Determining spatial resolution in spatial load forecasting using a grid-based model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Competing regression models for longitudinal data

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretestposttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.

Studio dei metodi di valutazione della pericolosità associata ai "debris flow" in ambiente alpino

The present work concerns with the study of debris flows and, in particular, with the related hazard in the Alpine Environment. During the last years several methodologies have been developed to evaluate hazard associated to such a complex phenomenon, whose velocity, impacting force and inappropriate temporal prediction are responsible of the related high hazard level. This research focuses its attention on the depositional phase of debris flows through the application of a numerical model (DFlowz), and on hazard evaluation related to watersheds morphometric, morphological and geological characterization. The main aims are to test the validity of DFlowz simulations and assess sources of errors in order to understand how the empirical uncertainties influence the predictions; on the other side the research concerns with the possibility of performing hazard analysis starting from the identification of susceptible debris flow catchments and definition of their activity level. 25 well documented debris flow events have been back analyzed with the model DFlowz (Berti and Simoni, 2007): derived form the implementation of the empirical relations between event volume and planimetric and cross section inundated areas, the code allows to delineate areas affected by an event by taking into account information about volume, preferential flow path and digital elevation model (DEM) of fan area. The analysis uses an objective methodology for evaluating the accuracy of the prediction and involve the calibration of the model based on factors describing the uncertainty associated to the semi empirical relationships. The general assumptions on which the model is based have been verified although the predictive capabilities are influenced by the uncertainties of the empirical scaling relationships, which have to be necessarily taken into account and depend mostly on errors concerning deposited volume estimation. In addition, in order to test prediction capabilities of physical-based models, some events have been simulated through the use of RAMMS (RApid Mass MovementS). The model, which has been developed by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) in Birmensdorf and the Swiss Federal Institute for Snow and Avalanche Research (SLF) takes into account a one-phase approach based on Voellmy rheology (Voellmy, 1955; Salm et al., 1990). The input file combines the total volume of the debris flow located in a release area with a mean depth. The model predicts the affected area, the maximum depth and the flow velocity in each cell of the input DTM. Relatively to hazard analysis related to watersheds characterization, the database collected by the Alto Adige Province represents an opportunity to examine debris-flow sediment dynamics at the regional scale and analyze lithologic controls. With the aim of advancing current understandings about debris flow, this study focuses on 82 events in order to characterize the topographic conditions associated with their initiation , transportation and deposition, seasonal patterns of occurrence and examine the role played by bedrock geology on sediment transfer.

Clutch-size adjustments and skew models: effects on reproductive partitioning and group stability

Reproductive skew theory seeks to integrate social and ecological factors thought to influence the division of reproduction among group-living animals. However, most reproductive skew models only examine interactions between individuals of the same sex. Here, we suggest that females can influence group stability and conflict among males by modifying their clutch size and may do so if they benefit from the presence of subordinate male helpers or from reduced conflict. We develop 3 models, based on concessions-based, restraint, and tug-of-war models, in which female clutch size is variable and ask when females will increase their clutch size above that which would be optimal in the absence of male-male conflict. In concessions-based and restraint models, females should increase clutch size above their optima if the benefits of staying for subordinate males are relatively low. Relatedness between males has no effect on clutch size. When females do increase clutch size, the division of reproduction between males is not influenced by relatedness and does not differ between restraint and concessions-based models. Both of these predictions are in sharp contrast to previous models. In tug-of-war models, clutch size is strongly influenced by relatedness between males, with the largest clutches, but the fewest surviving offspring, produced when males are unrelated. These 3 models demonstrate the importance of considering third-party interests in the decisions of group-living organisms.

Discrete element methods for asphalt concrete : development and application of user-defined microstructural models and a viscoelastic micromechanical model

As an important Civil Engineering material, asphalt concrete (AC) is commonly used to build road surfaces, airports, and parking lots. With traditional laboratory tests and theoretical equations, it is a challenge to fully understand such a random composite material. Based on the discrete element method (DEM), this research seeks to develop and implement computer models as research approaches for improving understandings of AC microstructure-based mechanics. In this research, three categories of approaches were developed or employed to simulate microstructures of AC materials, namely the randomly-generated models, the idealized models, and image-based models. The image-based models were recommended for accurately predicting AC performance, while the other models were recommended as research tools to obtain deep insight into the AC microstructure-based mechanics. A viscoelastic micromechanical model was developed to capture viscoelastic interactions within the AC microstructure. Four types of constitutive models were built to address the four categories of interactions within an AC specimen. Each of the constitutive models consists of three parts which represent three different interaction behaviors: a stiffness model (force-displace relation), a bonding model (shear and tensile strengths), and a slip model (frictional property). Three techniques were developed to reduce the computational time for AC viscoelastic simulations. It was found that the computational time was significantly reduced to days or hours from years or months for typical three-dimensional models. Dynamic modulus and creep stiffness tests were simulated and methodologies were developed to determine the viscoelastic parameters. It was found that the DE models could successfully predict dynamic modulus, phase angles, and creep stiffness in a wide range of frequencies, temperatures, and time spans. Mineral aggregate morphology characteristics (sphericity, orientation, and angularity) were studied to investigate their impacts on AC creep stiffness. It was found that aggregate characteristics significantly impact creep stiffness. Pavement responses and pavement-vehicle interactions were investigated by simulating pavement sections under a rolling wheel. It was found that wheel acceleration, steadily moving, and deceleration significantly impact contact forces. Additionally, summary and recommendations were provided in the last chapter and part of computer programming codes wree provided in the appendixes.

Index of Complexity, Outcome and Need scored on plaster and digital models

The aim of this study was to compare standard plaster models with their digital counterparts for the applicability of the Index of Complexity, Outcome, and Need (ICON). Generated study models of 30 randomly selected patients: 30 pre- (T(0)) and 30 post- (T(1)) treatment. Two examiners, calibrated in the ICON, scored the digital and plaster models. The overall ICON scores were evaluated for reliability and reproducibility using kappa statistics and reliability coefficients. The values for reliability of the total and weighted ICON scores were generally high for the T(0) sample (range 0.83-0.95) but less high for the T(1) sample (range 0.55-0.85). Differences in total ICON score between plaster and digital models resulted in mostly statistically insignificant values (P values ranging from 0.07 to 0.19), except for observer 1 in the T(1) sample. No statistically different values were found for the total ICON score on either plaster or digital models. ICON scores performed on computer-based models appear to be as accurate and reliable as ICON scores on plaster models.

Relative survival is an adequate estimate of cancer-specific survival: baseline mortality-adjusted 10-year survival of 771 rectal cancer patients.

BACKGROUND The objective of the present investigation is to assess the baseline mortality-adjusted 10-year survival of rectal cancer patients. METHODS Ten-year survival was analyzed in 771 consecutive American Joint Committee on Cancer (AJCC) stage I-IV rectal cancer patients undergoing open resection between 1991 and 2008 using risk-adjusted Cox proportional hazard regression models adjusting for population-based baseline mortality. RESULTS The median follow-up of patients alive was 8.8 years. The 10-year relative, overall, and cancer-specific survival were 66.5% [95% confidence interval (CI) 61.3-72.1], 48.7% (95% CI 44.9-52.8), and 66.4% (95% CI 62.5-70.5), respectively. In the entire patient sample (stage I-IV) 47.3% and in patients with stage I-III 33.6 % of all deaths were related to rectal cancer during the 10-year period. For patients with AJCC stage I rectal cancer, the 10-year overall survival was 96% and did not significantly differ from an average population after matching for gender, age, and calendar year (p = 0.151). For the more advanced tumor stages, however, survival was significantly impaired (p < 0.001). CONCLUSIONS Retrospective investigations of survival after rectal cancer resection should adjust for baseline mortality because a large fraction of deaths is not cancer related. Stage I rectal cancer patients, compared to patients with more advanced disease stages, have a relative survival close to 100% and can thus be considered cured. Using this relative-survival approach, the real public health burden caused by rectal cancer can reliably be analyzed and reported.

The Devil’s Calculus: Mathematical Models of Civil War

In spite of the movement to turn political science into a real science, various mathematical methods that are now the staples of physics, biology, and even economics are thoroughly uncommon in political science, especially the study of civil war. This study seeks to apply such methods - specifically, ordinary differential equations (ODEs) - to model civil war based on what one might dub the capabilities school of thought, which roughly states that civil wars end only when one side’s ability to make war falls far enough to make peace truly attractive. I construct several different ODE-based models and then test them all to see which best predicts the instantaneous capabilities of both sides of the Sri Lankan civil war in the period from 1990 to 1994 given parameters and initial conditions. The model that the tests declare most accurate gives very accurate predictions of state military capabilities and reasonable short term predictions of cumulative deaths. Analysis of the model reveals the scale of the importance of rebel finances to the sustainability of insurgency, most notably that the number of troops required to put down the Tamil Tigers is reduced by nearly a full order of magnitude when Tiger foreign funding is stopped. The study thus demonstrates that accurate foresight may come of relatively simple dynamical models, and implies the great potential of advanced and currently unconventional non-statistical mathematical methods in political science.

Medem Channel profiles based on stand-alone model simulation and corrections with nudging

State-of-the-art process-based models have shown to be applicable to the simulation and prediction of coastal morphodynamics. On annual to decadal temporal scales, these models may show limitations in reproducing complex natural morphological evolution patterns, such as the movement of bars and tidal channels, e.g. the observed decadal migration of the Medem Channel in the Elbe Estuary, German Bight. Here a morphodynamic model is shown to simulate the hydrodynamics and sediment budgets of the domain to some extent, but fails to adequately reproduce the pronounced channel migration, due to the insufficient implementation of bank erosion processes. In order to allow for long-term simulations of the domain, a nudging method has been introduced to update the model-predicted bathymetries with observations. The model-predicted bathymetry is nudged towards true states in annual time steps. Sensitivity analysis of a user-defined correlation length scale, for the definition of the background error covariance matrix during the nudging procedure, suggests that the optimal error correlation length is similar to the grid cell size, here 80-90 m. Additionally, spatially heterogeneous correlation lengths produce more realistic channel depths than do spatially homogeneous correlation lengths. Consecutive application of the nudging method compensates for the (stand-alone) model prediction errors and corrects the channel migration pattern, with a Brier skill score of 0.78. The proposed nudging method in this study serves as an analytical approach to update model predictions towards a predefined 'true' state for the spatiotemporal interpolation of incomplete morphological data in long-term simulations.

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

Teaching based on the case method of building collective protection

One of the fundamental aspects in the adaptation of the teaching to the European higher education is changing based models of teacher education to models based on student learning. In this work we present an educational experience developed with the teaching method based on the case method, with a clearly multidisciplinary. The experience has been developed in the teaching of analysis and verification of safety rails. This is a multidisciplinary field that presents great difficulties during their teaching. The use of the case method has given good results in the competences achieved by students

Nationwide impact and vehicle to grid application of electric vehicles mobility using an activity based model

This paper describes the impact of electric mobility on the transmission grid in Flanders region (Belgium), using a micro-simulation activity based models. These models are used to provide temporal and spatial estimation of energy and power demanded by electric vehicles (EVs) in different mobility zones. The increment in the load demand due to electric mobility is added to the background load demand in these mobility areas and the effects over the transmission substations are analyzed. From this information, the total storage capacity per zone is evaluated and some strategies for EV aggregator are proposed, allowing the aggregator to fulfill bids on the electricity markets.

Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers

In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques.