Temporary factors that condition innovation: comparison between family and non-family businesses

Studies conducted on innovation in family businesses have offered very diverse and sometimes contradictory results. The objective of this paper is to analyze the influence of time-related variables on the innovative behavior of companies. Furthermore, we compare the behavior of family and non-family companies, the influence of the generation and the transference of management. To do this, companies are classified according to the stage of life in which they are and are compared using a mean difference test (Anova). Subsequently, already focused on family businesses, the effects of generating control in the case of family businesses are analyzed, considering the foundational and subsequent periods. The results show that the behavior towards the innovation of family businesses is conditioned by the temporal dimension

On the effect of the thermostat in non-equilibrium molecular dynamics simulations

The numerical investigation of the statics and dynamics of systems in nonequilibrium in general, and under shear flow in particular, has become more and more common. However, not all the numerical methods developed to simulate equilibrium systems can be successfully adapted to out-of-equilibrium cases. This is especially true for thermostats. Indeed, even though thermostats developed to work under equilibrium conditions sometimes display good agreement with rheology experiments, their performance rapidly degrades beyond weak dissipation and small shear rates. Here we focus on gauging the relative performances of three thermostats, Langevin, dissipative particle dynamics, and Bussi-Donadio-Parrinello under varying parameters and external conditions. We compare their effectiveness by looking at different observables and clearly demonstrate that choosing the right thermostat (and its parameters) requires a careful evaluation of, at least, temperature, density and velocity profiles. We also show that small modifications of the Langevin and DPD thermostats greatly enhance their performance in a wide range of parameters.Comment: 13 pages, 9 figure

Self-assembly in chains, rings and branches: a single component system with two critical points

We study the interplay between phase separation and self-assembly in chains, rings and branched structures in a model of particles with dissimilar patches. We extend Wertheim's first order perturbation theory to include the effects of ring formation and theoretically investigate the thermodynamics of the model. We find a peculiar shape for the vapor-liquid coexistence, featuring re-entrant behavior in both phases and two critical points, despite the single-component nature of the system. The emergence of the lower critical point is caused by the self-assembly of rings taking place in the vapor, generating a phase with lower energy and lower entropy than the liquid. Monte Carlo simulations of the same model fully support these unconventional theoretical predictions.Comment: 5 pages, 3 figure

La afasia en las personas mayores

[Resumen] La afasia es una patología, secundaria a la enfermedad vascular cerebral, que cuenta con una gran incidencia en la población actual. Si se atiende a los factores de riesgo para padecer esta enfermedad se puede ver que el grupo más afectado va a ser el de las personas mayores. Por ello, mediante una revisión bibliográfica, se analizaron todas las características de la patología, su etiología y su tratamiento. El objetivo de este trabajo fue tratar de determinar las características de los pacientes aquejados de los distintos tipos de afasia y si existía alguna relación entre la afasia y la edad, haciendo a las personas de edades avanzadas más propensas a sufrir algún tipo de esta patología en particular. Se puede afirmar, tras el estudio, que esta última premisa no se cumple y que lo único que aumenta con la edad son el elevado número de factores de riesgo en los pacientes de edad avanzada.[Abstract] Aphasia is a pathology secondary to cerebral vascular disease, which has a large impact on the current population. If we look at the risk factors for developing this disease can be seen that the most affected group will be to the elderly. Thus, through a literature review is to try to analyze all the features of the disease, its etiology and treatment. The aim of this work is to determine the characteristics of patients suffering from different types of aphasia and whether there is any relationship between aphasia and age, making elderly people more likely to suffer some form of this particular pathology . It can be said, after examination, the latter assumption is not met and the only thing that increases with age are the high number of risk factors in the elderly.Traballo fin de mestrado (UDC.FCS). Mestrado en Xerontoloxía. Curso 2012/2013

Structure preserving schemes for the continuum Kuramoto model: phase transitions

The construction of numerical schemes for the Kuramoto model is challenging due to the structural properties of the system which are essential in order to capture the correct physical behavior, like the description of stationary states and phase transitions. Additional difficulties are represented by the high dimensionality of the problem in presence of multiple frequencies. In this paper, we develop numerical methods which are capable to preserve these structural properties of the Kuramoto equation in the presence of diffusion and to solve efficiently the multiple frequencies case. The novel schemes are then used to numerically investigate the phase transitions in the case of identical and non identical oscillators

Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018, Accepte

Unscented Bayesian Optimization for Safe Robot Grasping

We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy. Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize. In fact, this active object exploration is the same strategy that infants do to learn optimal grasps. One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector. We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps. Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas. We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima. The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost. Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method. We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations. The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions.Comment: conference pape

Vaccination is a suitable tool in the control of Aujeszky's disease outbreaks in pigs using a Population Dynamics P systems model

Aujeszky's disease is one of the main pig viral diseases and results in considerable economic losses in the pork production industry. The disease can be controlled using preventive measures such as improved stock management and vaccination throughout the pig-rearing period. We developed a stochastic model based on Population Dynamics P systems (PDP) models for a standard pig production system to differentiate between the effects of pig farm management regimes and vaccination strategies on the control of Aujeszky's disease under several different epidemiological scenarios. Our results suggest that after confirming the diagnosis, early vaccination of most of the population (>75%) is critical to decrease the spread of the virus and minimize its impact on pig productivity. The direct economic cost of an outbreak of Aujeszky's disease can be extremely high on a previously uninfected farm (from 352-792 Euros/sow/year) and highlights the positive benefits of investing in vaccination measures to control infections. We demonstrate the usefulness of computational models as tools in the evaluation of preventive medicine programs aimed at limiting the impact of disease on animal production.This work was partially supported by FEDER project COMRDI16-1-0035-03