A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.

Dobutamine in Paediatric Population: A Systematic Review in Juvenile Animal Models

Objective: Although dobutamine is widely used in neonatal clinical practice, the evidence for its use in this specific population is not clear. We conducted a systematic review of the use of dobutamine in juvenile animals to determine whether the evidence from juvenile animal experiments with dobutamine supported the design of clinical trials in neonatal/ paediatric population. Methods: Studies were identified by searching MEDLINE (1946-2012) and EMBASE (1974-2012). Articles retrieved were independently reviewed by three authors and only those concerning efficacy and safety of the drug in juvenile animals were included. Only original articles published in English and Spanish were included. Results: Following our literature search, 265 articles were retrieved and 24 studies were included in the review: 17 focused on neonatal models and 7 on young animal models. Although the aims and design of these studies, as well as the doses and ages analysed, were quite heterogeneous, the majority of authors agree that dobutamine infusion improves cardiac output in a dose dependent manner. Moreover, the cardiovascular effects of dobutamine are influenced by postnatal age, as well as by the dose used and the duration of the therapy. There is inadequate information about the effects of dobutamine on cerebral perfusion to draw conclusions. Conclusion: There is enough preclinical evidence to ensure that dobutamine improves cardiac output, however to better understand its effects in peripheral organs, such as the brain, more specific and well designed studies are required to provide additional data to support the design of clinical trials in a paediatric population.

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.

Cosmological constrains on fast transition Unified Dark Matter models

In this work we show the results obtained applying a Unified Dark Matter (UDM) model with a fast transition to a set of cosmological data. Two different functions to model the transition are tested, and the feasibility of both models is explored using CMB shift data from Planck [1], Galaxy Clustering data from [2] and [3], and Union2.1 SNe Ia [4]. These new models are also statistically compared with the ACDM and quiessence models using Bayes factor through evidence. Bayesian inference does not discard the UDM models in favor of ACDM.

Calibrating Analytical Models for Semilocal Strings

In this work we calibrate two different analytic models of semilocal strings by constraining the values of their free parameters. In order to do so, we use data obtained from the largest and most accurate field theory simulations of semilocal strings to date, and compare several key properties with the predictions of the models. As this is still work in progress, we present some preliminary results together with descriptions of the methodology we are using in the characterisation of semilocal string networks.