Robustness of the non-Markovian Alzheimer walk under stochastic perturbation

The elephant walk model originally proposed by Schutz and Trimper to investigate non-Markovian processes led to the investigation of a series of other random-walk models. Of these, the best known is the Alzheimer walk model, because it was the first model shown to have amnestically induced persistence-i.e. superdiffusion caused by loss of memory. Here we study the robustness of the Alzheimer walk by adding a memoryless stochastic perturbation. Surprisingly, the solution of the perturbed model can be formally reduced to the solutions of the unperturbed model. Specifically, we give an exact solution of the perturbed model by finding a surjective mapping to the unperturbed model. Copyright (C) EPLA, 2012

Exact field-driven interface dynamics in the two-dimensional stochastic Ising model with helicoidal boundary conditions

We investigate the interface dynamics of the two-dimensional stochastic Ising model in an external field under helicoidal boundary conditions. At sufficiently low temperatures and fields, the dynamics of the interface is described by an exactly solvable high-spin asymmetric quantum Hamiltonian that is the infinitesimal generator of the zero range process. Generally, the critical dynamics of the interface fluctuations is in the Kardar-Parisi-Zhang universality class of critical behavior. We remark that a whole family of RSOS interface models similar to the Ising interface model investigated here can be described by exactly solvable restricted high-spin quantum XXZ-type Hamiltonians. (C) 2012 Elsevier B.V. All rights reserved.

Elderly Individuals with Diabetes: Adding Cognitive Training to Psychoeducational Intervention

The present research examined the effects of a cognitive training program combined with psychoeducational intervention for diabetic elderly patients. Specifically, it aimed at assessing the effects of an eight-session cognitive training and educational program in diabetic elderly individuals and investigating changes in their awareness about specific aspects of diabetes. The final sample consisted of 34 individuals-19 in the experimental group (EG) and 15 in the control group (CG), all residing in the eastern region of the city of Sao Paulo. The protocol included clinical and sociodemographic questions; the Diabetes Attitudes Questionnaire (ATT-19); Diabetes Knowledge Scale (DKN-A); Mini Mental State Examination (MMSE); Verbal Fluency-animal category (VF); Geriatric Depression Scale (GDS); Short Cognitive Performance Test (SKT); and the Rivermead Behavioral Memory Test (RBMT). Results pointed to a significant difference between the two groups for the ATT-19, DKN, and SKT-memory and SKT-total, and a marginally significant difference for the RBMT history in the posttest. As for the remaining cognitive variables, no changes were observed. Retest effects were not observed in the CG. We concluded that cognitive training combined with psychoeducational intervention in diabetic elderly individuals may be effective in producing cognitive gains as well as attitude and knowledge improvement concerning diabetes mellitus (DM).

An evaluation of dosimetric characteristics of MAGIC gel modified by adding formaldehyde (MAGIC-f)

Purpose: To evaluate the dosimetric characteristics of a new formulation of MAGIC gel, called MAGIC-f, which contains the addition of 3.3% formaldehyde, resulting in a gel with increased thermal stability. Methods: MAGIC-f gel was prepared and stored in hermetically sealed plastic containers. After irradiation, magnetic resonance images (MRI) were acquired to evaluate dose and dose distribution. Dosimetric characterization was performed by means of depth dose measurements, dose response sensitivity and linearity, temporal stability, energy and dose rate dependence, dose integration using sequential beams, temperature influence during MRI acquisition and dose distribution integrity. Results: MAGIC-f depth dose measurements are compatible with the dosimetric table data within +/- 4% uncertainty. The dosimeter's R-2 response varies linearly with dose at least from 0 to 6 Gy. The time-course of the sensitivity of the dosimeter following irradiation, indicated stabilization after 2 weeks. The dosimeter's response to irradiation was altered by 6% when increasing the energy from cobalt beams to 10 MV beams. The dose rate dependence of this new formulation of gel dosimeter is small: less than 2.5% for a variation from 200 to 500 cGy/min, and the dependence with the fractionation scheme is about 50% smaller than for standard MAGIC gel, The dependence on scanning temperature was also verified, and the integrity of the dose distribution was confirmed for a period of 90 days. Conclusions: The results demonstrate the applicability of this new dosimeter in tridimensional dose distribution measurements. (C) 2012 Elsevier Ltd. All rights reserved.

Electromagnetic torque ripple and copper losses reduction in permanent magnet synchronous machines

This paper presents a method for electromagnetic torque ripple and copper losses reduction in (non-sinusoidal or trapezoidal) surface-mount permanent magnet synchronous machines (SM-PMSM). The method is based on an extension of classical dq transformation that makes it possible to write a vectorial model for this kind of machine (with a non-sinusoidal back-EMF waveform). This model is obtained by the application of that transformation in the classical machine per-phase model. That transformation can be applied to machines that have any type of back-EMF waveform, and not only trapezoidal or square-wave back-EMF waveforms. Implementation results are shown for an electrical converter, using the proposed vectorial model, feeding a non-sinusoidal synchronous machine (brushless DC motor). They show that the use of this vectorial mode is a way to achieve improvements in the performance of this kind of machine, considering the electromagnetic torque ripple and copper losses, if compared to a drive system that employs a classical six-step mode as a converter. Copyright (C) 2011 John Wiley & Sons, Ltd.

A spatial stochastic model for rumor transmission

We consider an interacting particle system representing the spread of a rumor by agents on the d-dimensional integer lattice. Each agent may be in any of the three states belonging to the set {0,1,2}. Here 0 stands for ignorants, 1 for spreaders and 2 for stiflers. A spreader tells the rumor to any of its (nearest) ignorant neighbors at rate lambda. At rate alpha a spreader becomes a stifler due to the action of other (nearest neighbor) spreaders. Finally, spreaders and stiflers forget the rumor at rate one. We study sufficient conditions under which the rumor either becomes extinct or survives with positive probability.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

Period adding cascades: Experiment and modeling in air bubbling

Period adding cascades have been observed experimentally/numerically in the dynamics of neurons and pancreatic cells, lasers, electric circuits, chemical reactions, oceanic internal waves, and also in air bubbling. We show that the period adding cascades appearing in bubbling from a nozzle submerged in a viscous liquid can be reproduced by a simple model, based on some hydrodynamical principles, dealing with the time evolution of two variables, bubble position and pressure of the air chamber, through a system of differential equations with a rule of detachment based on force balance. The model further reduces to an iterating one-dimensional map giving the pressures at the detachments, where time between bubbles come out as an observable of the dynamics. The model has not only good agreement with experimental data, but is also able to predict the influence of the main parameters involved, like the length of the hose connecting the air supplier with the needle, the needle radius and the needle length. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3695345]

EFFECTS OF ADDING LANTHANUM TO Ni/ZrO2 CATALYSTS ON ETHANOL STEAM REFORMING

EFFECTS OF ADDING LANTHANUM TO Ni/ZrO2 CATALYSTS ON ETHANOL STEAM REFORMING. The catalytic performance of Ni/ZrO2 catalysts loaded with different lanthanum content for steam reforming of ethanol was investigated. Catalysts were characterized by BET surface area, X-ray diffraction, UV-vis spectroscopy, temperature programmed reduction, and X-ray absorption fine structure techniques. Results showed that lanthanum addition led to an increase in the degree of reduction of both NiO and nickel surface species interacting; with the support, due to the higher dispersion effect. The best catalytic performance at 450 degrees C was found for the Ni/2LZ catalyst, which exhibited an effluent gaseous mixture with the highest H-2 yield.

Adding value to the Brazilian sisal: acid hydrolysis of its pulp seeking production of sugars and materials

The present work is inserted into the broad context of the upgrading of lignocellulosic fibers. Sisal was chosen in the present study because more than 50% of the world's sisal is cultivated in Brazil, it has a short life cycle and its fiber has a high cellulose content. Specifically, in the present study, the subject addressed was the hydrolysis of the sisal pulp, using sulfuric acid as the catalyst. To assess the influence of parameters such as the concentration of the sulfuric acid and the temperature during this process, the pulp was hydrolyzed with various concentrations of sulfuric acid (30-50%) at 70 A degrees C and with 30% acid (v/v) at various temperatures (60-100 A degrees C). During hydrolysis, aliquots were withdrawn from the reaction media, and the solid (non-hydrolyzed pulp) was separated from the liquid (liquor) by filtering each aliquot. The sugar composition of the liquor was analyzed by HPLC, and the non-hydrolyzed pulps were characterized by viscometry (average molar mass), and X-ray diffraction (crystallinity). The results support the following conclusions: acid hydrolysis using 30% H2SO4 at 100 A degrees C can produce sisal microcrystalline cellulose and the conditions that led to the largest glucose yield and lowest decomposition rate were 50% H2SO4 at 70 A degrees C. In summary, the study of sisal pulp hydrolysis using concentrated acid showed that certain conditions are suitable for high recovery of xylose and good yield of glucose. Moreover, the unreacted cellulose can be targeted for different applications in bio-based materials. A kinetic study based on the glucose yield was performed for all reaction conditions using the kinetic model proposed by Saeman. The results showed that the model adjusted to all 30-35% H2SO4 reactions but not to greater concentrations of sulfuric acid. The present study is part of an ongoing research program, and the results reported here will be used as a comparison against the results obtained when using treated sisal pulp as the starting material.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.

Reforming of a model biogas on Ni and Rh-Ni catalysts: Effect of adding La

Ni catalysts supported on gamma-Al2O3 modified by Rh and La were prepared and evaluated on the reforming of a model biogas. The catalysts were characterized by EDS, XRD, TPR, XANES and surface area estimation (BET). The results showed that in the original Ni catalyst, the Ni interacted strongly with the alumina support, exhibiting high reduction temperatures in TPR tests. In the catalytic tests, the addition of Rh on Ni catalysts improved CH4 conversion but also increased carbon deposition, possible by causing the segregation of Ni species under the reaction conditions. The presence of La on Ni catalysts reduced the carbon deposition by favoring the gasification of carbon species. Addition of synthetic air to the process improved the CH4 conversion and also decreased the carbon formation. The catalysts Ni, Rh-NiLa, and Rh showed good results in the conversion of model sulfur-free biogas, which suggests that they are promising catalysts to be tested in conversion of real biogas. (C) 2012 Elsevier B.V. All rights reserved.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

A Chaotic Approach of Differential Evolution Optimization Applied to Loudspeaker Design Problem

Over the past few years, the field of global optimization has been very active, producing different kinds of deterministic and stochastic algorithms for optimization in the continuous domain. These days, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. Differential evolution (DE) algorithms are a family of evolutionary optimization techniques that use a rather greedy and less stochastic approach to problem solving, when compared to classical evolutionary algorithms. The main idea is to construct, at each generation, for each element of the population a mutant vector, which is constructed through a specific mutation operation based on adding differences between randomly selected elements of the population to another element. Due to its simple implementation, minimum mathematical processing and good optimization capability, DE has attracted attention. This paper proposes a new approach to solve electromagnetic design problems that combines the DE algorithm with a generator of chaos sequences. This approach is tested on the design of a loudspeaker model with 17 degrees of freedom, for showing its applicability to electromagnetic problems. The results show that the DE algorithm with chaotic sequences presents better, or at least similar, results when compared to the standard DE algorithm and other evolutionary algorithms available in the literature.