Tentative zinc biofortification of banana fruit via bunch spray and bunch stalk feeding

Due to the importance of banana consumption in developing countries and producing zinc-rich foods, the objective of this study was to evaluate the feasibility of banana bunch sprays and bunch stalk feeding with Zn aiming to biofortify this fruit. Zn sprays were carried out with a dose of 80 g ha⁻¹ using ZnCl₂ and ZnCl₂ combined with a chelating agent in two banana cultivars (‘Maravilha’ and ‘FHIA 18’). Bunch stalk feeding with Zn was carried out by fixing plastic bags on the stem end containing solutions of ZnSO₄ (0, 0.5, 1.0 and 2.0 g L⁻¹) in two banana cultivars (‘Williams’ and ‘Prata Anã’). Both strategies resulted in a maximum fruit Zn content of 0.14 mg 100 g⁻¹. Although it was possible to increase the Zn content in banana fruit from ‘FHIA 18’ and ‘Prata Anã’ cultivars, both strategies did not result in biofortified banana fruit with a Zn level which would fulfil the Zn dietary reference intake (DRI) of 2 mg dia⁻¹ for children. Banana bunch sprays with Zn did not affect the fruit quality, but bunch stalk feeding with Zn resulted in fruits with lower ratio, pH and ascorbic acid content.     

Human features recognition with CNM: An applied study concerning undergraduate students

In this paper we apply the Combinatorial Neural Model (CNM) to help scientists in Applied Social Sciences to understand a proactive agent in relation to the social environment. CNM is a hybrid neural network that represents an alternative to overcome the black box limitation of the Multilayer Perceptron by applying the neural network structure along with a symbolic processing. The model built comprises the recognition of patterns from a socioeconomic survey and from a set of written texts, and aims at understanding the student point of view about its role in the society. The self-perception of the young in relation to willingness for social proactivity was studied. Proactivity was taken as the possibility of transforming society from the perspective of social inequality. The whole process was driven under CRISP-DM guidelines. The model succeeded in identifying different rules that characterize non-proactive students. Results show that current approach is useful for subsidizing educators and managers of educational institutions in decision making with information on students’ profile.     

A comparative study of high temperature properties of cobalt-free perovskites

Co-free perovskites with chemical composition Ba_0.5Sr_0.5Fe_0.8M_0.2O_3-δ (M = Ni, Cu, Zn) were synthesized by the modified Pechini method, and their structure and microstructure were characterized by XRD and SEM. Oxygen content, electrical resistivity and Thermal Expansion Coefficient (TEC) were evaluated in air between room temperature and 900 °C. The high-temperature properties of these perovskites were compared with those of Co containing Ba_0.5Sr_0.5Fe_0.8Co_0.2O_3-δ perovskite. The highest electrical conductivity was obtained for Ba_0.5Sr_0.5Fe_0.8Cu_0.2O_3-δ, with values of 47.6 Scm^?1 at 544 °C. This same composition also exhibits the highest oxygen vacancies concentration: 3-δ = 2.61 at room temperature. In contrast, the Ba_0.5Sr_0.5Fe_0.8Zn_0.2O_3-δ, showed lower electrical conductivity suggesting that the Zn⁺² ions block electron transport. Co-free perovskites seem to be stable at high temperatures for long term periods. However, these compounds suffered degradation at room temperature in samples stored in air.     

An inverse analysis of the hot uniaxial compression test by means of the finite element method

A computer simulation using the finite element method is carried out in the present work to model a hot compression test. Real constitutive equations of the hot flow behaviour of a medium carbon microalloyed steel, experimentally determined, are implemented in a commercial computer code and an inverse analysis is performed to determine under which conditions the experimental test can be considered as valid. In other words, the degree of strain and strain rate heterogeneity as well as the isothermatility of the test, for the given material, are verified. The effect of the friction coefficient is also included in the current analysis.     

Genetic fuzzy rule-based scheduling system for grid computing in virtual organizations

One of the most challenging problems when facing the implementation of computational grids is the system resources effective management commonly referred as to grid scheduling. A rule-based scheduling system is presented here to schedule computationally intensive Bag-of-Tasks applications on grids for virtual organizations. There exist diverse techniques to develop rule-base scheduling systems. In this work, we suggest the joining of a gathering and sorting criteria for tasks and a fuzzy scheduling strategy. Moreover, in order to allow the system to learn and thus to improve its performance, two different off-line optimization procedures based on Michigan and Pittsburgh approaches are incorporated to apply Genetic Algorithms to the fuzzy scheduler rules. A complex objective function considering users differentiation is followed as a performance metric. It not only provides the conducted system evaluation process a comparison with other classical approaches in terms of accuracy and convergence behaviour characterization, but it also analyzes the variation of a wide set of evolution parameters in the learning process to achieve the best performance.     

Mean Quantum Sojourn Time

This article introduces the notion of Mean Quantum Sojourn Time for a Quantum Dynamical Semigroup acting over an arbitrary von Neumann algebra. This notion is used to analyze the asymptotic behaviour of the underlying dynamics and allows one to include, as a particular case, earlier classification of states obtained in scattering theory. Furthermore, certain connections with convergence towards an equilibrium, as well as with spectral-type measures, are studied.     

Web Application Commercial Design for Financial Entities Based on Business Intelligence

Multiple customer data management has become a focus of attention in big organizations. Although much information is available, it does not translate into significant profitable value-added services. We present a design of a commercial web application based on business intelligence that generates information on social and financial behavior of clients in an organization; with the purpose of obtain additional information that allows to get more profits. This app will provide a broader perspective for making strategic decisions to increase profits and reduce internal investment costs. A case in point is the financial sector, a group of financial entities were used to make measurements and test them. A design to build a web application aimed at achieving a large and ambitious goal by means of defined tools reflecting clients’ business needs is proposed. In this research, different techniques and technologies are explored, such as diagrams, frameworks, design, architecture, model entity-relationship, tables, equations, mental maps and development tools. Through the Personal Software Process methodology and with the help of information extraction, consolidation, and visualization, the implementation can be carried out. This article provides the importance of implementing business intelligence in an organization and expands on the steps needed for the implementation of this valuable technology.     

Virtual center for renal support: technological approach to patient physiological image

The patient physiological image (PPI) is a novel concept which manages the knowledge of the virtual center for renal support (VCRS), currently being developed by the Biomedical Engineering Group of the University of Seville. PPI is a virtual "replica" of the patient, built by means of a mathematical model, which represents several physiological subsystems of a renal patient. From a technical point of view, PPI is a component-oriented software module based on cutting-edge modeling and simulation technology. This paper provides a methodological and technological approach to the PPI. Computational architecture of PPI-based VCRS is also described. This is a multi-tier and multi-protocol system. Data are managed by several ORDBMS instances. Communications design is based on the virtual private network (VPN) concept. Renal patients have a minimum reliable access to the VCRS through a public switch telephone network--X.25 gateway. Design complies with the universal access requirement, allowing an efficient and inexpensive connection even in rural environments and reducing computational requirements in the patient's remote access unit. VCRS provides support for renal patients' healthcare, increasing the quality and quantity of monitored biomedical signals, predicting events as hypotension or low dialysis dose, assisting further to avoid them by an online therapy modification and easing diagnostic tasks. An online therapy adjustment experiment simulation is presented. Finally, the presented system serves as a computational aid for research in renal physiology. This is achieved by an open and reusable modeling and simulation architecture which allows the interaction among models and data from different scales and computer platforms, and a faster transference of investigation models toward clinical applications.     

Multivariate time series modeling and classification via hierarchical VAR mixtures

A novel class of models for multivariate time series is presented. We consider hierarchical mixture-of-expert (HME) models in which the experts, or building blocks of the model, are vector autoregressions (VAR). It is assumed that the VAR-HME model partitions the covariate space, specifically including time as a covariate, into overlapping regions called overlays. In each overlay a given number of VAR experts compete with each other so that the most suitable one for the overlay is favored by a large weight. The weights have a particular parametric form that allows the modeler to include relevant covariates. Estimation of the model parameters is achieved via the EM (expectation-maximization) algorithm. A new algorithm to select the optimal number of overlays, the number of VAR models and the model orders of the VARs that define a particular VAR-HME model configuration, is also developed. The algorithm uses the Bayesian information criterion (BIC) as an optimality criterion. Issues of model checking and inference of latent structure in multiple time series are investigated. The new methodology is illustrated by analyzing a synthetic data set and a 7-channel electroencephalogram data set.     

Enhanced grasshopper optimization algorithm with extreme learning machines for motor-imagery classification

In Brain Computer Interface (BCI), achieving a reliable motor-imagery classification is a challenging task. The set of discriminative and relevant feature vectors plays a crucial role in classification. In this article, an enhanced optimization technique is implemented for selecting active feature vectors to enhance motor-imagery classification using Electroencephalography (EEG) signals. After collecting the input EEG signals from BCI competition III-4a and IV-2a databases, the 6th-order butter-worth filter is employed for eliminating base-line wander noise from the raw EEG signals. Further, the Variational Mode Decomposition technique is applied for separating the important signal components from the composite EEG signals, and then, the Higher Order Statistic, kurtosis, skewness, standard deviation, and entropy are utilized for feature extraction. The high-dimensional feature values are given to the Enhanced Grasshopper Optimization Algorithm for optimum feature selection, which are given to the Extreme Learning Machines (ELM) classifier for motor-imagery classification. Finally, in the resulting section, the optimized ELM model achieved 99.48% and 99.12% of accuracy on the BCI competition III-4a and IV-2a databases, where the achieved results are maximum compared to the traditional deep learning models.