Análisis input-output de encadenamientos productivos y sectores clave en la economía mexicana

Este artículo busca identificar las industrias clave de la economía mexicana. Para este propósito, se aplican las siguientes metodologías basadas en el análisis input-output: a) el método Chenery-Watabane (1958) para el cálculo de encadenamientos productivos directos; b) el método Rasmussen (1963) para el cálculo de encadenamientos productivos totales; c) el enfoque de demanda de Leontief (1985) para cuantificar los encadenamientos hacia atrás directos y totales; d) el enfoque de oferta de Ghosh (1958, 1968) para la cuantificación de los encadenamientos hacia delante directos y totales. Finalmente, los resultados de estas aplicaciones muestran que los sectores clave de México son las industrias de bienes intermedios y bienes de capital.

Model simulation output data (CCSM3) related to uplift of Africa and Himalaya/Tibet Plateau and the influence on tropical African vegetation

Hominid evolution in the late Miocene has long been hypothesized to be linked to the retreat of the tropical rainforest in Africa. One cause for the climatic and vegetation change often considered was uplift of Africa, but also uplift of the Himalaya and the Tibetan Plateau was suggested to have impacted rainfall distribution over Africa. Recent proxy data suggest that in East Africa open grassland habitats were available to the common ancestors of hominins and apes long before their divergence and do not find evidence for a closed rainforest in the late Miocene. We used the coupled global general circulation model CCSM3 including an interactively coupled dynamic vegetation module to investigate the impact of topography on African hydro-climate and vegetation. We performed sensitivity experiments altering elevations of the Himalaya and the Tibetan Plateau as well as of East and Southern Africa. The simulations confirm the dominant impact of African topography for climate and vegetation development of the African tropics. Only a weak influence of prescribed Asian uplift on African climate could be detected. The model simulations show that rainforest coverage of Central Africa is strongly determined by the presence of elevated African topography. In East Africa, despite wetter conditions with lowered African topography, the conditions were not favorable enough to maintain a closed rainforest. A discussion of the results with respect to other model studies indicates a minor importance of vegetation-atmosphere or ocean-atmosphere feedbacks and a large dependence of the simulated vegetation response on the land surface/vegetation model.

Matriz Input-Output da região Alentejo: Acerca da Distribuição de Rendimento (3º quadrante)

No número 18 do “Boletim Trimestral” apresentámos os principais resultados do estudo que elaborou a Matriz Input-Output da Região Alentejo (MIO-Alentejo). Com este texto prosseguimos o propósito de divulgação dos resultados e conclusões do projeto, mas adotando agora uma perspectiva mais focalizada. Em particular, interessa-nos de momento olhar para o processo de formação do valor acrescentado, ou, de forma equivalente, para a distribuição do rendimento gerado na produção, sob a forma de remuneração dos diferentes fatores produtivos (3º quadrante). Nos pontos 2, 3, e 4 apresentamos os resultados e, em conclusão, deixamos algumas considerações finais no ponto 5. Anexamos um glossário com uma breve descrição metodológica.

Demand and supply oriented input-output models: a linkage analysis for Portugal.

Doutoramento em Economia.

Modelli Input-Output: un'analisi simultanea di shock domanda e offerta

La tesi analizza il modello Input-Output, introdotto da Leontief nel 1936, per studiare la reazione dei sistemi industriali di Germania, Spagna ed Italia alle restrizioni imposte dai governi per limitare la diffusione della pandemia da COVID-19. Si studiano le economie considerando gli scambi tra i settori produttivi intermedi e la domanda finale. La formulazione originale del modello necessita diverse modifiche per descrivere realisticamente le reti di produzione e comunque non è del tutto esaustiva in quanto si ipotizza che la produttività dei sistemi sia sempre tale da soddisfare pienamente la domanda che giunge per il prodotto emesso. Perciò si introduce una distinzione tra le variabili del problema, assumendo che alcune componenti di produzione siano indipendenti dalla richiesta e che altre componenti siano endogene. Le soluzioni di questo sistema tuttavia non sempre risultano appartenenti al dominio di definizione delle variabili. Dunque utilizzando tecniche di programmazione lineare, si osservano i livelli massimi di produzione e domanda corrisposta in un periodo di crisi anche quando i sistemi non raggiungono questa soglia poiché non pienamente operativi. Si propongono diversi schemi di razionamento per distribuire tra i richiedenti i prodotti emessi: 1) programma proporzionale in base alle domande di tutti i richiedenti; 2) programma proporzionale in base alle richieste, con precedenza ai settori intermedi; 3) programma prioritario in cui vengono riforniti i settori intermedi in base alla dimensione dell’ordine; 4) programma prioritario con fornitura totale degli ordini e ordine di consegna casuale. I risultati ottenuti dipendono dal modello di fornitura scelto, dalla dimensione dello shock cui i settori sono soggetti e dalle proprietà della rete industriale, descritta come grafo pesato.

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.

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.

An on-line algorithm of uncertain time delay estimation in a continuous system

In this paper, we present an on-line estimation algorithm for an uncertain time delay in a continuous system based on the observational input-output data, subject to observational noise. The first order Pade approximation is used to approximate the time delay. At each time step, the algorithm combines the well known Kalman filter algorithm and the recursive instrumental variable least squares (RIVLS) algorithm in cascade form. The instrumental variable least squares algorithm is used in order to achieve the consistency of the delay parameter estimate, since an error-in-the-variable model is involved. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Autonomous ship collision free trajectory navigation and control algorithms

A new autonomous ship collision free (ASCF) trajectory navigation and control system has been introduced with a new recursive navigation algorithm based on analytic geometry and convex set theory for ship collision free guidance. The underlying assumption is that the geometric information of ship environment is available in the form of a polygon shaped free space, which may be easily generated from a 2D image or plots relating to physical hazards or other constraints such as collision avoidance regulations. The navigation command is given as a heading command sequence based on generating a way point which falls within a small neighborhood of the current position, and the sequence of the way points along the trajectory are guaranteed to lie within a bounded obstacle free region using convex set theory. A neurofuzzy network predictor which in practice uses only observed input/output data generated by on board sensors or external sensors (or a sensor fusion algorithm), based on using rudder deflection angle for the control of ship heading angle, is utilised in the simulation of an ESSO 190000 dwt tanker model to demonstrate the effectiveness of the system.

Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm

In this article a simple and effective controller design is introduced for the Hammerstein systems that are identified based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The controller is composed by computing the inverse of the B-spline approximated nonlinear static function, and a linear pole assignment controller. The contribution of this article is the inverse of De Boor algorithm that computes the inverse efficiently. Mathematical analysis is provided to prove the convergence of the proposed algorithm. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Wiener System identification using B-spline functions with De Boor recursion

A simple and effective algorithm is introduced for the system identification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton algorithm with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is demonstrated using an illustrative example.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.