The reform of the European Energy Tax Directive reform : effect on prices

[spa] Para hacer frente a los riesgos relacionados con la contaminación atmosférica, es ampliamente aceptada la necesidad de instrumentos de política encaminados a reducir las emisiones. La intervención tiene por objeto reducir las conductas contaminantes y incentivar una conducta más respetuosa y el uso de tecnologías más eficientes. La Unión Europea cuenta con dos importantes mecanismos económicos para el control de emisiones a escala europea: la directiva sobre los impuestos energéticos, un instrumento de fiscalidad ambiental aprobado en 2003 que afecta el precio de los productos energéticos, y el sistema de comercio de los derechos de emisiones, introducido en 2005, que afecta directamente a la cantidad de emisiones de CO2. En 2011, la Comisión Europea propuso una nueva versión de la directiva sobre los impuestos energéticos. El objetivo principal de la propuesta es aumentar la eficacia del instrumento a través de una mayor presión fiscal sobre los productos energéticos y de coordinar este instrumento de fiscalidad medioambiental con el sistema de comercio de los derechos de emisiones, para establecer una señal de precio de CO2 coherente para todos los sectores. Sin embargo, en mayo de 2012 el Parlamento Europeo bloqueó la propuesta de la nueva versión del impuesto, y el proceso de actualización se detuvo. La preocupación principal parecía ser el efecto de dicha propuesta en la competitividad, en particular para los sectores que serían los más afectados dado el uso intensivo de los productos energéticos, como el sector del transporte. El objetivo de este estudio es analizar el efecto que la reforma de la directiva sobre los impuestos energéticos podría tener sobre el nivel de precios, en particular en los países de la Unión Europea donde esta reforma implicaría un aumento de los impuestos energéticos. Utilizando datos del proyecto “World Input-Output Database”, la principal conclusión es que el nuevo sistema de impuestos energéticos tendría un impacto muy bajo sobre los precios. Por lo tanto, dado que los precios no serían fuertemente afectados por la reforma, no habrá inconvenientes para la competitividad y implicaciones en términos de distribución, pero, por otro lado, este resultado también implica una baja capacidad de esta reforma para provocar cambios en el consumo y la producción hacia menos presiones ambientales.

The reform of the European Energy Tax Directive reform : effect on prices

[spa] Para hacer frente a los riesgos relacionados con la contaminación atmosférica, es ampliamente aceptada la necesidad de instrumentos de política encaminados a reducir las emisiones. La intervención tiene por objeto reducir las conductas contaminantes y incentivar una conducta más respetuosa y el uso de tecnologías más eficientes. La Unión Europea cuenta con dos importantes mecanismos económicos para el control de emisiones a escala europea: la directiva sobre los impuestos energéticos, un instrumento de fiscalidad ambiental aprobado en 2003 que afecta el precio de los productos energéticos, y el sistema de comercio de los derechos de emisiones, introducido en 2005, que afecta directamente a la cantidad de emisiones de CO2. En 2011, la Comisión Europea propuso una nueva versión de la directiva sobre los impuestos energéticos. El objetivo principal de la propuesta es aumentar la eficacia del instrumento a través de una mayor presión fiscal sobre los productos energéticos y de coordinar este instrumento de fiscalidad medioambiental con el sistema de comercio de los derechos de emisiones, para establecer una señal de precio de CO2 coherente para todos los sectores. Sin embargo, en mayo de 2012 el Parlamento Europeo bloqueó la propuesta de la nueva versión del impuesto, y el proceso de actualización se detuvo. La preocupación principal parecía ser el efecto de dicha propuesta en la competitividad, en particular para los sectores que serían los más afectados dado el uso intensivo de los productos energéticos, como el sector del transporte. El objetivo de este estudio es analizar el efecto que la reforma de la directiva sobre los impuestos energéticos podría tener sobre el nivel de precios, en particular en los países de la Unión Europea donde esta reforma implicaría un aumento de los impuestos energéticos. Utilizando datos del proyecto “World Input-Output Database”, la principal conclusión es que el nuevo sistema de impuestos energéticos tendría un impacto muy bajo sobre los precios. Por lo tanto, dado que los precios no serían fuertemente afectados por la reforma, no habrá inconvenientes para la competitividad y implicaciones en términos de distribución, pero, por otro lado, este resultado también implica una baja capacidad de esta reforma para provocar cambios en el consumo y la producción hacia menos presiones ambientales.

WM Program manual

This manual describes how to run the new produced GUI C++ program that so called'WM' program. Section two describes the instructions of the program installation.Section three illustrates test runs description including running the program WM,sample of the input, output files, in addition to some generated graphs followed by the main form of the program created by using the Borland C++ Builder 6.

Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.

On-site calibration of a phase fraction meter by an inverse technique

The formal calibration procedure of a phase fraction meter is based on registering the outputs resulting from imposed phase fractions at known flow regimes. This can be straightforwardly done in laboratory conditions, but is rarely the case in industrial conditions, and particularly for on-site applications. Thus, there is a clear need for less restrictive calibration methods regarding to the prior knowledge of the complete set of inlet conditions. A new procedure is proposed in this work for the on-site construction of the calibration curve from total flown mass values of the homogeneous dispersed phase. The solution is obtained by minimizing a convenient error functional, assembled with data from redundant tests to handle the intrinsic ill-conditioned nature of the problem. Numerical simulations performed for increasing error levels demonstrate that acceptable calibration curves can be reconstructed, even from total mass measured within a precision of up to 2%. Consequently, the method can readily be applied, especially in on-site calibration problems in which classical procedures fail due to the impossibility of having a strict control of all the input/output parameters.

Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines

The dissertation proposes two control strategies, which include the trajectory planning and vibration suppression, for a kinematic redundant serial-parallel robot machine, with the aim of attaining the satisfactory machining performance. For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set of trajectories in the robot's joint space are generated based on the best stiffness performance of the robot along the prescribed trajectory. To construct the required system-wide analytical stiffness model for the serial-parallel robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation. The modified method is an evolution of Gosselin's lumped model that can account for the deformations of a flexible link in more directions. The effectiveness of this VJM variant is validated by comparing the computed stiffness results of a flexible link with the those of a matrix structural analysis (MSA) method. The comparison shows that the numerical results from both methods on an individual flexible beam are almost identical, which, in some sense, provides mutual validation. The most prominent advantage of the presented VJM variant compared with the MSA method is that it can be applied in a flexible structure system with complicated kinematics formed in terms of flexible serial links and joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide analytical stiffness model can be easily obtained for mechanisms with both serial kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a kinematic redundant serial-parallel robot machine is constructed based on integration of the VJM variant and the virtual work principle. Numerical results of its stiffness performance are reported. For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed location of the end-effector, the robot permits an infinite number of inverse solutions, which consequently yields infinite kinds of stiffness performance. Therefore, a differential evolution (DE) algorithm in which the positions of redundant joints in the kinematics are taken as input variables was employed to search for the best stiffness performance of the robot. Numerical results of the generated joint trajectories are given for a kinematic redundant serial-parallel robot machine, IWR (Intersector Welding/Cutting Robot), when a particular trajectory of its end-effector has been prescribed. The numerical results show that the joint trajectories generated based on the stiffness optimization are feasible for realization in the control system since they are acceptably smooth. The results imply that the stiffness performance of the robot machine deviates smoothly with respect to the kinematic configuration in the adjacent domain of its best stiffness performance. To suppress the vibration of the robot machine due to varying cutting force during the machining process, this dissertation proposed a feedforward control strategy, which is constructed based on the derived inverse dynamics model of target system. The effectiveness of applying such a feedforward control in the vibration suppression has been validated in a parallel manipulator in the software environment. The experimental study of such a feedforward control has also been included in the dissertation. The difficulties of modelling the actual system due to the unknown components in its dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed for identification of the unknown components of the dynamics model of the target system. To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can utilize an experimental input-output data set of the entire dynamic system is introduced in the dissertation. Validation of the BP neural network and the modified Levenberg- Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a second order system parameters estimation, and a friction model estimation of a parallel manipulator, which represent three different application aspects of this method.

JNK regulates dendrite and spine architecture in the central nervous system influencing spatial learning and motor tasks

JNK1 is a MAP-kinase that has proven a significant player in the central nervous system. It regulates brain development and the maintenance of dendrites and axons. Several novel phosphorylation targets of JNK1 were identified in a screen performed in the Coffey lab. These proteins were mainly involved in the regulation of neuronal cytoskeleton, influencing the dynamics and stability of microtubules and actin. These structural proteins form the dynamic backbone for the elaborate architecture of the dendritic tree of a neuron. The initiation and branching of the dendrites requires a dynamic interplay between the cytoskeletal building blocks. Both microtubules and actin are decorated by associated proteins which regulate their dynamics. The dendrite-specific, high molecular weight microtubule associated protein 2 (MAP2) is an abundant protein in the brain, the binding of which stabilizes microtubules and influences their bundling. Its expression in non-neuronal cells induces the formation of neurite-like processes from the cell body, and its function is highly regulated by phosphorylation. JNK1 was shown to phosphorylate the proline-rich domain of MAP2 in vivo in a previous study performed in the group. Here we verify three threonine residues (T1619, T1622 and T1625) as JNK1 targets, the phosphorylation of which increases the binding of MAP2 to microtubules. This binding stabilizes the microtubules and increases process formation in non-neuronal cells. Phosphorylation-site mutants were engineered in the lab. The non-phosphorylatable mutant of MAP2 (MAP2- T1619A, T1622A, T1625A) in these residues fails to bind microtubules, while the pseudo-phosphorylated form, MAP2- T1619D, T1622D, Thr1625D, efficiently binds and induces process formation even without the presence of active JNK1. Ectopic expression of the MAP2- T1619D, T1622D, Thr1625D in vivo in mouse brain led to a striking increase in the branching of cortical layer 2/3 (L2/3) pyramidal neurons, compared to MAP2-WT. The dendritic complexity defines the receptive field of a neuron and dictates the output to the postsynaptic cells. Previous studies in the group indicated altered dendrite architecture of the pyramidal neurons in the Jnk1-/- mouse motor cortex. Here, we used Lucifer Yellow loading and Sholl analysis of neurons in order to study the dendritic branching in more detail. We report a striking, opposing effect in the absence of Jnk1 in the cortical layers 2/3 and 5 of the primary motor cortex. The basal dendrites of pyramidal neurons close to the pial surface at L2/3 show a reduced complexity. In contrast, the L5 neurons, which receive massive input from the L2/3 neurons, show greatly increased branching. Another novel substrate identified for JNK1 was MARCKSL1, a protein that regulates actin dynamics. It is highly expressed in neurons, but also in various cancer tissues. Three phosphorylation target residues for JNK1 were identified, and it was demonstrated that their phosphorylation reduces actin turnover and retards migration of these cells. Actin is the main cytoskeletal component in dendritic spines, the site of most excitatory synapses in pyramidal neurons. The density and gross morphology of the Lucifer Yellow filled dendrites were characterized and we show reduced density and altered morphology of spines in the motor cortex and in the hippocampal area CA3. The dynamic dendritic spines are widely considered to function as the cellular correlate during learning. We used a Morris water maze to test spatial memory. Here, the wild-type mice outperformed the knock-out mice during the acquisition phase of the experiment indicating impaired special memory. The L5 pyramidal neurons of the motor cortex project to the spinal cord and regulate the movement of distinct muscle groups. Thus the altered dendrite morphology in the motor cortex was expected to have an effect on the input-output balance in the signaling from the cortex to the lower motor circuits. A battery of behavioral tests were conducted for the wild-type and Jnk1-/- mice, and the knock-outs performed poorly compared to wild-type mice in tests assessing balance and fine motor movements. This study expands our knowledge of JNK1 as an important regulator of the dendritic fields of neurons and their manifestations in behavior.

Structural dynamics in the global pulp and paper industry

The pulp and paper industry is currently facing broad structural changes due to global shifts in demand and supply. These changes have significant impacts on national economies worldwide. Planted forests (especially eucalyptus) and recovered paper have quickly increased their importance as raw material for paper and paperboard production. Although advances in information and communication technologies could reduce the demand for communication papers, and the growth of paper consumption has indeed flattened in developed economies, particularly in North America and Western Europe, the consumption is increasing on a global scale. Moreover, the focal point of production and consumption is moving from the Western world to the rapidly growing markets of Southeast Asia. This study analyzes how the so-called megatrends (globalization, technological development, and increasing environmental awareness) affect the pulp and paper industry’s external environment, and seeks reliable ways to incorporate the impact of the megatrends on the models concerning the demand, trade, and use of paper and pulp. The study expands current research in several directions and points of view, for example, by applying and incorporating several quantitative methods and different models. As a result, the thesis makes a significant contribution to better understand and measure the impacts of structural changes on the pulp and paper industry. It also provides some managerial and policy implications.

Uncertainty of Efficiency Measurements in Electric Drives

The two central goals of this master's thesis are to serve as a guidebook on the determination of uncertainty in efficiency measurements and to investigate sources of uncertainty in efficiency measurements in the field of electric drives by a literature review, mathematical modeling and experimental means. The influence of individual sources of uncertainty on the total instrumental uncertainty is investigated with the help of mathematical models derived for a balance and a direct air cooled calorimeter. The losses of a frequency converter and an induction motor are measured with the input-output method and a balance calorimeter at 50 and 100 % loads. A software linking features of Matlab and Excel is created to process measurement data, calculate uncertainties and to calculate and visualize results. The uncertainties are combined with both the worst case and the realistic perturbation method and distributions of uncertainty by source are shown based on experimental results. A comparison of the calculated uncertainties suggests that the balance calorimeter determines losses more accurately than the input-output method with a relative RPM uncertainty of 1.46 % compared to 3.78 - 12.74 % respectively with 95 % level of confidence at the 93 % induction motor efficiency or higher. As some principles in uncertainty analysis are open to interpretation the views and decisions of the analyst can have noticeable influence on the uncertainty in the measurement result.

Nearly Serial Sharing Methods

A group of agents participate in a cooperative enterprise producing a single good. Each participant contributes a particular type of input; output is nondecreasing in these contributions. How should it be shared? We analyze the implications of the axiom of Group Monotonicity: if a group of agents simultaneously decrease their input contributions, not all of them should receive a higher share of output. We show that in combination with other more familiar axioms, this condition pins down a very small class of methods, which we dub nearly serial.

The Transmission of Monetary Policy in a Multi-Sector Economy

This paper constructs and estimates a sticky-price, Dynamic Stochastic General Equilibrium model with heterogenous production sectors. Sectors differ in price stickiness, capital-adjustment costs and production technology, and use output from each other as material and investment inputs following an Input-Output Matrix and Capital Flow Table that represent the U.S. economy. By relaxing the standard assumption of symmetry, this model allows different sectoral dynamics in response to monetary policy shocks. The model is estimated by Simulated Method of Moments using sectoral and aggregate U.S. time series. Results indicate 1) substantial heterogeneity in price stickiness across sectors, with quantitatively larger differences between services and goods than previously found in micro studies that focus on final goods alone, 2) a strong sensitivity to monetary policy shocks on the part of construction and durable manufacturing, and 3) similar quantitative predictions at the aggregate level by the multi-sector model and a standard model that assumes symmetry across sectors.

Production Linkages Between Informal and Formal Activities Considering Domestic and Imported Inputs: an Application of the Minimal-Flow-Analysis Method to Senegal.

We apply to the Senegalese input-output matrix of 1990, disagregated into formal and informal activities, a recently designed structural analytical method (Minimal-Flow-Analysis) which permits to depict the direct and indirect production likanges existing between activities.

Durable Goods, Inter-Sectoral Linkages and Monetary Policy

Barsky, House and Kimball (2007) show that introducing durable goods into a sticky-price model leads to negative sectoral comovement of production following a monetary policy shock and, under certain conditions, to aggregate neutrality. These results appear to undermine sticky-price models. In this paper, we show that these results are not robust to two prominent and realistic features of the data, namely input-output interactions and limited mobility of productive inputs. When extended to allow for both features, the sticky-price model with durable goods delivers implications in line with VAR evidence on the effects of monetary policy shocks.

Le financement public de l’enseignement supérieur et ses impacts au Pérou : une analyse longitudinale (1993 – 2003)

Cette recherche porte sur le financement public de l’enseignement supérieur au Pérou et ses impacts dans une perspective longitudinale couvant la période 1993-2003. Cette période est importante parce qu’elle a été témoin, dans ce pays, de changements majeurs aux plans du financement public et de la configuration du système d’enseignement supérieur. La recherche consiste principalement dans des analyses secondaires de données pertinentes publiées par des organismes nationaux et internationaux. Les analyses sont structurées à partir d’un schéma d’inputs et outputs. On considère comme inputs les ressources financières et les ressources humaines, lesquelles comprennent les professeurs et les étudiants, et comme outputs les taux de diplomation (efficacité interne) et la demande de diplômés par le marché du travail (efficacité externe). La théorie de la dépendance de ressources sert de cadre pour interpréter les rapports entre le financement public et ses incidences sur les réponses institutionnels et ses conséquences. Dans la période retenue, le financement du secteur public a décru de 32% en raison d’un désengagement progressif de l’État. Une conséquence majeure de la diminution du financement public a été la croissance rapide du secteur privé de l’enseignement supérieur. En effet, alors qu’en 1993 il y avait 24 institutions privées d’enseignement supérieur, il y en avait, en 2003, 46 institutions. La baisse du financement public et la croissance du secteur privé d’enseignement supérieur ont eu des incidences sur la sélectivité des étudiants, sur le statut des professeurs, sur l’implication des universités en recherche et sur les taux de diplomation. Le taux de sélectivité dans le secteur public a augmenté entre 1993 et 2003, alors que ce taux a diminué, dans la même période, dans le secteur privé. Ainsi, le secteur public répond à la diminution du financement en restreignant l’accès à l’enseignement supérieur. Le secteur privé, par contre, diminue sa sélectivité compensant ainsi l’augmentation de la sélectivité dans le secteur public et, par le fait même, augmente sa part de marché. Également, tant dans le secteur public que dans le secteur privé, les professeurs sont engagés principalement sur une base temporaire, ce qui se traduit, particulièrement dans le secteur privé, dans un moindre engagement institutionnel. Enfin, les universités publiques et privées du Pérou font peu de recherche, car elles favorisent, pour balancer leurs budgets, la consultation et les contrats au détriment de la recherche fondamentale. Paradoxalement, alors que, dans le secteur privé, les taux de sélectivité des étudiants diminuent, leurs taux de diplomation augmentent plus que dans le secteur public. Enfin, les formations avec plus d’étudiants inscrits, tant dans le secteur public que privé, sont les moins coûteuses en infrastructure et équipements. Dès lors, la pertinence de la production universitaire devient problématique. Cette recherche révèle que les organisations universitaires, face à un environnement où les ressources financières deviennent de plus en plus rares, développent des stratégies de survie qui peuvent avoir des incidences sur la qualité et la pertinence de l’enseignement supérieur.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.