Greek de Steven Berkoff (1980): L’arriscada conversió de l’Èdip rei de Sòfocles en una love story

[cat] ¿Podem convertir l’Èdip rei de Sòfocles en una història d’amor com ho fa Berkoff a Greek?. Tot i que alguns crítics llegeixen Greek només com un drama provocador que de cap manera intenta justificar l’incest, directors, actors i crítics resten finalment captivats per la impactant història d’amor entre Eddy i la seva esposa-mare. Això demostraria que l’adaptació de Berkoff, pensada per a il·lustrar la degradació social de la Gran Bretanya dels 80s, esdevé una proposta arriscada, car significa negar de fet la consciència tràgica dels homes i dones contemporanis. Tanmateix, si aquest és el cas, lectors i espectadors, a banda de l’indubtable plaer d’assistir a la representació de Greek, poden preguntar-se, fins i tot des d’una perspectiva no fonamentalista de la tradició clàssica, si té sentit inspirar-se en el text de Sòfocles, el qual mostra precisament la gran consciència tràgica dels grecs.

Control and femtosecond time-resolved imaging of torsion in a chiral molecule

We study how the combination of long and short laser pulses can be used to induce torsion in an axially chiral biphenyl derivative (3,5-difluoro-3 ,5 -dibromo-4 -cyanobiphenyl). A long, with respect to the molecular rotational periods, elliptically polarized laser pulse produces 3D alignment of the molecules, and a linearly polarized short pulse initiates torsion about the stereogenic axis. The torsional motion is monitored in real-time by measuring the dihedral angle using femtosecond time-resolved Coulomb explosion imaging. Within the first 4 picoseconds (ps), torsion occurs with a period of 1.25 ps and an amplitude of 3◦ in excellent agreement with theoretical calculations. At larger times, the quantum states of the molecules describing the torsional motion dephase and an almost isotropic distribution of the dihedral angle is measured.We demonstrate an original application of covariance analysis of two-dimensional ion images to reveal strong correlations between specific ejected ionic fragments from Coulomb explosion. This technique strengthens our interpretation of the experimental data

Numerical Investigation of Flow Past a Circular Cylinder and in a Staggered Tube Bundle Using Various Turbulence Models

Transitional flow past a three-dimensional circular cylinder is a widely studied phenomenon since this problem is of interest with respect to many technical applications. In the present work, the numerical simulation of flow past a circular cylinder, performed by using a commercial CFD code (ANSYS Fluent 12.1) with large eddy simulation (LES) and RANS (κ - ε and Shear-Stress Transport (SST) κ - ω! model) approaches. The turbulent flow for ReD = 1000 & 3900 is simulated to investigate the force coefficient, Strouhal number, flow separation angle, pressure distribution on cylinder and the complex three dimensional vortex shedding of the cylinder wake region. The numerical results extracted from these simulations have good agreement with the experimental data (Zdravkovich, 1997). Moreover, grid refinement and time-step influence have been examined. Numerical calculations of turbulent cross-flow in a staggered tube bundle continues to attract interest due to its importance in the engineering application as well as the fact that this complex flow represents a challenging problem for CFD. In the present work a time dependent simulation using κ – ε, κ - ω! and SST models are performed in two dimensional for a subcritical flow through a staggered tube bundle. The predicted turbulence statistics (mean and r.m.s velocities) have good agreement with the experimental data (S. Balabani, 1996). Turbulent quantities such as turbulent kinetic energy and dissipation rate are predicted using RANS models and compared with each other. The sensitivity of grid and time-step size have been analyzed. Model constants sensitivity study have been carried out by adopting κ – ε model. It has been observed that model constants are very sensitive to turbulence statistics and turbulent quantities.

A quantitative view on fuzzy numbers

Since its introduction, fuzzy set theory has become a useful tool in the mathematical modelling of problems in Operations Research and many other fields. The number of applications is growing continuously. In this thesis we investigate a special type of fuzzy set, namely fuzzy numbers. Fuzzy numbers (which will be considered in the thesis as possibility distributions) have been widely used in quantitative analysis in recent decades. In this work two measures of interactivity are defined for fuzzy numbers, the possibilistic correlation and correlation ratio. We focus on both the theoretical and practical applications of these new indices. The approach is based on the level-sets of the fuzzy numbers and on the concept of the joint distribution of marginal possibility distributions. The measures possess similar properties to the corresponding probabilistic correlation and correlation ratio. The connections to real life decision making problems are emphasized focusing on the financial applications. We extend the definitions of possibilistic mean value, variance, covariance and correlation to quasi fuzzy numbers and prove necessary and sufficient conditions for the finiteness of possibilistic mean value and variance. The connection between the concepts of probabilistic and possibilistic correlation is investigated using an exponential distribution. The use of fuzzy numbers in practical applications is demonstrated by the Fuzzy Pay-Off method. This model for real option valuation is based on findings from earlier real option valuation models. We illustrate the use of number of different types of fuzzy numbers and mean value concepts with the method and provide a real life application.

Perfomance evaluation of a direct injection engine using different blends of soybean (Glycine max) methyl biodiesel

Diesel fuel is used widely in Brazil and worldwide. On the other hand, the growing environmental awareness leads to a greater demand for renewable energy resources. Thus, this study aimed to evaluate the use of different blends of soybean (Glycine max) methyl biodiesel and diesel in an ignition compression engine with direct injection fuel. The tests were performed on an electric eddy current dynamometer, using the blends B10, B50 and B100, with 10; 50 e 100% of biodiesel, respectively, in comparison to the commercial diesel B5, with 5% of biodiesel added to the fossil diesel. The engine performance was analyzed trough the tractor power take off (PTO) for each fuel, and the best results obtained for the power and the specific fuel consumption, respectively, were: B5 (44.62 kW; 234.87 g kW-1 h-1); B10 (44.73 kW; 233.78 g kW-1 h-1); B50 (44.11 kW; 250.40 g kW-1 h-1) e B100 (43.40 kW; 263.63 g kW-1 h-1). The best performance occurred with the use of B5 and B10 fuel, without significant differences between these blends. The B100 fuel showed significant differences compared to the other fuels.

Bartlett-Lewis of rectangular pulse modified model: estimate of parameters for simulation of precipitation in sub-hourly duration

The Bartlett-Lewis Rectangular Pulse Modified (BLPRM) model simulates the precipitous slide in the hourly and sub-hourly and has six parameters for each of the twelve months of the year. This study aimed to evaluate the behavior of precipitation series in the duration of 15 min, obtained by simulation using the model BLPRM in situations: (a) where the parameters are estimated from a combination of statistics, creating five different sets; (b) suitability of the model to generate rain. To adjust the parameters were used rain gauge records of Pelotas/RS/Brazil, which statistics were estimated - mean, variance, covariance, autocorrelation coefficient of lag 1, the proportion of dry days in the period considered. The results showed that the parameters related to the time of onset of precipitation (λ) and intensities (μx) were the most stable and the most unstable were ν parameter, related to rain duration. The BLPRM model adequately represented the mean, variance, and proportion of the dry period of the series of precipitation lasting 15 min and, the time dependence of the heights of rain, represented autocorrelation coefficient of the first retardation was statistically less simulated series suitability for the duration of 15 min.

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Kirjallisuusarvostelu

Identification of Some Additional Loss Components in High-Power Low-Voltage Permanent Magnet Generators

Permanent magnet generators (PMG) represent the cutting edge technology in modern wind mills. The efficiency remains high (over 90%) at partial loads. To improve the machine efficiency even further, every aspect of machine losses has to be analyzed. Additional losses are often given as a certain percentage without providing any detailed information about the actual calculation process; meanwhile, there are many design-dependent losses that have an effect on the total amount of additional losses and that have to be taken into consideration. Additional losses are most often eddy current losses in different parts of the machine. These losses are usually difficult to calculate in the design process. In this doctoral thesis, some additional losses are identified and modeled. Further, suggestions on how to minimize the losses are given. Iron losses can differ significantly between the measured no-load values and the loss values under load. In addition, with embedded magnet rotors, the quadrature-axis armature reaction adds losses to the stator iron by manipulating the harmonic content of the flux. It was, therefore, re-evaluated that in salient pole machines, to minimize the losses and the loss difference between the no-load and load operation, the flux density has to be kept below 1.5 T in the stator yoke, which is the traditional guideline for machine designers. Eddy current losses may occur in the end-winding area and in the support structure of the machine, that is, in the finger plate and the clamping ring. With construction steel, these losses account for 0.08% of the input power of the machine. These losses can be reduced almost to zero by using nonmagnetic stainless steel. In addition, the machine housing may be subjected to eddy current losses if the flux density exceeds 1.5 T in the stator yoke. Winding losses can rise rapidly when high frequencies and 10–15 mm high conductors are used. In general, minimizing the winding losses is simple. For example, it can be done by dividing the conductor into transposed subconductors. However, this comes with the expense of an increase in the DC resistance. In the doctoral thesis, a new method is presented to minimize the winding losses by applying a litz wire with noninsulated strands. The construction is the same as in a normal litz wire but the insulation between the subconductors has been left out. The idea is that the connection is kept weak to prevent harmful eddy currents from flowing. Moreover, the analytical solution for calculating the AC resistance factor of the litz-wire is supplemented by including an end-winding resistance in the analytical solution. A simple measurement device is developed to measure the AC resistance in the windings. In the case of a litz-wire with originally noninsulated strands, vacuum pressure impregnation (VPI) is used to insulate the subconductors. In one of the two cases studied, the VPI affected the AC resistance factor, but in the other case, it did not have any effect. However, more research is needed to determine the effect of the VPI on litz-wire with noninsulated strands. An empirical model is developed to calculate the AC resistance factor of a single-layer formwound winding. The model includes the end-winding length and the number of strands and turns. The end winding includes the circulating current (eddy currents that are traveling through the whole winding between parallel strands) and the main current. The end-winding length also affects the total AC resistance factor.

Application of a non isotropic turbulence model to stable atmospheric flows and dispersion over 3D topography

A non isotropic turbulence model is extended and applied to three dimensional stably stratified flows and dispersion calculations. The model is derived from the algebraic stress model (including wall proximity effects), but it retains the simplicity of the "eddy viscosity" concept of first order models. The "modified k-epsilon" is implemented in a three dimensional numerical code. Once the flow is resolved, the predicted velocity and turbulence fields are interpolated into a second grid and used to solve the concentration equation. To evaluate the model, various steady state numerical solutions are compared with small scale dispersion experiments which were conducted at the wind tunnel of Mitsubishi Heavy Industries, in Japan. Stably stratified flows and plume dispersion over three distinct idealized complex topographies (flat and hilly terrain) are studied. Vertical profiles of velocity and pollutant concentration are shown and discussed. Also, comparisons are made against the results obtained with the standard k-epsilon model.

Is there correlation between the turbulent eddies size and mechanical hemolysis ?

Hemolytic profile of an artificial device chronically implanted in the cardiovascular system may represent the difference between the success and failure in its long-term performance. Last decades have witnessed efforts on the development of methods capable of predicting red blood cell damage in artificial organs. However, all of them have had limited success to predict hemolysis. The primary cause of this problem is that such models do not take into consideration structures of turbulent flow. The present paper demonstrates that microscopic measurable occurrences of the turbulent flow may be linked to red blood cell trauma. This study suggests that if the smallest turbulent eddies dimension is under 10 m m hemolysis is not dependent on the exposure time and the red blood cells damage depends only on the dissipation of the turbulent energy in the erythrocyte membrane. The analysis reported here opens the possibility of mapping the flow field in artificial assist devices based on the smallest eddy length scales. This is a promising new trend and should be considered in the designing requirements of the next generations of artificial organs.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Context-specific independence in graphical models

The theme of this thesis is context-speci c independence in graphical models. Considering a system of stochastic variables it is often the case that the variables are dependent of each other. This can, for instance, be seen by measuring the covariance between a pair of variables. Using graphical models, it is possible to visualize the dependence structure found in a set of stochastic variables. Using ordinary graphical models, such as Markov networks, Bayesian networks, and Gaussian graphical models, the type of dependencies that can be modeled is limited to marginal and conditional (in)dependencies. The models introduced in this thesis enable the graphical representation of context-speci c independencies, i.e. conditional independencies that hold only in a subset of the outcome space of the conditioning variables. In the articles included in this thesis, we introduce several types of graphical models that can represent context-speci c independencies. Models for both discrete variables and continuous variables are considered. A wide range of properties are examined for the introduced models, including identi ability, robustness, scoring, and optimization. In one article, a predictive classi er which utilizes context-speci c independence models is introduced. This classi er clearly demonstrates the potential bene ts of the introduced models. The purpose of the material included in the thesis prior to the articles is to provide the basic theory needed to understand the articles.

Restraint-induced hypoactivity in an elevated plus-maze

Rodents submitted to restraint stress show decreased activity in an elevated plus-maze (EPM) 24 h later. The objective of the present study was to determine if a certain amount of time is needed after stress for the development of these changes. We also wanted to verify if behavioral tolerance of repeated daily restraint would be detectable in this model. Male Wistar rats were restrained for 2 h and tested in the EPM 1, 2, 24 or 48 h later. Another group of animals was immobilized daily for 2 h for 7 days, being tested in the EPM 24 h after the last restraint period. Restraint induced a significant decrease in the percent of entries and time spent in the open arms, as well as a decrease in the number of enclosed arm entries. The significant effect in the number of entries and the percentage of time spent in the open arms disappeared when the data were submitted to analysis of covariance using the number of enclosed arm entries as a covariate. This suggests that the restraint-induced hypoactivity influences the measures of open arm exploration. The modifications of restraint-induced hypoactivity are evident 24 or 48 h, but not 1 or 2 h, after stress. In addition, rats stressed daily for seven days became tolerant to this effect.

Familial predisposition to hypertension and the association between urinary sodium excretion and blood pressure in a population-based sample of young adults

The reasons for the inconsistent association between salt consumption and blood pressure levels observed in within-society surveys are not known. A total of 157 normotensive subjects aged 18 to 35 years, selected at random in a cross-sectional population-based survey, answered a structured questionnaire. They were classified as strongly predisposed to hypertension when two or more first-degree relatives had a diagnosis of hypertension. Anthropometric parameters were obtained and sitting blood pressure was determined with aneroid sphygmomanometers. Sodium and potassium excretion was measured by flame spectrophotometry in an overnight urine sample. A positive correlation between blood pressure and urinary sodium excretion was detected only in the group of individuals strongly predisposed to hypertension, both for systolic blood pressure (r = 0.51, P<0.01) and diastolic blood pressure (r = 0.50, P<0.01). In a covariance analysis, after controlling for age, skin color and body mass index, individuals strongly predisposed to hypertension who excreted amounts of sodium above the median of the entire sample had higher systolic and diastolic blood pressure than subjects classified into the remaining conditions. The influence of familial predisposition to hypertension on the association between salt intake and blood pressure may be an additional explanation for the weak association between urinary sodium excretion and blood pressure observed in within-population studies, since it can influence the association between salt consumption and blood pressure in some but not all inhabitants.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.