Building a Open Source Framework for Virtual Medical Training

This paper presents a framework to build medical training applications by using virtual reality and a tool that helps the class instantiation of this framework. The main purpose is to make easier the building of virtual reality applications in the medical training area, considering systems to simulate biopsy exams and make available deformation, collision detection, and stereoscopy functionalities. The instantiation of the classes allows quick implementation of the tools for such a purpose, thus reducing errors and offering low cost due to the use of open source tools. Using the instantiation tool, the process of building applications is fast and easy. Therefore, computer programmers can obtain an initial application and adapt it to their needs. This tool allows the user to include, delete, and edit parameters in the functionalities chosen as well as storing these parameters for future use. In order to verify the efficiency of the framework, some case studies are presented.

A study on chemical constituents and sugars extraction from spent coffee grounds

Spent coffee grounds (SCG) the residual materials obtained during the processing of raw coffee powder to prepare instant coffee are the main coffee Industry residues In the present work this material was chemically characterized and subsequently submitted to a dilute acid hydrolysis aiming to recover the hemicellulose sugars Reactions were performed according to experimental designs to verify the effects of the variables H(2)SO(4) concentration liquid-to-solid ratio temperature and reaction time on the efficiency of hydrolysis SCG was found to be rich in sugars (45 3% w/w) among of which hemicellulose (constituted by mannose galactose and arabinose) and cellulose (glucose homopolymer) correspond to 367% (w/w) and 8 6% (w/w) respectively Optimal conditions for hemicellulose sugars extraction consisted in using 100 mg acid/g dry matter 10g liquid/g solid at 163 degrees C for 45 min Under these conditions hydrolysis efficiencies of 100% 774% and 895% may be achieved for galactan mannan and arabinan respectively corresponding to a hemicellulose hydrolysis efficiency of 874% (C) 2010 Elsevier Ltd All rights reserved

Extraction of manganese peroxidase produced by Lentinula edodes

Lentinula edodes, commonly called shiitake, is considered a choice edible mushroom with exotic taste and medicinal quality. L. edodes grows very well and produces a range of enzymes when cultivated on eucalyptus residues. Development of appropriate experimental procedures for recovery and determination of enzymes became a widely important cash crop. In this work, enzymes produced by L. edodes were extracted using different pH buffer and determined regarding peroxidases and proteases. Lignin peroxidase (LiP) was not detected in the extracts based on veratryl alcohol or azure B oxidation. Proteases were very low while Mn-peroxidases (MnP) predominated. The optimal pH for MnP recovery was 5.0, under agitation at 25 degrees C. The oxidation of phenol red decreased after dark-colored small compounds or ions were eliminated by dialysis. The extract of L. edodes contained components of high molecular weight, such as proteases or high polyphenol, that could be involved in the LiP inactivation. L. edodes sample previously submitted to dialysis was also joined to UP of Phanerochaete chrysosporium and a total inhibition of UP was observed. (C) 2007 Elsevier Ltd. All rights reserved.

Search for optimum conditions of sugarcane bagasse hemicellulose extraction

A process has been elaborated for one-step low lignin content sugarcane bagasse hemicellulose extraction using alkaline solution of hydrogen peroxide. To maximize the hemicellulose yields several extraction conditions were examined applying the 2(4) factorial design: H(2)O(2) concentration from 2 to 6% (w/v), reaction time from 4 to 16 h, temperature from 20 to 60 degrees C, and magnesium sulfate absence or presence (0.5%, w/v). This approach allowed selection of conditions for the extraction of low and high lignin content hemicellulose. At midpoint the yield of hemicellulose was 94.5% with more than 88% of lignin removed. Lignin removal is suppressed at low extraction temperatures and in the absence of magnesium sulfate. Hemicellulose in 86% yield with low lignin content (5.9%) was obtained with 6% H(2)O(2) treatment for 4 h and 20 degrees C. This hemicellulose is much lighter in color than samples obtained at the midpoint condition and was found suitable for subsequent enzymatic hydrolysis. (C) 2009 Elsevier B.V. All rights reserved.

Total Soluble Solids from Banana: Evaluation and Optimization of Extraction Parameters

Banana, an important component in the diet of the global population, is one of the most consumed fruits in the world. This fruit is also very favorable to industry processes (e. g., fermented beverages) due to its rich content on soluble solids and minerals, with low acidity. The main objective of this work was to evaluate the influence of factors such as banana weight and extraction time during a hot aqueous extraction process on the total soluble solids content of banana. The extract is to be used by the food and beverage industries. The experiments were performed with 105 mL of water, considering the moisture of the ripe banana (65%). Total sugar concentrations were obtained in a beer analyzer and the result expressed in degrees Plato (degrees P, which is the weight of the extract or the sugar equivalent in 100 g solution at 20 degrees C), aiming at facilitating the use of these results by the beverage industries. After previous studies of characterization of the fruit and of ripening performance, a 2(2) full-factorial star design was carried out, and a model was developed to describe the behavior of the dependent variable (total soluble solids) as a function of the factors (banana weight and extraction time), indicating as optimum conditions for extraction 38.5 g of banana at 39.7 min.

Aqueous two-phase extraction using thermoseparating copolymer: a new system for phenolic compounds removal from hemicelullosic hydrolysate

BACKGROUND: The hydrolysis of hemicellulosic material can provide liquor with high xylose concentration (which can be used as a fermentation medium) and phenolic compounds (Phs), potentially immunostimulating compounds. However, these hydrolysates must be detoxified in order to remove the Phs that can act as inhibitors in bioconversions. RESULTS: Aqueous two-phase systems composed of thermoseparating copolymers were used for rice straw hydrolysate detoxification. The hydrolysis process was able to promote chemical breakdown of 85% of the total hemicellulose content, 14% of the cellulose, and 2% of the lignin. The hydrolysate obtained contained 19.7 g L-1 of xylose and several phenolic compounds, such as vanillin, vanillic acid, ferullic acid, etc. The phenolics extraction was studied as a function of copolymer molar mass (1100 g mol(-1), 2000 g mol(-1) and 2800 g mol(-1)), their percentages (from 5% to 50%) and Phs initial concentration. Phenolic compounds extraction of around 80% was obtained under the following conditions: 20% (w/w) and 35% (w/w) copolymer 1100 g mol-1, 35% (w/w) copolymer 2000 g mol(-1) and 35% (w/w) copolymer 2800 g mol(-1) at 25 degrees C. CONCLUSIONS: The results demonstrated the viability of this method for the removal of Phs from rice straw hydrolysate, which has potential uses in bioconversion processes. (c) 2007 Society of Chemical Industry.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Dimension stone for building facades: methodology for structural design

The importance of a careful selection of rocks used in building facade cladding is highlighted. A simple and viable methodology for the structural detailing of dimension stones and the verification of the global performance is presented based on a Strap software simulation. The results obtained proved the applicability of the proposed structural dimensioning methodology which represents an excellent simple tool for dimensioning rock slabs used for building facade cladding. The Strap software satisfactorily simulated the structural conditions of the stone slabs under the studied conditions, allowing the determination of alternative slab dimensions and the verification of the cladding strength at the support.

Performance evaluation of dual-flush WC cistern in a multifamily building in Brazil

The performance assessment as to water consumption in WC cisterns has contributed to the development of flushing system technologies, which allow smaller flushing volumes. The purpose of this work is to assess the performance of the the low water consumption requirement of WC cisterns with dual flushing system (6/3L), when compared to 6L flushing volume WC cisterns in multifamily buildings. The research methodology consisted of a case study in a multifamily residential building with submetering system, by monitoring the total water consumption and the two flushing systems using water meters installed in WC cisterns. By means of a mathematical model, a comparison of the design flowrate in the main branch was carried out considering the two types of WC cisterns. The results indicated that the water consumption in the 6L WC cistern was 20% in relation to the total domestic consumption, whereas the water consumption observed in the dual-flush WC cistern (6/3L) was 16%. The dual flushing system (6/3L) presented about 18% consumption reduction impact as compared to the 6 L system. The design flowrate values in the main branch, obtained by the mathematical model, were 0.35 L/s for systems with 6 L WC cistern and 0.34 L/s with dual-flush WC cistern (6/3 L), that is, a reduction of similar to 3%. Practical application: The knowledge of the performance in field of dual-flush WC cistern contributes to industry to improve this system and to users to aid their choice of technologies aimed at water conservation, and so assisting to the development of sustainable buildings.

Modeling of Distributed Control Systems in Intelligent Building based on Colored Petri Nets

Distributed control systems consist of sensors, actuators and controllers, interconnected by communication networks and are characterized by a high number of concurrent process. This work presents a proposal for a procedure to model and analyze communication networks for distributed control systems in intelligent building. The approach considered for this purpose is based on the characterization of the control system as a discrete event system and application of coloured Petri net as a formal method for specification, analysis and verification of control solutions. With this approach, we develop the models that compose the communication networks for the control systems of intelligent building, which are considered the relationships between the various buildings systems. This procedure provides a structured development of models, facilitating the process of specifying the control algorithm. An application example is presented in order to illustrate the main features of this approach.

A critical reassessment of elastic unloading in sharp instrumented indentation experiments and the extraction of mechanical properties

This work examines the extraction of mechanical properties from instrumented indentation P-h(s) curves via extensive three-dimensional finite element analyses for pyramidal tips in a wide range of solids under frictional and frictionless contact conditions. Since the topography of the imprint changes with the level of pile-up or sink-in, a relationship is identified between correction factor beta in the elastic equation for the unloading indentation stage and the amount of surface deformation effects. It is shown that the presumption of a constant beta significantly affects mechanical property extractions. Consequently, a new best-fit function is found for the correlation between penetration depth ratios h(e)/h(max), h(r)/h(max) and n, circumventing the need for the assumption of a constant value for beta, made in our prior investigation [Acta Mater. 53 (2005) pp. 3545-3561]. Simulations under frictional contact conditions provide sensible boundaries for the influence of friction on both h(e)/h(max) and h(r)/h(max). Friction is essentially found to induce an overestimation in the inferred n. Instrumented indentation experiments are also performed in three archetypal metallic materials exhibiting distinctly different contact responses. Mechanical property extractions are finally demonstrated in each of these materials.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

Batch Liquid-Liquid Extraction of Phenol from Aqueous Solutions

The aim of this work is the study of batch liquid-liquid extraction of phenol from aqueous solutions in a bench-scale well-mixed reactor. The influence of the ratio of phase volumes, temperature, and rotational speed on phenol removal (0.72-1.1% w/w) was investigated using methyl isobutyl ketone as an extracting solvent. For this purpose, the ratio of phase volumes were set at 0.1 and 0.2, the temperature at 10, 20, and 30 degrees C, and the rotational speed at 300, 400, and 500 rpm. A physical model based on the material balance of the phases as well as the equation of mass flux between the phases allowed the estimation of the overall coefficient of mass transfer coupled with the superficial area. Moreover, it proved to fit, satisfactorily well, the experimental data of residual phenol concentration in the organic phase versus time under all the conditions investigated.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.