuscripts were included. Several molecules, including CD68 and genetic polymorphisms, have been identified as promising surrogate biomarkers. Combinatory analysis of the candidate predictive markers is a crucial step to create a predictive profile of treatment response.

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Dissertação para a obtenção de grau de doutor em Bioquímica pelo Instituto de Tecnologia Química e Biológica da Universidade Nova de Lisboa