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Mechanical ventilation (MV) is widely used in hospitals or in intensive care units in patients with acute respiratory failure (ARF). The efficiency of mechanical ventilation depends on frequent manual adjustments by trained professionals and in accordance with the patient's physiological signals. This task implies a specialized, constant and exhaustive work. This paper proposes an artificial neural network (RNA) model for automatic parametrization of the mechanical ventilator aiming to maintain adequate ventilation of the patient plus the optimization of human resources in the health area. [ABSTRACT FROM AUTHOR]

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Brazilian Journal of Surgery & Clinical Research is the property of Master Editora and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Ciência e Natura is the property of Revista Ciencia e Natura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tecno-Lógica is the property of Tecno-Logica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Iberoamericana de Ingeniería Mecánica is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Gestão & Tecnologia is the property of Revista Gestao & Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computing & System Journal (C&S) / Revista de Sistemas e Computação (RSC) is the property of FACS Servicos Educacionais S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Ingeniería Industrial is the property of Departamento de Ingenieria Industrial, Universidad del Bio-Bio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Colloquium Exactarum is the property of Asociacao Prudentina de Educacao e Cultura (APEC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper discusses the results of a mixed grinding simulator application based on an artificial neural network (multiple-layer perceptron using a back-propagation-like algorithm with moment). The data used came from a previous paper entitled "Selective grinding of dolomite and quartz mixes". The Shewhart control chart for individual values was used in order to verify the statistical stability of the simulation process results, which was useful for testing acceptance. The results have displayed good performance of this tool related to mix grinding simulation, a common issue in the mining and metallurgical sectors. [ABSTRACT FROM AUTHOR]

Copyright of Colloquium Exactarum is the property of Asociacao Prudentina de Educacao e Cultura (APEC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As estradas florestais são o principal meio de integração entre as florestas e as empresas. A partir do exposto, percebe-se a necessidade não apenas da correta aplicação de atividades de manutenção, mas também de se determinar o tempo exato para tal intervenção. Partindo desse pressuposto, este trabalho apresenta os resultados da apreciação de dois métodos de classificação da qualidade de estradas não pavimentadas, com o intuito de se verificar a aplicabilidade dos mesmos na caracterização das estradas florestais brasileiras e servir como base para um sistema de gestão das operações de manutenção destas vias. Foram medidos os principais defeitos em estradas florestais seguindo um método denominado de Índice de Condição de Rodovia Não Pavimentada (ICRNP), que serviram de base para gerar um banco de dados para testar a eficiência do uso de redes neurais artificiais (RNA) na administração das estradas florestais, minimizando custos e paralisações de tráfego. Concluiu-se que a utilização das redes neurais artificiais apresentou resultados superiores ao método do ICRNP.The forest roads are the main integration mode between forests and companies. Therefore, there is a need for defining not only the required maintenance activities, but also the exact time for such intervention. Starting from this premise this paper presents the results of the assessment of two methods of classification of the quality of unpaved roads in order to verify which one reflects the field reality and thus can serve as the basis for a unpaved road management system. In this paper main defects in forest roads were measured following a method named Unsurfaced Road Condition Index (URCI) which served as a database for testing the efficiency of using artificial neural networks (ANN) in the management of forest roads taking into account costs minimization and traffic stoppage. It was concluded that the use of artificial neural networks showed superior performance that the URCI method.

This paper shows the implementation of a neurosimulation technique for the well placement applied to the development of a heterogeneous hydrocarbon field with an irregular geometry. During the development of a hydrocarbon field the well placement is a major task, because a small change in location can make gains or losses of money during the remaining productive life of the field; this paper presents a neurosimulation technique as an alternative to conventional methods of well placement which are expensive and consume large amounts of time. This technique is a bridge between hard-computing and soft-computing; effectively mixes artificial neural networks (ANN) and numerical reservoir simulation, in this way using the numerical reservoir simulation in a combination of training wells, production data are obtained along with other data which are used to train and adjust the network, then a large number of scenarios are generated which are evaluated by the trained ANN, the best results are verified whit the numerical reservoir simulation, and then it is possible to predict the rate at which the wells will produce and the cumulative hydrocarbon production. For the development of this work open source tools and free software was used to encourage their use and development in research, in academia and in hydrocarbon industry. This work shows an alternative method of selecting wells that produce fast and accurate results, with which it is easy to take the decision about where is the best place to drill new wells during the development of a hydrocarbon field.

Copyright of Exacta is the property of Exacta - Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Discrete choice models have been used over the years in disaggregated approaches to forecast destination choices. However, there are important constraints in some of these models that pose obstacles to using them, such as the Independence of Irrelevant Alternatives (IIA) property in the Multinomial Logit model, the need to assume specific structures and high calibration times, depending on the complexity of the case being evaluated. However, some of these mentioned constraints could be mitigated using Mixed Models or Nested Logit. Therefore, this paper proposes a comparative analysis between the Artificial Neural Network (ANNs), the Multinomial and Nested Logit models for disaggregated forecasting of urban trip distribution. A case study was conducted in a medium-sized Brazilian city, Santa Maria (RS), Brazil. The data used come from a household survey, prepared for the Urban Mobility Master Plan. For the sake of comparison, hit rates and frequency of trip distribution distances were analyzed, showing that ANNs can be as efficient as the Discrete Choice models for disaggregated forecasting of urban trip destination without, however, assuming some constraints. Finally, based on the results obtained, the efficiency of ANNs is observed for predicting alternatives with a low number of observations. They are important tools for obtaining Origin-Destination matrices from incomplete sample matrices or with a low number of observations. However, it is important to mention that discrete choice models can provide important information for the analyst, such as statistical significance of parameters, elasticities, subjective value of attributes, etc.

Copyright of Universitas. Gestão e Tecnologia is the property of Centro Universitario de Brasilia, UNICEUB and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper aims to develop an insolvency/failure analysis model allying accounting information with Data Envelopment Analysis (DEA). The research was conducted in phases: initially previous studies were examined; then samples were obtained from different databases containing financial information of distressed and healthy companies; and, finally a methodology for application of DEA to failure/insolvency analysis was proposed. The results were comparable to those obtained from logistic regression models and neural network models, without, however, a clear indication of the superiority of any procedure. [ABSTRACT FROM AUTHOR]

This paper presents a formulation for distortional buckling stress (σdist) in cold-formed steel lipped U section columns, with simply supported end conditions and free warping. This formulation is based on an adjusted model from an artificial neural network (ANN), whose data was provided by a program based on the Finite Strip Method (FSM). The results show the viability of the obtained equation for determining σdist on a columns made with the analyzed sections. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Administração e Inovação (RAI) is the property of Revista de Administracao e Inovacao- RAI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The dynamics and complexity of contemporary markets have made it difcult for marketing professionals to understand the buying behavior of industrial decision-makers. Efcient market segmentation can improve marketing strategies and knowledge of buyers, favoring a match between offer and demand. This paper discusses the possibility of developing an Articial Neural Network (ANN) to identify and build a prole of the professional purchasing agent, by means of a psychographic model based on perceived risk. The construction of this network is based on the Perceptron Multi-Layer model and the Backpropagation learning algorithm. This type of network is capable of identifying data patterns, which do not show linear characteristics, a common feature of psychographic data. This technology can reveal patterns and develop skills that enable a better understanding of industrial markets. [ABSTRACT FROM AUTHOR]

The Technical Assistance and Integrated Control of Usiminas-Cubatão - have studied and analyzed the microstructural characteristics and mechanical properties of the structural steels the company produces as well as the phenomenological models it has created to describe their properties. An important tool that uses artificial intelligence was recently incorporated: the Adaptive Neural Networks (ANN's). This paper shows how the ANN's are improving the product development activities, as well as the adequacy and application of hot rolled products. The chemical composition, the rolling temperatures, the final product size and position of sampling are parameters used in the ANN's, making it possible to select effectively and practically, and not only theoretically, the best combinations of these parameters to obtain a more appropriate product. The application of products has been benefited by the use of these tools, especially for petrochemical and automobile uses, as well as the internal activities of product quality control. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to evaluate the impact of the exchange rate volatility in price prediction of derivative securities in the Brazilian capital markets using an artificial neural network technique, given the Black & Scholes Model limitations. For this purpose a multiplayer, feedforward neural network, trained by the backpropagation algorithm model, to perform the prediction of the Telemar option prices was developed. The model results show that price estimates are close to the real values, mainly when appended to the exchange rate, confirming that the performance of neural network is superior to other results. The inclusion of the exchange rate in neural networks technique results in a better price forecasting for the options, because the volatility in the price of the underlying asset is caused by temporary arbitrage of quotes among the national and foreign stock markets where the company is listed. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economia e Administração is the property of INSPER Instituto de Ensino e Pesquisa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Economia e Administração is the property of INSPER Instituto de Ensino e Pesquisa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Business & Projects / Revista de Gestão e Projetos is the property of Revista de Gestao e Projetos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant.

Time series forecasting is widely used in various areas of human knowledge, especially in the planning and strategic direction of companies. The success of this task depends on the forecasting techniques applied. In this paper, a hybrid approach to project time series is suggested. To validate the methodology, a time series already modeled by other authors was chosen, allowing the comparison of results. The proposed methodology includes the following techniques: wavelet shrinkage, wavelet decomposition at level r, and artificial neural networks (ANN). Firstly, a time series to be forecasted is submitted to the proposed wavelet filtering method, which decomposes it to components of trend and linear residue. Then, both are decomposed via level r wavelet decomposition, generating r + 1 Wavelet Components (WCs) for each one; and then each WC is individually modeled by an ANN. Finally, the predictions for all WCs are linearly combined, producing forecasts to the underlying time series. For evaluating purposes, the time series of Canadian Lynx has been used, and all results achieved by the proposed method were better than others in existing literature.

Objective: Previous research has indicated high rates of intercity movement among homeless young adults (HYAs) for a variety of prosocial (e.g., avoiding domestic violence and seeking new employment opportunities) and antisocial (e.g., following drug supplies and avoiding law enforcement) reasons. The complicated mixture of individual circumstances associated with transience has made it difficult to predict features of transience, such as distance traveled and move frequency. Method: This study describes a method to build an artificial neural network (ANN) that predicts distance traveled and compares the results of thatANN to a generalized linear regression. Results: The ANN more accurately predicts distance traveled than does the linear statistical model and advances the development of approaches to predict complicated human phenomena. Conclusions: Accurately predicting features of transience among HYAs is important in tailoring effective interventions aimed at minimizing travel for negative reasons and making travel for positive reasons safer. [ABSTRACT FROM AUTHOR]

Las redes neuronales artificiales, simuladoras del proceso de aprendizaje de las neuronas biológicas, han sido utilizadas con éxito en el cálculo de parámetros en diversos problemas de ingeniería en que las variables involucradas tienen una alta relación no lineal entre sí y la modelación no permite representar el problema mediante una función matemática de fácil deducción. En la ciencia del suelo la predicción de algunas propiedades involucra diversas variables que hacen de su estimación por medio de modelos matemáticos un proceso complejo, y trasladan la solución del problema al campo de la inteligencia artificial. En el presente artículo se reporta la elaboración de redes neuronales artificiales para la estimación de la resistencia a la penetración a diferentes profundidades de un suelo; se consideran como variables influyentes el contenido de humedad, la densidad, la carga estática y la presión de inflado. Los resultados muestran una mejor estimación para profundidades entre 20 cm y 30 cm.Artificial Neural Networks simulate the learning process of biological neurons, and these have been successfully used in the computation of parameters on several engineering problems where exist a strong nonlinear relation among the variables. In soil science, estimation of some properties involves variables that are complicated to estimate using mathematical models, so the solution for the problems fall into the field of Artificial Intelligence. The present paper reports the elaboration of an Artificial Neural Network for the estimation of penetration resistance of soil at different depths, considering as influential variables humidity, density, static load, and inflate pressure. The best estimation results were obtained at a depth of 20-30 cm.

O objetivo desse estudo é demonstrar, por meio de análise quantitativa e qualitativa, a eficácia de ferramentas linguístico-computacionais na seleção de terminologia para a produção de material terminológico. Serão apresentadas duas ferramentas linguístico-computacionais (WordSmith Tools e VocabProfile) e, também, sugestões para que o ensino de termos ofereça resultados práticos. A fundamentação teórico-metodológica recorreu a Barros (2004); Berber Sardinha (2000; 2005); Biderman (2001); Cabré (2007); Cobb (2007); Nation, (2003) e Sinclair (2004). O corpus da pesquisa foi constituído exclusivamente de material escrito na língua inglesa em diversas áreas de especialidade. Os procedimentos de preparação de material terminológico são exemplificados a partir de uma das áreas de especialidades utilizadas nos corpora de pesquisa, as Redes Neurais Artificiais. Os resultados obtidos indicam que a utilização do Wordsmith Tools juntamente com o VocabProfile pode fornecer dados importantes para a pesquisa linguistica.This paper aims to demonstrate by means of quantitative and qualitative analyses the effectiveness of the linguistic computational tools in selecting terminology for the production of terminological material. Two linguistic computational tools will be introduced (WordSmith Tools e VocabProfile) and also suggestions so as the teaching of terms may offer practical results. The theoretical-methodological approach relies on Barros (2004); Berber Sardinha (2000; 2005); Biderman (2001); Cabré (2007); Cobb (2007); Nation (2003) and Sinclair (2004). The research corpus was made solely of written material in English in several specialty languages. The procedures regarding terminological material preparation are exemplified with one of the specialty fields used in the research corpus, the Artificial Neural Networks. The obtained results indicate that the use of Wordsmith Tools in conjunction with VocabProfile might provide useful data for the linguistic research.

To evaluate real estates means estimate its market value, taking into account several factors, such as: the particular characteristics of the property, market conditions and the balance between stakeholders in the negotiation. All these features make the evaluation a complex task, mainly because the real estate market represents an important segment of the national economy requiring more precise results. In this context, we conducted a survey on evaluation methodologies of properties that are used and identified that the main technique, regression analysis, has limitations that may compromise the outcome of the evaluation. This paper presents the research methodology developed using Multicriteria Analysis and Artificial Neural Network to evaluate residential apartments. The results were considered very satisfactory, indicating that the combination of multicriteria analysis and artificial neural network is efficient and promising.

The main goal of this paper is to evaluate the impact of the exchange rate volatility in price prediction of derivative securities in the Brazilian capital markets using an artificial neural network technique, given the Black & Scholes Model limitations. For this purpose a multiplayer, feedforward neural network, trained by the backpropagation algorithm model, to perform the prediction of the Telemar option prices was developed. The model results show that price estimates are close to the real values, mainly when appended to the exchange rate, confirming that the performance of neural network is superior to other results. The inclusion of the exchange rate in neural networks technique results in a better price forecasting for the options, because the volatility in the price of the underlying asset is caused by temporary arbitrage of quotes among the national and foreign stock markets where the company is listed.

Inúmeras são as atividades agrícolas que necessitam de interação humana nos processos decisórios, e entre elas encontra-se a classificação de frutos. O consumo de frutos "in natura" exige altíssimo nível de qualidade, demandando um processo classificatório mais acurado. A classificação de frutos depende do reconhecimento de padrões natural ou artificial, de acordo com algumas categorias pré-definidas. Uma vez que um padrão de um fruto está sendo classificado, esse deve ser comparado com algum outro padrão armazenado. A maior parte da classificação de frutos é baseada na classificação humana.Este trabalho apresenta a possibilidade de uso de redes neurais artificiais no desenvolvimento de modelos de classificação de frutos por meio de vetores de padrões. Este trabalho foi desenvolvido no Departamento de Máquinas Agrícolas da Faculdade de Engenharia Agrícola da Universidade Estadual de Campinas, as redes neurais armazenaram os vetores de padrões de frutos peso, diâmetro. Esses componentes vetoriais associados entre si interagiram, determinando um vetor padrão de saída de acordo com os padrões de frutos armazenados. Para atingir esses objetivos, foi usada uma rede Perceptron de múltiplas camadas, com algoritmo de treinamento tipo retro-propagação para armazenar os vetores de padrões de frutos e para classificação desses padrões de entrada. A rede treinada conseguiu aprender a relação entre vetores de entrada e saída, demonstrando a potencialidade do uso de tais ferramentas na classificação artificial.Agriculture is one of the economic activities that more require the presence human being in the decision taking. Innumerable are the processes that require some type of human being interference in the conclusion of the processes. Fruit Sorting depends on human or artificial pattern recognition according to some pre defined categories. Once a fruit pattern is under classification, this one must be compared to some other ones stored. After that comparison it can be classified. Most sorting fruits jobs are human basis classification. This paper shows that using neural networks is possible to develop capable models of storing fruit pattern vectors. Given any fruit pattern vector to the model it can classify to the closest fruit pattern vector stored. The number of patterns were incremented and presented to the neural networks, classifying the presented fruits and proved the scalability of number of vector components used in fruit pattern vectors stored in the model. This work was developed in the Agricultural Machinery Department of the Agricultural Faculty in State University of Campinas, the neural networks stored fruit pattern vectors such as Weight, Diameter. These vectors components associated itself interacted determining an output pattern vector classifying according to the stored fruit vector patterns. A Multi Layer Perceptron Network with Backpropagation algorithm was used, storing the relationship between input fruit pattern vectors and output classification class vectors. The neural network was trained and tested presenting the desired results, it can be used as a tool for future fruit classification processes.