Metodologias estatísticas na análise de germinação de sementes de mamona

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Influência dos defeitos cristalinos nas propriedades dielétricas e não-ôhmicas do 'CA' 'CU IND.3' 'TI IND.4' 'O IND.12'(CCTO)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Thermal and structural study of glasses in the binary system TeO2-Pb(PO3)(2)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Atenção Psicossocial e Psicologia: um mapeamento da produção científica

By the end of the 1970´s, begins the Psychiatric Reform Movement, whose development was the beginning of the construction of a new model, here termed Psychosocial Attention, to substitute the traditional psychiatric model. As such, aspires to be a process of paradigm shift and, therefore, requires transformations in the fields: theoretical-conceptual, technical-assistance, legal-political e sociocultural. This qualitative study composes a research which sought to ascertain the scientific production on the topic conducted by psychology, from the implementation of the Brazilian National Health System and the creation of new services to mental health care. In this sense, it proposed to investigate how the papers published in journals of psychology found in the database LILACS, since 1990, contribute to the process of building a new model that actually replace the so-called traditional psychiatric model.

Women's subjective and objective health over time : the role of psychosocial conditions and physiological stress responses

Today, health problems are likely to have a complex and multifactorial etiology, whereby psychosocial factors interact with behaviour and bodily responses. Women generally report more health problems than men. The present thesis concerns the development of women’s health from a subjective and objective perspective, as related to psychosocial living conditions and physiological stress responses. Both cross-sectional and longitudinal studies were carried out on a representative sample of women. Data analysis was based on a holistic person-oriented approach as well as a variable approach. In Study I, the women’s self-reported symptoms and diseases as well as self-rated general health status were compared to physician-rated health problems and ratings of the general health of the women, based on medical examinations. The findings showed that physicians rated twice as many women as having poor health compared to the ratings of the women themselves. Moreover, the symptom ”a sense of powerlessness” had the highest predictive power for self-rated general health. Study II investigated individual and structural stability in symptom profiles between adolescence and middle-age as related to pubertal timing. There was individual stability in symptom reporting for nearly thirty years, although the effect of pubertal timing on symptom reporting did not extend into middle-age. Study III explored the longitudinal and current influence of socioeconomic and psychosocial factors on women’s self-reported health. Contemporary factors such as job strain, low income, financial worries, and double exposure in terms of high job strain and heavy domestic responsibilities increased the risk for poor self-reported health in middle-aged women. In Study IV, the association between self-reported symptoms and physiological stress responses was investigated. Results revealed that higher levels of medically unexplained symptoms were related to higher levels of cortisol, cholesterol, and heart rate. The empirical findings are discussed in relation to existing models of stress and health, such as the demand-control model, the allostatic load model, the biopsychosocial model, and the multiple role hypothesis. It was concluded that women’s health problems could be reduced if their overall life circumstances were improved. The practical implications of this might include a redesign of the labour market giving women more influence and control over their lives, both at and away from work.

Identity dynamics and the emergence of new organizational arrangements: A multi-level study

Organizational and institutional scholars have advocated the need to examine how processes originating at an individual level can change organizations or even create new organizational arrangements able to affect institutional dynamics (Chreim et al., 2007; Powell & Colyvas, 2008; Smets et al., 2012). Conversely, research on identity work has mainly investigated the different ways individuals can modify the boundaries of their work in actual occupations, thus paying particular attention to ‘internal’ self-crafting (e.g. Wrzesniewski & Dutton, 2001). Drawing from literatures on possible and alternative self and on positive organizational scholarship (e.g., Obodaru, 2012; Roberts & Dutton, 2009), my argument is that individuals’ identity work can go well beyond the boundaries of internal self-crafting to the creation of new organizational arrangements. In this contribution I analyze, through multiple case studies, healthcare professionals who spontaneously participated in the creation of new organizational arrangements, namely health structures called Community Hospitals. The contribution develops this form of identity work by building a grounded model. My findings disclose the process that leads from the search for the enactment of different self-concepts to positive identities, through the creation of a new organizational arrangement. I contend that this is a particularly complex form of collective identity work because it requires, to be successful, concerted actions of several internal, external and institutional actors, and it also requires balanced tensions that – at the same time - enable individuals’ aspirations and organizational equilibrium. I name this process organizational collective crafting. Moreover I inquire the role of context in supporting the triggering power of those unrealized selves. I contribute to the comprehension of the consequences of self-comparisons, organizational identity variance, and positive identity. The study bears important insights on how identity work originating from individuals can influence organizational outcomes and larger social systems.

Lava discharge rate estimates from thermal infrared satellite data at Pacaya Volcano, Guatemala

Time-averaged discharge rates (TADR) were calculated for five lava flows at Pacaya Volcano (Guatemala), using an adapted version of a previously developed satellite-based model. Imagery acquired during periods of effusive activity between the years 2000 and 2010 were obtained from two sensors of differing temporal and spatial resolutions; the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Geostationary Operational Environmental Satellites (GOES) Imager. A total of 2873 MODIS and 2642 GOES images were searched manually for volcanic “hot spots”. It was found that MODIS imagery, with superior spatial resolution, produced better results than GOES imagery, so only MODIS data were used for quantitative analyses. Spectral radiances were transformed into TADR via two methods; first, by best-fitting some of the parameters (i.e. density, vesicularity, crystal content, temperature change) of the TADR estimation model to match flow volumes previously estimated from ground surveys and aerial photographs, and second by measuring those parameters from lava samples to make independent estimates. A relatively stable relationship was defined using the second method, which suggests the possibility of estimating lava discharge rates in near-real-time during future volcanic crises at Pacaya.

Coupled human and natural system dynamics as key to the sustainability of Lake Victoria's ecosystem services

East Africa’s Lake Victoria provides resources and services to millions of people on the lake’s shores and abroad. In particular, the lake’s fisheries are an important source of protein, employment, and international economic connections for the whole region. Nonetheless, stock dynamics are poorly understood and currently unpredictable. Furthermore, fishery dynamics are intricately connected to other supporting services of the lake as well as to lakeshore societies and economies. Much research has been carried out piecemeal on different aspects of Lake Victoria’s system; e.g., societies, biodiversity, fisheries, and eutrophication. However, to disentangle drivers and dynamics of change in this complex system, we need to put these pieces together and analyze the system as a whole. We did so by first building a qualitative model of the lake’s social-ecological system. We then investigated the model system through a qualitative loop analysis, and finally examined effects of changes on the system state and structure. The model and its contextual analysis allowed us to investigate system-wide chain reactions resulting from disturbances. Importantly, we built a tool that can be used to analyze the cascading effects of management options and establish the requirements for their success. We found that high connectedness of the system at the exploitation level, through fisheries having multiple target stocks, can increase the stocks’ vulnerability to exploitation but reduce society’s vulnerability to variability in individual stocks. We describe how there are multiple pathways to any change in the system, which makes it difficult to identify the root cause of changes but also broadens the management toolkit. Also, we illustrate how nutrient enrichment is not a self-regulating process, and that explicit management is necessary to halt or reverse eutrophication. This model is simple and usable to assess system-wide effects of management policies, and can serve as a paving stone for future quantitative analyses of system dynamics at local scales.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Harmony: Towards automated self-adaptive consistency in cloud storage

In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consistency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid?5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80% while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

Geografía del carbono en alta resolución en bosque tropical amazónico del Ecuador mediante sensores aerotransportados

La estimación de la biomasa de la vegetación terrestre en bosque tropical no sólo es un área de investigación en rápida expansión, sino también es un tema de gran interés para reducir las emisiones de carbono asociadas a la deforestación y la degradación forestal (REDD+). Las estimaciones de densidad de carbono sobre el suelo (ACD) en base a inventarios de campo y datos provenientes de sensores aerotransportados, en especial con sensores LiDAR, han conducido a un progreso sustancial en el cartografiado a gran escala de las reservas de carbono forestal. Sin embargo, estos mapas de carbono tienen incertidumbres considerables, asociadas generalmente al proceso de calibración del modelo de regresión utilizado para producir los mapas. En esta tesis se establece una metodología para la calibración y validación de un modelo general de estimación de ACD usando LiDAR en un sector del Parque Nacional Yasuní en Ecuador. En el proceso de calibración del modelo se considera el tamaño y la ubicación de las parcelas, la influencia de la topografía y la distribución espacial de la biomasa. Para el análisis de los datos se utilizan técnicas geoestadísticas en combinación con variables geomorfométricas derivadas de datos LiDAR, y se propone un esquema de muestreo estratificado por posiciones topográficas (valle, ladera y cima). La validación del modelo general para toda la zona de estudio presentó valores de RMSE = 5.81 Mg C ha-1, R2 = 0.94 y sesgo = 0.59, mientras que, al considerar las posiciones topográficas, el modelo presentó valores de RMSE = 1.67 Mg C ha-1, R2 = 0.98 y sesgo = 0.23 para el valle; RMSE = 3.13 Mg C ha-1, R2 = 0.98 y sesgo = - 0.34 para la ladera; y RMSE = 2.33 Mg C ha-1, R2 = 0.97 y sesgo = 0.74 para la cima. Los resultados obtenidos demuestran que la metodología de muestreo estratificado por posiciones topográficas propuesto, permite calibrar de manera efectiva el modelo general con las estimaciones de ACD en campo, logrando reducir el RMSE y el sesgo. Los resultados muestran el potencial de los datos LiDAR para caracterizar la estructura vertical de la vegetación en un bosque altamente diverso, permitiendo realizar estimaciones precisas de ACD, y conocer patrones espaciales continuos de la distribución de la biomasa aérea y del contenido de carbono en la zona de estudio. ABSTRACT Estimating biomass of terrestrial vegetation in tropical forest is not only a rapidly expanding research area, but also a subject of tremendous interest for reducing carbon emissions associated with deforestation and forest degradation (REDD+). The aboveground carbon density estimates (ACD) based on field inventories and airborne sensors, especially LiDAR sensors have led to a substantial progress in large-scale mapping of forest carbon stocks. However, these carbon maps have considerable uncertainties generally associated with the calibration of the regression model used to produce these maps. This thesis establishes a methodology for calibrating and validating a general ACD estimation model using LiDAR in Ecuador´s Yasuní National Park. The size and location of the plots are considered in the model calibration phase as well as the influence of topography and spatial distribution of biomass. Geostatistical analysis techniques are used in combination with geomorphometrics variables derived from LiDAR data, and then a stratified sampling scheme considering topographic positions (valley, slope and ridge) is proposed. The validation of the general model for the study area showed values of RMSE = 5.81 Mg C ha-1, R2 = 0.94 and bias = 0.59, while considering the topographical positions, the model showed values of RMSE = 1.67 Mg C ha-1, R2 = 0.98 and bias = 0.23 for the valley; RMSE = 3.13 Mg C ha-1, R2 = 0.98 and bias = - 0.34 for the slope; and RMSE = 2.33 Mg C ha-1, R2 = 0.97 and bias = 0.74 for the ridge. The results show that the stratified sampling methodology taking into account topographic positions, effectively calibrates the general model with field estimates of ACD, reducing RMSE and bias. The results show the potential of LiDAR data to characterize the vertical structure of vegetation in a highly diverse forest, allowing accurate estimates of ACD, and knowing continuous spatial patterns of biomass distribution and carbon stocks in the study area.

Co-simulation Methodologies for Hybrid and Electric Vehicle Dynamics

In recent decades, full electric and hybrid electric vehicles have emerged as an alternative to conventional cars due to a range of factors, including environmental and economic aspects. These vehicles are the result of considerable efforts to seek ways of reducing the use of fossil fuel for vehicle propulsion. Sophisticated technologies such as hybrid and electric powertrains require careful study and optimization. Mathematical models play a key role at this point. Currently, many advanced mathematical analysis tools, as well as computer applications have been built for vehicle simulation purposes. Given the great interest of hybrid and electric powertrains, along with the increasing importance of reliable computer-based models, the author decided to integrate both aspects in the research purpose of this work. Furthermore, this is one of the first final degree projects held at the ETSII (Higher Technical School of Industrial Engineers) that covers the study of hybrid and electric propulsion systems. The present project is based on MBS3D 2.0, a specialized software for the dynamic simulation of multibody systems developed at the UPM Institute of Automobile Research (INSIA). Automobiles are a clear example of complex multibody systems, which are present in nearly every field of engineering. The work presented here benefits from the availability of MBS3D software. This program has proven to be a very efficient tool, with a highly developed underlying mathematical formulation. On this basis, the focus of this project is the extension of MBS3D features in order to be able to perform dynamic simulations of hybrid and electric vehicle models. This requires the joint simulation of the mechanical model of the vehicle, together with the model of the hybrid or electric powertrain. These sub-models belong to completely different physical domains. In fact the powertrain consists of energy storage systems, electrical machines and power electronics, connected to purely mechanical components (wheels, suspension, transmission, clutch…). The challenge today is to create a global vehicle model that is valid for computer simulation. Therefore, the main goal of this project is to apply co-simulation methodologies to a comprehensive model of an electric vehicle, where sub-models from different areas of engineering are coupled. The created electric vehicle (EV) model consists of a separately excited DC electric motor, a Li-ion battery pack, a DC/DC chopper converter and a multibody vehicle model. Co-simulation techniques allow car designers to simulate complex vehicle architectures and behaviors, which are usually difficult to implement in a real environment due to safety and/or economic reasons. In addition, multi-domain computational models help to detect the effects of different driving patterns and parameters and improve the models in a fast and effective way. Automotive designers can greatly benefit from a multidisciplinary approach of new hybrid and electric vehicles. In this case, the global electric vehicle model includes an electrical subsystem and a mechanical subsystem. The electrical subsystem consists of three basic components: electric motor, battery pack and power converter. A modular representation is used for building the dynamic model of the vehicle drivetrain. This means that every component of the drivetrain (submodule) is modeled separately and has its own general dynamic model, with clearly defined inputs and outputs. Then, all the particular submodules are assembled according to the drivetrain configuration and, in this way, the power flow across the components is completely determined. Dynamic models of electrical components are often based on equivalent circuits, where Kirchhoff’s voltage and current laws are applied to draw the algebraic and differential equations. Here, Randles circuit is used for dynamic modeling of the battery and the electric motor is modeled through the analysis of the equivalent circuit of a separately excited DC motor, where the power converter is included. The mechanical subsystem is defined by MBS3D equations. These equations consider the position, velocity and acceleration of all the bodies comprising the vehicle multibody system. MBS3D 2.0 is entirely written in MATLAB and the structure of the program has been thoroughly studied and understood by the author. MBS3D software is adapted according to the requirements of the applied co-simulation method. Some of the core functions are modified, such as integrator and graphics, and several auxiliary functions are added in order to compute the mathematical model of the electrical components. By coupling and co-simulating both subsystems, it is possible to evaluate the dynamic interaction among all the components of the drivetrain. ‘Tight-coupling’ method is used to cosimulate the sub-models. This approach integrates all subsystems simultaneously and the results of the integration are exchanged by function-call. This means that the integration is done jointly for the mechanical and the electrical subsystem, under a single integrator and then, the speed of integration is determined by the slower subsystem. Simulations are then used to show the performance of the developed EV model. However, this project focuses more on the validation of the computational and mathematical tool for electric and hybrid vehicle simulation. For this purpose, a detailed study and comparison of different integrators within the MATLAB environment is done. Consequently, the main efforts are directed towards the implementation of co-simulation techniques in MBS3D software. In this regard, it is not intended to create an extremely precise EV model in terms of real vehicle performance, although an acceptable level of accuracy is achieved. The gap between the EV model and the real system is filled, in a way, by introducing the gas and brake pedals input, which reflects the actual driver behavior. This input is included directly in the differential equations of the model, and determines the amount of current provided to the electric motor. For a separately excited DC motor, the rotor current is proportional to the traction torque delivered to the car wheels. Therefore, as it occurs in the case of real vehicle models, the propulsion torque in the mathematical model is controlled through acceleration and brake pedal commands. The designed transmission system also includes a reduction gear that adapts the torque coming for the motor drive and transfers it. The main contribution of this project is, therefore, the implementation of a new calculation path for the wheel torques, based on performance characteristics and outputs of the electric powertrain model. Originally, the wheel traction and braking torques were input to MBS3D through a vector directly computed by the user in a MATLAB script. Now, they are calculated as a function of the motor current which, in turn, depends on the current provided by the battery pack across the DC/DC chopper converter. The motor and battery currents and voltages are the solutions of the electrical ODE (Ordinary Differential Equation) system coupled to the multibody system. Simultaneously, the outputs of MBS3D model are the position, velocity and acceleration of the vehicle at all times. The motor shaft speed is computed from the output vehicle speed considering the wheel radius, the gear reduction ratio and the transmission efficiency. This motor shaft speed, somehow available from MBS3D model, is then introduced in the differential equations corresponding to the electrical subsystem. In this way, MBS3D and the electrical powertrain model are interconnected and both subsystems exchange values resulting as expected with tight-coupling approach.When programming mathematical models of complex systems, code optimization is a key step in the process. A way to improve the overall performance of the integration, making use of C/C++ as an alternative programming language, is described and implemented. Although this entails a higher computational burden, it leads to important advantages regarding cosimulation speed and stability. In order to do this, it is necessary to integrate MATLAB with another integrated development environment (IDE), where C/C++ code can be generated and executed. In this project, C/C++ files are programmed in Microsoft Visual Studio and the interface between both IDEs is created by building C/C++ MEX file functions. These programs contain functions or subroutines that can be dynamically linked and executed from MATLAB. This process achieves reductions in simulation time up to two orders of magnitude. The tests performed with different integrators, also reveal the stiff character of the differential equations corresponding to the electrical subsystem, and allow the improvement of the cosimulation process. When varying the parameters of the integration and/or the initial conditions of the problem, the solutions of the system of equations show better dynamic response and stability, depending on the integrator used. Several integrators, with variable and non-variable step-size, and for stiff and non-stiff problems are applied to the coupled ODE system. Then, the results are analyzed, compared and discussed. From all the above, the project can be divided into four main parts: 1. Creation of the equation-based electric vehicle model; 2. Programming, simulation and adjustment of the electric vehicle model; 3. Application of co-simulation methodologies to MBS3D and the electric powertrain subsystem; and 4. Code optimization and study of different integrators. Additionally, in order to deeply understand the context of the project, the first chapters include an introduction to basic vehicle dynamics, current classification of hybrid and electric vehicles and an explanation of the involved technologies such as brake energy regeneration, electric and non-electric propulsion systems for EVs and HEVs (hybrid electric vehicles) and their control strategies. Later, the problem of dynamic modeling of hybrid and electric vehicles is discussed. The integrated development environment and the simulation tool are also briefly described. The core chapters include an explanation of the major co-simulation methodologies and how they have been programmed and applied to the electric powertrain model together with the multibody system dynamic model. Finally, the last chapters summarize the main results and conclusions of the project and propose further research topics. In conclusion, co-simulation methodologies are applicable within the integrated development environments MATLAB and Visual Studio, and the simulation tool MBS3D 2.0, where equation-based models of multidisciplinary subsystems, consisting of mechanical and electrical components, are coupled and integrated in a very efficient way.

Estimación del consumo de potencia en un terminal portátil multimedia basada en la PMU del procesador

Este proyecto se incluye en una línea de trabajo que tiene como objetivo final la optimización de la energía consumida por un dispositivo portátil multimedia mediante la aplicación de técnicas de control realimentado, a partir de una modificación dinámica de la frecuencia de trabajo del procesador y de su tensión de alimentación. La modificación de frecuencia y tensión se realiza a partir de la información de realimentación acerca de la potencia consumida por el dispositivo, lo que supone un problema ya que no suele ser posible la monitorización del consumo de potencia en dispositivos de estas características. Este es el motivo por el que se recurre a la estimación del consumo de potencia, utilizando para ello un modelo de predicción. A partir del número de veces que se producen ciertos eventos en el procesador del dispositivo, el modelo de predicción es capaz de obtener una estimación de la potencia consumida por dicho dispositivo. El trabajo llevado a cabo en este proyecto se centra en la implementación de un modelo de estimación de potencia en el kernel de Linux. La razón por la que la estimación se implementa en el sistema operativo es, en primer lugar para lograr un acceso directo a los contadores del procesador. En segundo lugar, para facilitar la modificación de frecuencia y tensión, una vez obtenida la estimación de potencia, ya que esta también se realiza desde el sistema operativo. Otro motivo para implementar la estimación en el sistema operativo, es que la estimación debe ser independiente de las aplicaciones de usuario. Además, el proceso de estimación se realiza de forma periódica, lo que sería difícil de lograr si no se trabajase desde el sistema operativo. Es imprescindible que la estimación se haga de forma periódica ya que al ser dinámica la modificación de frecuencia y tensión que se pretende implementar, se necesita conocer el consumo de potencia del dispositivo en todo momento. Cabe destacar también, que los algoritmos de control se tienen que diseñar sobre un patrón periódico de actuación. El modelo de estimación de potencia funciona de manera específica para el perfil de consumo generado por una única aplicación determinada, que en este caso es un decodificador de vídeo. Sin embargo, es necesario que funcione de la forma más precisa posible para cada una de las frecuencias de trabajo del procesador, y para el mayor número posible de secuencias de vídeo. Esto es debido a que las sucesivas estimaciones de potencia se pretenden utilizar para llevar a cabo la modificación dinámica de frecuencia, por lo que el modelo debe ser capaz de continuar realizando las estimaciones independientemente de la frecuencia con la que esté trabajando el dispositivo. Para valorar la precisión del modelo de estimación se toman medidas de la potencia consumida por el dispositivo a las distintas frecuencias de trabajo durante la ejecución del decodificador de vídeo. Estas medidas se comparan con las estimaciones de potencia obtenidas durante esas mismas ejecuciones, obteniendo de esta forma el error de predicción cometido por el modelo y realizando las modificaciones y ajustes oportunos en el mismo. ABSTRACT. This project is included in a work line which tries to optimize consumption of handheld multimedia devices by the application of feedback control techniques, from a dynamic modification of the processor work frequency and its voltage. The frequency and voltage modification is performed depending on the feedback information about the device power consumption. This is a problem because normally it is not possible to monitor the power consumption on this kind of devices. This is the reason why a power consumption estimation is used instead, which is obtained from a prediction model. Using the number of times some events occur on the device processor, the prediction model is able to obtain a power consumption estimation of this device. The work done in this project focuses on the implementation of a power estimation model in the Linux kernel. The main reason to implement the estimation in the operating system is to achieve a direct access to the processor counters. The second reason is to facilitate the frequency and voltage modification, because this modification is also done from the operating system. Another reason to implement the estimation in the operating system is because the estimation must be done apart of the user applications. Moreover, the estimation process is done periodically, what is difficult to obtain outside the operating system. It is necessary to make the estimation in a periodic way because the frequency and voltage modification is going to be dynamic, so it needs to know the device power consumption at every time. Also, it is important to say that the control algorithms have to be designed over a periodic pattern of action. The power estimation model works specifically for the consumption profile generated by a single application, which in this case is a video decoder. Nevertheless, it is necessary that the model works as accurate as possible for each frequency available on the processor, and for the greatest number of video sequences. This is because the power estimations are going to be used to modify dynamically the frequency, so the model must be able to work independently of the device frequency. To value the estimation model precision, some measurements of the device power consumption are taken at different frequencies during the video decoder execution. These measurements are compared with the power estimations obtained during that execution, getting the prediction error committed by the model, and if it is necessary, making modifications and settings on this model.

A Discursive Perspective on China's Global Politics of Climate Change, 1992-2013

This dissertation investigates China’s recent shift in its climate change policy with a refined discourse approach. Methodologically, by adopting a neo-Gramscian notion of hegemony, a generative definition of discourse and an ontological pluralist position, the study constructs a theoretical framework named “discursive hegemony” that identifies the “social forces” for enabling social change and focuses on the role of discursive mechanisms via which the forces operate and produce effects. The key empirical finding of this study was that it was a co-evolution of conditions that shaped the outcome as China’s climate policy shift. In examining the case, a before-after within-case comparison was designed to analyze the variations in the material, institutional, and ideational conditions, with methods including interviews, conventional narrative/text analysis and descriptive statistics. Specifically, changes in energy use, the structure of decision-making body, and the narratives about sustainable development reflected how the above three types of social force processed in China in the first few years of the 21st century, causing the economic development agenda to absorb the climate issue, and turning the policy frame for the latter from mainly a diplomatic matter to a potential opportunity for better-quality growth. With the discursive operation of the “Science-based development”, China’s energy policy has been a good example of the Chinese understanding of sustainability characterized by economic primacy, ecological viability and social green-engineering. This way of discursive evolution, however, is a double-edged sword that has pushed forward some fast, top-down mitigation measures on the one hand, but has also created and will likely continue creating social and ecological havoc on the other hand. The study makes two major contributions. First and on the empirical level, because China is an international actor that was not expected to cooperate on the climate issue according to major IR theories, this study would add one critical case to the studies on global (environmental) governance and the ideational approach in the IR discipline. Second and on the theory-building level, the model of discursive hegemony can be a causally deeper mode of explanation because it traces the process of co-evolution of social forces.