Teste baseado em casos de uso.

Os casos de uso consistem em um meio de se capturar as funcionalidades de um sistema de forma centrada no usuário. Entender como o usuário interage com um sistema é um aspecto importante quando se tem como objetivo entregar um produto que atenda às suas necessidades.

Backup incremental e recuperação cronológica de dados no PostgreSQL.

Este documento consiste no estudo, análise e identificação de uma solução voltada para a recuperação de dados no Sistema Gerenciador de Banco de Dados (SGBD) PostgreSQL. A solução em recuperação de dados, aqui apresentada, foi testada e direcionada para o sistema operacional Linux Ubuntu, porém a mesma é compatível com outras distribuições, bem como passível de ser implementada e executada em outros sistemas operacionais, nos quais funcionam o SGBD PostgreSQL, resguardadas as suas respectivas peculiaridades, ou, basicamente, a nomenclatura e o formato dos comandos empregados relativos ao sistema operacional adotado.

Programa Nacional de Melhoramento do Gir Leiteiro: sumário brasileiro de touros: resultado do teste de progênie: 6a prova de pré-seleção de touros - maio 2015.

2015

Programa Nacional de Melhoramento do Gir Leiteiro: sumário brasileiro de touros: resultado do teste de progênie 7a prova de pré-seleção de touros - maio 2016.

2016

Programa Nacional de Melhoramento do Guzerá para Leite: resultados do Teste de Progênie, do Programa de Melhoramento Genético de Zebuínos da ABCZ e do Núcleo MOET.

2016

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

Does an incremental filling technique reduce polymerization shrinkage stresses

It is widely accepted that volumetric contraction and solidification during the polymerization process of restorative composites in combination with bonding to the hard tissue result in stress transfer and inward deformation of the cavity walls of the restored tooth. Deformation of the walls decreases the size of the cavity during the filling process. This fact has a profound influence on the assumption-raised and discussed in this paper-that an incremental filling technique reduces the stress effect of composite shrinkage on the tooth. Developing stress fields for different incremental filling techniques are simulated in a numerical analysis. The analysis shows that, in a restoration with a well-established bond to the tooth-as is generally desired-incremental filling techniques increase the deformation of the restored tooth. The increase is caused by the incremental deformation of the preparation, which effectively decreases the total amount of composite needed to fill the cavity. This leads to a higher-stressed tooth-composite structure. The study also shows that the assessment of intercuspal distance measurements as well as simplifications based on generalization of the shrinkage stress state cannot be sufficient to characterize the effect of polymerization shrinkage in a tooth-restoration complex. Incremental filling methods may need to be retained for reasons such as densification, adaptation, thoroughness of cure, and bond formation. However, it is very difficult to prove that incrementalization needs to be retained because of the abatement of shrinkage effects.

Knowledge networks change during radical and incremental innovation episodes: a case study on software developmen

The role intra-organizational knowledge exchanges play in innovation processes has been widely acknowledged in the organizational literature. This paper contributes to the understanding of which specific configurations knowledge networks assume during different phases of radical and incremental innovation processes. The case study we selected is a FLOSS (Free/Libre Open Source Software) community consisting of 233 developers committed to the development of a web browser application since November 2002. By harvesting the mailing list, official blog and code repository of a FLOSS community, we investigate the patterns of knowledge exchange and individual contributions of its developers. We measure structural cohesion and compare global and local network properties at different points in time. Preliminary results show that phases of radical and incremental innovation are associated with specific configurations of the knowledge network as a whole as well as with different network positions of the core developers of the software.

The effects of cadence and power output upon physiological and biomechanical responses to incremental arm-crank ergometry

The aim of this study was to examine the effects of cadence and power output on physiological and biomechanical responses to incremental arm-crank ergometry (ACE). Ten male subjects (mean +/- SD age, 30.4 +/-5.4 y; height, 1.78 +/-0.07 m; mass, 86.1 +/-14.2 kg) undertook 3 incremental ACE protocols to determine peak oxygen uptake (VO2 peak; mean of 3 tests: 3.07 +/- 0.17 L.min-1) at randomly assigned cadences of 50, 70, or 90 r.min-1. Heart rate and expired air were continually monitored. Central (RPE-C) and local (RPE-L) ratings of perceived exertion were recorded at volitional exhaustion. Joint angles and trunk rotation were analysed during each exercise stage. During submaximal power outputs of 50, 70, and 90 W, oxygen consumption (VO2) was lowest for 50 r.min-1 and highest for 90 r.min-1 (p < 0.01). VO2 peak was lowest during 50 r.min-1 (2.79 +/-0.45 L.min-1; p < 0.05) when compared with both 70 r.min-1 and 90 r.min-1 (3.16 +/-0.58, 3.24 +/-0.49 L.min-1, respectively; p > 0.05). The difference between RPE-L and RPE-C at volitional exhaustion was greatest during 50 r.min-1 (2.9 +/- 1.6) when compared with 90 r.min-1 (0.9 +/- 1.9, p < 0.05). At VO2 peak, shoulder range of motion (ROM) and trunk rotation were greater for 50 and 70 r.min-1 when compared with 90 r.min-1 (p < 0.05). During submaximal power outputs, shoulder angle and trunk rotation were greatest at 50 r.min-1 when compared with 90 r.min-1 (p < 0.05). VO2 was inversely related to both trunk rotation and shoulder ROM during submaximal power outputs. The results of this study suggest that the greater forces required at lower cadences to produce a given power output resulted in greater joint angles and range of shoulder and trunk movement. Greater isometric contractions for torso stabilization and increased cost of breathing possibly from respiratory-locomotor coupling may have contributed increased oxygen consumption at higher cadences.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.