Uniaxial-deformation behavior of ice Ih as described by the TIP4P/Ice and mW water models

Using molecular dynamics simulations, we assess the uniaxial deformation response of ice Ih as described by two popular water models, namely, the all-atom TIP4P/Ice potential and the coarse-grained mW model. In particular, we investigate the response to both tensile and compressive uniaxial deformations along the [0001] and [01̄10] crystallographic directions for a series of different temperatures. We classify the respective failure mechanisms and assess their sensitivity to strain rate and cell size. While the TIP4P/Ice model fails by either brittle cleavage under tension at low temperatures or large-scale amorphization/melting, the mW potential behaves in a much more ductile manner, displaying numerous cases in which stress relief involves the nucleation and subsequent activity of lattice dislocations. Indeed, the fact that mW behaves in such a malleable manner even at strain rates that are substantially higher than those applied in typical experiments indicates that the mW description of ice Ih is excessively ductile. One possible contribution to this enhanced malleability is the absence of explicit protons in the mW model, disregarding the fundamental asymmetry of the hydrogen bond that plays an important role in the nucleation and motion of lattice dislocations in ice Ih.Fil: Santos Flórez, Pedro Antonio. Universidade Estadual de Campinas; BrasilFil: Ruestes, Carlos Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: de Koning, Maurice. Universidade Estadual de Campinas; Brasi

De novo analysis of the haustorial transcriptome of the cucurbit powdery mildew fungus Podosphaera xanthii reveals new candidate secreted effector proteins

Backgrounds. Cucurbit crops are affected, among other pathogens, by the obligate biotrophic fungus Podosphaera xanthii, the main causal agent of powdery mildew in cucurbits. This fungus develops a specialized structure of parasitism termed haustorium. Haustoria are developed into epidermal cells and are responsible for nutrients uptake and effectors delivery. Objectives. The aim of this study was to obtain the haustorial transcriptome of P. xanthii to complete the panel of effector candidates of this fungal pathogen. Methods. To obtain the haustorial transcriptome, we have developed an effective method for isolation of haustoria without contaminants by flow cytometry. The cDNA library was built using a combination of dT primers and random primers followed by a depletion of ribosomal sequences. Sequencing was carried out by Illumina NextSeq550. Conclusions. After bioinformatic analysis, we were able to identify 25 new effector candidates secreted by the classic pathway (with signal peptide) and 269 new candidates secreted by the non-classic pathway (without signal peptide). Most proteins had no functional annotation. By protein modelling and ligand predictions, we are now being able to assign putative functions to some of these candidates to select those with potential roles in pathogenesis for subsequent functional in vivo analysis by HIGS (host-induced gene silencing). By these approaches, we are starting to shed some light into the molecular mechanisms of pathogenesis in this very important pathogen of cucurbits.This work was supported by a grant from the Ministerio de Economía y Competitividad (AGL2013-41938-R), co-financed with FEDER funds (EU). A grant form Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech, is also acknowledged

Competitive Advantages as a Complete Mediator Variable in Strategic Resources, Dynamic Capabilities and Performance Relations in the Car Sales Sector

Taking the resource-based view –RBV- and the dynamic capability view –DCV- as an orientation, the main aim of this study is to develop the mediator role that competitive advantages play in the relations between strategic resources, dynamic capabilities and performance. The study takes place in a dynamic and changing sector: the sale of new cars in Portugal. The results show that (a) achieving competitive advantages, which are decisive for business results, depends on the available strategic resources and the generating of dynamic capabilities, (b) in dynamic and changing sectors strategic resources are essential to generate dynamic capabilities, (c) firms must center their attention on, more than results, the generating of sustainable competitive advantages as these act as a mediator variable of the effect of strategic resources and dynamic capabilities on performance. The data scrutiny uses structural equation modeling (SEM) through PLS as the statistical instrument. The sample comprises 89 firms which sell new cars in Portugal

An enhanced classifier system for autonomous robot navigation in dynamic environments

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad

Hierarchical genetic algorithms for composite laminate panels stress optimisation

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.Genetic algorithms (GAs) have demonstrated to be a powerful technique for solving optimisation problems. In this article, the problem of optimising the number of plies and their stacking sequence in the design of laminated composite panels is considered. This problem has special features that makes it different from traditional problems in which GAs have been applied, which make the problem a multiobjective optimisation one. Symmetry and equilibrium constraints have also been included in the solution. A modification of the canonical GA is needed and a new perspective for solving this problem by using GA techniques is introduced

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad

Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad