Reactivity of Nellore steers in two feedlot housing systems and its relationship with plasmatic cortisol

To evaluate reactivity to assess the temperament of Nellore steers in two feedlot housing systems (group pen or individual pen) and its relationship with plasmatic cortisol, 36 experimental units were observed five times at 28-day intervals of weight management during a 112-day feedlot confinement. A reactivity score scale ranging from 1 to 5 was applied when an animal was in the chute system. To the calmest animal, a reactivity score of 1 was ascribed and to the most agitated, 5. Blood samples were collected for cortisol analysis. No differences were found in reactivity and feedlot system. There was a relationship noted between reactivity and feedlot time in both housing systems (P < 0.01). There was a relation between reactivity and cortisol levels for group animals (P = 0.0616) and for individual ones (P < 0.01). Cortisol levels varied among housing systems (P < 0.01). Feedlot time influenced the cortisol levels (P < 0.09 individual; P < 0.01 group) and when variable time was included, these levels changed, decreasing in the group pen and increasing in individual pens. The continuous handling reduces reactivity and plasmatic cortisol, and group pen system seems to be less stressfully than individual pens. (C) 2010 Elsevier B.V. All rights reserved.

THE POINCARE-BENDIXSON THEOREM ON THE KLEIN BOTTLE FOR CONTINUOUS VECTOR FIELDS

We present a version of the Poincare-Bendixson Theorem on the Klein bottle K(2) for continuous vector fields. As a consequence, we obtain the fact that K(2) does not admit continuous vector fields having a omega-recurrent injective trajectory.

UNIFORM DISSIPATIVENESS, ROBUST SYNCHRONIZATION AND LOCATION OF THE ATTRACTOR OF PARAMETRIZED NONAUTONOMOUS DISCRETE SYSTEMS

In this series of papers, we study issues related to the synchronization of two coupled chaotic discrete systems arising from secured communication. The first part deals with uniform dissipativeness with respect to parameter variation via the Liapunov direct method. We obtain uniform estimates of the global attractor for a general discrete nonautonomous system, that yields a uniform invariance principle in the autonomous case. The Liapunov function is allowed to have positive derivative along solutions of the system inside a bounded set, and this reduces substantially the difficulty of constructing a Liapunov function for a given system. In particular, we develop an approach that incorporates the classical Lagrange multiplier into the Liapunov function method to naturally extend those Liapunov functions from continuous dynamical system to their discretizations, so that the corresponding uniform dispativeness results are valid when the step size of the discretization is small. Applications to the discretized Lorenz system and the discretization of a time-periodic chaotic system are given to illustrate the general results. We also show how to obtain uniform estimation of attractors for parametrized linear stable systems with nonlinear perturbation.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.