Designing adaptive learning itineraries using features modelling and swarm intelligence

In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user??s success on the course. Therefore, the path can change in order to adapt itself to learners?? preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.     

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models.     

Effect of diacetyl piquerol on H+-ATPase activity of microsomes fromIpomoea purpurea

The effect of an allelopathic compound, diacetyl-piquerol on the H⁺ -ATPase activity of the microsomal fraction from the radicles of a common weedIpomoea purpurea was studied. The diacetyl-piquerol inhibited the germination and radicle growth fromI. purpurea; the radicle growth was increasingly inhibited (10% to 100%) as piquerol concentrations were raised (10 M to 1000 M). The H⁺-ATPase activity was inhibited (48%) by 500 M diacetyl-piquerol, and this inhibition was higher in plasma membrane ATPase (67.2%) than in tonoplast membrane ATPase (31.4%). Additional studies of the precise physiological mechanisms of interference caused by allelopathic compounds are needed.This study was sponsored by Project PCECCCNA-050954 of Consejo Nacional de Ciencia y Tecnología (CONACyT).     

mfERG_LAB: Software for processing multifocal electroretinography signals

The multifocal electroretinography technique consists of performing sectorized light excitation of the retina and capturing the resulting evoked potential. This provides functional localized information about the state of the retinal neurons. Analysis of multifocal electroretinography signals can be used for diagnosing different types of optic neuropathies (glaucomatous, demyelinating and ischemic ethiology). In order to obtain a reliable diagnosis, it is necessary to apply advanced processing algorithms (morphological, frequency and time-frequency analysis, etc.) to the multifocal electroretinography signal. This paper presents a software application developed in MATLAB(?) (MathWorks Inc., MA) designed to perform advanced multifocal electroretinography signal analysis and classification. This intuitive application, mfERG_LAB, is used to plot the signals, apply various algorithms to them and present the data in an appropriate format. The application's computational power and modular structure make it suitable for use in clinical settings as a powerful and innovative diagnostic tool, as well as in research and teaching settings as a means of assessing new algorithms.     

Online motion recognition using an accelerometer in a mobile device

This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.     

Stabilization of electromechanical systems via interconnection and damping assignment

In this article, we propose a general controller structure for asymptotic position regulation of electromechanical systems derived using the Interconnection and Damping Assignment Passivity‐Based Control methodology recently proposed in the literature. The controller is applicable to arbitrary fully actuated electromechanical systems with linear magnetic materials consisting of inductances, permanent magnets, and one mechanical co‐ordinate. We assume linear magnetic materials and fully actuated electrical dynamics; however, no restrictions are imposed on the particular form of the parameters that define the system dynamics, i.e. the inductance matrix, the magnetic coupling or the potential energy function. This allows us to treat—in a unified framework and without any additional simplifying assumptions—very diverse applications, including magnetic suspensions, and stepper and permanent magnet synchronous motors. Instrumental for our developments is the inclusion of ‘virtual’ couplings between the electrical and the mechanical subsystem, which is naturally suggested in this control methodology. Copyright © 2003 John Wiley & Sons, Ltd.     

Cathodoluminescence study of semiconductor oxide micro- and nanostructures grown by vapor deposition

Nano- and microstructures of SnO(2), In(2)O(3) and ZnO have been grown during thermal treatment of compacted powders under argon flow. Indium-doped SnO(2) tube-shaped structures with rectangular cross-section are obtained by adding a fraction of In(2)O(3) to the starting SnO(2) powder. In-rich nanoislands were found to grow on some edges of the tubes. ZnO nanostructures doped with Sn or Eu were grown by adding SnO(2) and Eu(2)O(3) powder, respectively, to the ZnO precursor powder. All the samples have been characterized by the emissive and cathodoluminescence (CL) modes of scanning electron microscopy. CL images from SnO(2):In and In(2)O(3):Sn tubes and islands show a higher emission from the Sn-rich structures related to oxygen deficiency. CL of doped ZnO enables to detect the presence of dopant in specific regions or structures. CL appears to be a useful technique to study optical and electronic properties of semiconductor oxide nanostructures.     

Adaptive control of a class of non-linearly parametrized systems using convexification

In this paper we deal with the problem of adaptive control of a class of non-linearly parametrized plants. The main contribution is to show that, through a well-chosen reparametrization, it is possible to transform the non-convex nonlinear parametrization into a convex one, a property that is used to design a globally stable adaptive controller.     

Physicochemical and structural effects of electrolyte‐induced delamination on polyethylene teraphthalate‐coated steel plates

This research evaluated the in situ physicochemical changes and alterations occurring in an electrolytic chromium coated steel (ECCS), surface protected by polyethylene teraphthalate (PET) copolymer, after inducing a fracture on the coating in an acid acetic‐acetate medium. The delamination was characterized in the front of the failure by means of anodic and cathodic electrochemical mechanisms, and the resistance and degradation of the metal‐polymer composite's substrates were analyzed by means of Raman vibrational spectroscopy. The application of an electrochemical cell to generate in situ delamination, simulating the formation of pores or artificial defects, provided information on the activity inside the substrates of the PET‐coated ECCS composite as a result of the effect of the acetic acid. The anodic delamination mechanism is based on the diffusion of the electrolyte through the metal‐polymer interface and the pre‐existence of pores on the polymer layer. The cathodic delamination mechanism is based on the mechanical action of the gaseous hydrogen as a result of the reduction of H⁺. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012