A reduced load approximation accounting for link interactions in a loss network

This paper is concerned with evaluating the performance of loss networks. Accurate determination of loss network performance can assist in the design and dimen- sioning of telecommunications networks. However, exact determination can be difficult and generally cannot be done in reasonable time. For these reasons there is much interest in developing fast and accurate approximations. We develop a reduced load approximation that improves on the famous Erlang fixed point approximation (EFPA) in a variety of circumstances. We illustrate our results with reference to a range of networks for which the EFPA may be expected to perform badly.

Genotype and environment effects on dynamics of harvest index during grain filling in sorghum

An approach based on a linear rate of increase in harvest index (141) with time after anthesis has been used as a simple means-to predict grain growth and yield in many crop simulation models. When applied to diverse situations, however, this approach has been found to introduce significant error in grain yield predictions. Accordingly, this study was undertaken to examine the stability of the HI approach for yield prediction in sorghum [Sorghum bicolor (L.) Moench]. Four field experiments were conducted under nonlimiting water. and N conditions. The experiments were sown at times that ensured a broad range in temperature and radiation conditions. Treatments consisted of two population densities and three genotypes varying in maturity. Frequent sequential harvests were used to monitor crop growth, yield, and the dynamics of 111. Experiments varied greatly in yield and final HI. There was also a tendency for lower HI with later maturity. Harvest index dynamics also varied among experiments and, to a lesser extent, among treatments within experiments. The variation was associated mostly with the linear rate of increase in HI and timing of cessation of that increase. The average rate of HI increase was 0.0198 d(-1), but this was reduced considerably (0.0147) in one experiment that matured in cool conditions. The variations found in IN dynamics could be largely explained by differences in assimilation during grain filling and remobilization of preanthesis assimilate. We concluded that this level of variation in HI dynamics limited the general applicability of the HI approach in yield prediction and suggested a potential alternative for testing.

Ventricular performance and Na+-K+ ATPase activity are reduced early and late after myocardial infarction in rats

Myocardial infarction leads to compensatory ventricular remodeling. Disturbances in myocardial contractility depend on the active transport of Ca2+ and Na+, which are regulated by Na+-K+ ATPase. Inappropriate regulation of Na+-K+ ATPase activity leads to excessive loss of K+ and gain of Na+ by the cell. We determined the participation of Na+-K+ ATPase in ventricular performance early and late after myocardial infarction. Wistar rats (8-10 per group) underwent left coronary artery ligation (infarcted, Inf) or sham-operation (Sham). Ventricular performance was measured at 3 and 30 days after surgery using the Langendorff technique. Left ventricular systolic pressure was obtained under different ventricular diastolic pressures and increased extracellular Ca2+ concentrations (Ca2+e) and after low and high ouabain concentrations. The baseline coronary perfusion pressure increased 3 days after myocardial infarction and normalized by 30 days (Sham 3 = 88 ± 6; Inf 3 = 130 ± 9; Inf 30 = 92 ± 7 mmHg; P < 0.05). The inotropic response to Ca2+e and ouabain was reduced at 3 and 30 days after myocardial infarction (Ca2+ = 1.25 mM; Sham 3 = 70 ± 3; Inf 3 = 45 ± 2; Inf 30 = 29 ± 3 mmHg; P < 0.05), while the Frank-Starling mechanism was preserved. At 3 and 30 days after myocardial infarction, ventricular Na+-K+ ATPase activity and contractility were reduced. This Na+-K+ ATPase hypoactivity may modify the Na+, K+ and Ca2+ transport across the sarcolemma resulting in ventricular dysfunction.

Avaliação da adsorção do herbicida 2,4-D em carvão ativado em pó utilizando água com diferentes qualidades

O 2,4-diclorofenoxiacético (2,4-D) é um dos herbicidas mais consumidos no Brasil e é preferencialmente usado devido a sua boa seletividade e baixo custo. Possui alta toxidade e baixa biodegradabilidade, oferecendo risco à saúde humana e ao meio ambiente, podendo ser encontrado em solos, águas superficiais e subterrâneas. Estudos mostram que o tratamento convencional da água possui baixa eficácia na remoção de microcontaminantes, com isso várias técnicas têm sido utilizadas na remoção de compostos em água, como a adsorção por carvão ativado. Apresenta-se a adsorção em carvão ativado tem se demonstrado como tecnologia eficiente na remoção de diversos contaminantes, dentre eles os agrotóxicos. Assim, o presente trabalho objetivou avaliar a adsorção do 2,4-D por três carvões ativados em pó (CAP) em água ultrapura e em água bruta do Rio Santa Maria da Vitória. A quantificação do herbicida foi analisada por cromatografia líquida de alta eficiência, após concentração da amostra pelo método de extração em fase sólida. Os ensaios de adsorção foram realizados com carvões ativados derivados da casca de coco (CAP-01), pinus (CAP-02) e palha de café (CAP-03), que foram caracterizados e avaliados na sua capacidade de remoção do 2,4-D nas duas matrizes de água. Dois modelos de isoterma de adsorção, Langmuir e Freundlich, foram aplicados para descrever os dados de adsorção, que indicaram o CAP-02 como o carvão que apresentou a melhor capacidade de adsorção do 2,4-D entre os carvões estudados, tanto em água ultrapura quanto em água bruta. Nos ensaios realizados em água bruta, houve redução da adsorção do 2,4-D para as três amostras de CAP, quando comparado com os ensaios realizados em água ultrapura, indicando interferência de compostos, como a matéria orgânica, no processo de adsorção.

Efeito da privação de sono paradoxal e do bloqueio da síntese da corticosterona sobre os limiares da reação de defesa induzida pela estimulação elétrica da matéria cinzenta periaquedutal e colículo superior

O Transtorno do pânico (TP) é um transtorno mental comum que afeta até 5% da população em algum momento da vida, sendo caracterizada pela presença de ataques de pânico (AP) recorrentes. Constitui uma psicopatologia que pode ser afetada pela privação do sono (PS), relação que ainda é pouco compreendida. Neste contexto, modelos experimentais de AP e de PS são ferramentas úteis na investigação dessa possível correlação, especialmente motivado pela crescente condição de privação de sono, que tem se tornado cada vez mais frequente na sociedade moderna. Assim, este estudo avaliou os efeitos da privação de sono paradoxal (PSP) sobre os limiares dos comportamentos defensivos induzidos por estimulação intracraniana (EI) da MCPD e CS de ratos num modelo experimental de AP, assim como verificou a influência da corticosterona sobre esses limiares. Foram utilizados 160 ratos Wistar machos (300g), organizados em 4 grupos com 40 animais cada, como se segue: Grupo Controle (CTR) submetido à EI, porém sem PSP; Grupo Privação (PRV), submetido à EI e privado por 96 horas; Grupo Privação + Bloqueio da corticosterona (PRB), submetido ao tratamento com metirapona, EI, e privado por 96 horas, e Grupo Controle + Bloqueio da corticosterona (CTB), submetido ao tratamento com metirapona e EI, porém sem privação de sono. Após 10 dias do implante cirúrgico intracraniano de eletrodo na MCPD e CS, os animais passaram por 5 sessões de estimulação, como se segue: 1ª (TRI) considerada triagem - imediatamente antes da privação, 2ª (P48) após 48h de privação, 3ª (P96) após 96h de privação, 4ª (R48) após 48h de retirada da privação e 5ª (R96) após 96h de retirada da privação. As curvas de limiares das respostas individuais de defesa obtidas nas várias sessões de estimulação da MCPD e CS (TRI, P48, P96, R48 e R96) dos ratos foram comparadas entre si, bem como as curvas de limiares de uma dada resposta nos diferentes grupos (CTR, PRV, CTB e PRB). Além disso, os níveis de corticosterona (CORT) foram dosados nas diferentes sessões de EI, e comparadas num mesmo grupo, bem como nos diferentes grupos. No grupo CTR, todos os comportamentos foram iguais em todas as sessões quando comparados à TRI, entretanto, nos animais privados (PRV), o limiar do galope (GLP) reduziu significativamente em R48 e R96, não ocorrendo xix alterações nos demais comportamentos. Em contraste, no grupo PRB, o Trote (TRT) aumentou a partir de P48, enquanto o GLP não foi alterado em nenhuma sessão de EI. Na comparação entre os grupos, em Salto (SLT), Micção (MIC), Exoftalmia (EXO), Imobilidade (IMO), Defecação (DEF), TRT e GLP, não sofreram alterações decorrentes da CORT produzida decorrente da PSP, sugerindo que a corticosterona não altera os comportamentos defensivos característicos do Ataque de Pânico. Em adição, tais resultados sugerem que os efeitos tardios da PSP sobre os limiares de GLP possivelmente se devam a mecanismos neuroquímicos tempo-dependente.

Análise numérico-experimental do comportamento da aderência aço-concreto

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2016.

Conditioning information in mutual fund performance evaluation: Portuguese evidence

We estimate and compare the performance of Portuguese-based mutual funds that invest in the domestic market and in the European market using unconditional and conditional models of performance evaluation. Besides applying both partial and full conditional models, we use European information variables, instead of the most common local ones, and consider stochastically detrended conditional variables in order to avoid spurious regressions. The results suggest that mutual fund managers are not able to outperform the market, presenting negative or neutral performance. The incorporation of conditioning information in performance evaluation models is supported by our findings, as it improves the explanatory power of the models and there is evidence of both time-varying betas and alphas related to the public information variables. It is also shown that the number of lags to be used in the stochastic detrending procedure is a critical choice, as it will impact the significance of the conditioning information. In addition, we observe a distance effect, since managers who invest locally seem to outperform those who invest in the European market. However, after controlling for public information, this effect is slightly reduced. Furthermore, the results suggest that survivorship bias has a small impact on performance estimates.

Measuring fund performance using multi-factor models: evidence for the Portuguese market

This paper examines the performance of Portuguese equity funds investing in the domestic and in the European Union market, using several unconditional and conditional multi-factor models. In terms of overall performance, we find that National funds are neutral performers, while European Union funds under-perform the market significantly. These results do not seem to be a consequence of management fees. Overall, our findings are supportive of the robustness of conditional multi-factor models. In fact, Portuguese equity funds seem to be relatively more exposed to smallcaps and more value-oriented. Also, they present strong evidence of time-varying betas and, in the case of the European Union funds, of time-varying alphas too. Finally, in terms of market timing, our tests suggest that mutual fund managers in our sample do not exhibit any market timing abilities. Nevertheless, we find some evidence of timevarying conditional market timing abilities but only at the individual fund level.

Models for the Reverse Engineering of Java/Swing Applications

Abstract. Interest in design and development of graphical user interface (GUIs) is growing in the last few years. However, correctness of GUI's code is essential to the correct execution of the overall software. Models can help in the evaluation of interactive applications by allowing designers to concentrate on its more important aspects. This paper describes our approach to reverse engineering abstract GUI models directly from the Java/Swing code.

Color Models and Illumination Changes

Color model representation allows characterizing in a quantitative manner, any defined color spectrum of visible light, i.e. with a wavelength between 400nm and 700nm. To accomplish that, each model, or color space, is associated with a function that allows mapping the spectral power distribution of the visible electromagnetic radiation, in a space defined by a set of discrete values that quantify the color components composing the model. Some color spaces are sensitive to changes in lighting conditions. Others assure the preservation of certain chromatic features, remaining immune to these changes. Therefore, it becomes necessary to identify the strengths and weaknesses of each model in order to justify the adoption of color spaces in image processing and analysis techniques. This chapter will address the topic of digital imaging, main standards and formats. Next we will set the mathematical model of the image acquisition sensor response, which enables assessment of the various color spaces, with the aim of determining their invariance to illumination changes.

Extracting and verifying coordination models from source code

Current software development relies increasingly on non-trivial coordination logic for com- bining autonomous services often running on di erent platforms. As a rule, however, in typical non-trivial software systems, such a coordination layer is strongly weaved within the application at source code level. Therefore, its precise identi cation becomes a major methodological (and technical) problem which cannot be overestimated along any program understanding or refactoring process. Open access to source code, as granted in OSS certi cation, provides an opportunity for the devel- opment of methods and technologies to extract, from source code, the relevant coordination information. This paper is a step in this direction, combining a number of program analysis techniques to automatically recover coordination information from legacy code. Such information is then expressed as a model in Orc, a general purpose orchestration language

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS

The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.