Modelling the secular evolution of migrating planet pairs

The subject of this paper is the secular behaviour of a pair of planets evolving under dissipative forces. In particular, we investigate the case when dissipative forces affect the planetary semimajor axes and the planets move inwards/outwards the central star, in a process known as planet migration. To perform this investigation, we introduce fundamental concepts of conservative and dissipative dynamics of the three-body problem. Based on these concepts, we develop a qualitative model of the secular evolution of the migrating planetary pair. Our approach is based on the analysis of the energy and the orbital angular momentum exchange between the two-planet system and an external medium; thus no specific kind of dissipative forces is invoked. We show that, under the assumption that dissipation is weak and slow, the evolutionary routes of the migrating planets are traced by the Mode I and Mode II stationary solutions of the conservative secular problem. The ultimate convergence and the evolution of the system along one of these secular modes of motion are determined uniquely by the condition that the dissipation rate is sufficiently smaller than the proper secular frequency of the system. We show that it is possible to reassemble the starting configurations and the migration history of the systems on the basis of their final states and consequently to constrain the parameters of the physical processes involved.

Outer Trust-Region Method for Constrained Optimization

Given an algorithm A for solving some mathematical problem based on the iterative solution of simpler subproblems, an outer trust-region (OTR) modification of A is the result of adding a trust-region constraint to each subproblem. The trust-region size is adaptively updated according to the behavior of crucial variables. The new subproblems should not be more complex than the original ones, and the convergence properties of the OTR algorithm should be the same as those of Algorithm A. In the present work, the OTR approach is exploited in connection with the ""greediness phenomenon"" of nonlinear programming. Convergence results for an OTR version of an augmented Lagrangian method for nonconvex constrained optimization are proved, and numerical experiments are presented.

Study of some physical properties of biodegradable films based on blends of gelatin and poly(vinyl alcohol) using a response-surface methodology

The aim of this work was to study the effect of the hydrolysis degree (HD) and the concentration (C(PVA)) Of two types of poly(vinyl alcohol) (PVA) and of the type (glycerol and sorbitol) and the concentration (C(P)) of plasticizers on some physical properties of biodegradable films based on blends of gelatin and PVA Using a response-surface methodology. The films were prepared with a film forming solutions (FFS) with 2 g of macromolecules (gelatin+PVA)/100 g de FFS. The responses analyzed were the mechanical properties, the solubility, the moisture Content. the color difference and the opacity. The linear model was statistically significant and predictive for puncture force and deformation. elongation at break, solubility in water, Moisture content and opacity. The CPVA affected strongly the elongation at break of the films. The interaction of the HD and the C(P) affected this property. Moreover. the puncture force was affected slightly by the C(PVA). Concerning the Solubility in water, the reduction of the HD increased it and this effect was greater for high CPVA Values. In general. the most important effect observed in the physical properties of the films was that of the plasticizer type and concentration. The PVA hydrolysis degree and concentration have an important effect only for the elongation at break, puncture deformation and solubility in water. (C) 2008 Elsevier B.V. All rights reserved.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Sustainable vegetable crop supply problem

We consider an agricultural production problem, in which one must meet a known demand of crops while respecting ecologically-based production constraints. The problem is twofold: in order to meet the demand, one must determine the division of the available heterogeneous arable areas in plots and, for each plot, obtain an appropriate crop rotation schedule. Rotation plans must respect ecologically-based constraints such as the interdiction of certain crop successions, and the regular insertion of fallows and green manures. We propose a linear formulation for this problem, in which each variable is associated with a crop rotation schedule. The model may include a large number of variables and it is, therefore, solved by means of a column-generation approach. We also discuss some extensions to the model, in order to incorporate additional characteristics found in field conditions. A set of computational tests using instances based on real-world data confirms the efficacy of the proposed methodology. (C) 2009 Elsevier B.V. All rights reserved.

Realistic calculations of carbon-based disordered systems

Carbon nanotubes rank amongst potential candidates for a new family of nanoscopic devices, in particular for sensing applications. At the same time that defects in carbon nanotubes act as binding sites for foreign species, our current level of control over the fabrication process does not allow one to specifically choose where these binding sites will actually be positioned. In this work we present a theoretical framework for accurately calculating the electronic and transport properties of long disordered carbon nanotubes containing a large number of binding sites randomly distributed along a sample. This method combines the accuracy and functionality of ab initio density functional theory to determine the electronic structure with a recursive Green`s functions method. We apply this methodology on the problem of nitrogen-rich carbon nanotubes, first considering different types of defects and then demonstrating how our simulations can help in the field of sensor design by allowing one to compute the transport properties of realistic nanotube devices containing a large number of randomly distributed binding sites.

A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem

In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related to the dose-response model. A hierarchical formulation is provided so that a Markov chain Monte Carlo approach is developed. The methodology is applied to simulated and real data.

Applying sequential injection analysis (SIA) and response surface methodology for optimization of Fenton-based processes

This work presents the use of sequential injection analysis (SIA) and the response surface methodology as a tool for optimization of Fenton-based processes. Alizarin red S dye (C.I. 58005) was used as a model compound for the anthraquinones family. whose pigments have a large use in coatings industry. The following factors were considered: [H(2)O(2)]:[Alizarin] and [H(2)O(2)]:[FeSO(4)] ratios and pH. The SIA system was designed to add reagents to the reactor and to perform on-line sampling of the reaction medium, sending the samples to a flow-through spectrophotometer for monitoring the color reduction of the dye. The proposed system fed the statistical program with degradation data for fast construction of response surface plots. After optimization, 99.7% of the dye was degraded and the TOC content was reduced to 35% of the original value. Low reagents consumption and high sampling throughput were the remarkable features of the SIA system. (C) 2008 Published by Elsevier B.V.