Autopercepción de la identidad en la transición a la edad adulta

[Es] La complejidad social de los países desarrollados, junto a la peculiaridad de los factores socioculturales de cada país y el protagonismo del sujeto en su propio desarrollo, están condicionando las diferentes trayectorias en el paso de la adolescencia a la edad adulta. La investigación que se presenta tiene por objeto profundizar en la autopercepción de la identidad personal y describir los procesos psicológicos que favorecen la madurez psicológica y la autonomía personal a pesar de mantener relaciones y estados de dependencia familiar y económica. Se han encuestado a un total de 231 varones y mujeres, de edades comprendidas entre los 19 y los 30 años de edad, para saber cómo se definen en relación con su identidad personal y cuáles son las razones que les lleva a tal percepción. Los resultados demuestran que hay hasta cuatro identidades diferenciadas en la década de los 20 a los 30 años, basadas en un proceso subjetivo del desarrollo del yo individual orientado hacia la madurez psicológica, más que en otros acontecimientos sociales más o menos normativos.

MPI parallel programming of mixed integer optimization problems using CPLEX with COIN-OR

The aim of this technical report is to present some detailed explanations in order to help to understand and use the Message Passing Interface (MPI) parallel programming for solving several mixed integer optimization problems. We have developed a C++ experimental code that uses the IBM ILOG CPLEX optimizer within the COmputational INfrastructure for Operations Research (COIN-OR) and MPI parallel computing for solving the optimization models under UNIX-like systems. The computational experience illustrates how can we solve 44 optimization problems which are asymmetric with respect to the number of integer and continuous variables and the number of constraints. We also report a comparative with the speedup and efficiency of several strategies implemented for some available number of threads.

A rational fraction polynomials model to study vertical dynamic wheel-rail interaction

This paper presents a model designed to study vertical interactions between wheel and rail when the wheel moves over a rail welding. The model focuses on the spatial domain, and is drawn up in a simple fashion from track receptances. The paper obtains the receptances from a full track model in the frequency domain already developed by the authors, which includes deformation of the rail section and propagation of bending, elongation and torsional waves along an infinite track. Transformation between domains was secured by applying a modified rational fraction polynomials method. This obtains a track model with very few degrees of freedom, and thus with minimum time consumption for integration, with a good match to the original model over a sufficiently broad range of frequencies. Wheel-rail interaction is modelled on a non-linear Hertzian spring, and consideration is given to parametric excitation caused by the wheel moving over a sleeper, since this is a moving wheel model and not a moving irregularity model. The model is used to study the dynamic loads and displacements emerging at the wheel-rail contact passing over a welding defect at different speeds.

The Economics of Gasification: A Market-Based Approach

This paper deals with the economics of gasification facilities in general and IGCC power plants in particular. Regarding the prospects of these systems, passing the technological test is one thing, passing the economic test can be quite another. In this respect, traditional valuations assume constant input and/or output prices. Since this is hardly realistic, we allow for uncertainty in prices. We naturally look at the markets where many of the products involved are regularly traded. Futures markets on commodities are particularly useful for valuing uncertain future cash flows. Thus, revenues and variable costs can be assessed by means of sound financial concepts and actual market data. On the other hand, these complex systems provide a number of flexibility options (e.g., to choose among several inputs, outputs, modes of operation, etc.). Typically, flexibility contributes significantly to the overall value of real assets. Indeed, maximization of the asset value requires the optimal exercise of any flexibility option available. Yet the economic value of flexibility is elusive, the more so under (price) uncertainty. And the right choice of input fuels and/or output products is a main concern for the facility managers. As a particular application, we deal with the valuation of input flexibility. We follow the Real Options approach. In addition to economic variables, we also address technical and environmental issues such as energy efficiency, utility performance characteristics and emissions (note that carbon constraints are looming). Lastly, a brief introduction to some stochastic processes suitable for valuation purposes is provided.

Exploring Game Performance in the National Basketball Association Using Player Tracking Data

Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.