23,344 research outputs found
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 patologÃa, 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 caracterÃsticas de la patologÃa, su etiologÃa y su tratamiento. El objetivo de
este trabajo fue tratar de determinar las caracterÃsticas de los pacientes aquejados de
los distintos tipos de afasia y si existÃa alguna relación entre la afasia y la edad,
haciendo a las personas de edades avanzadas más propensas a sufrir algún tipo de
esta patologÃa 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 XerontoloxÃa. 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
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