Modelos matemáticos para estimar as exigências de lisina digestível para aves de corte ISA Label

O objetivo neste estudo foi avaliar diferentes modelos ajustados às respostas de ganho de peso obtidas em experimento com aves da linhagem ISA Label no período de 1 a 28 dias de idade. Foram utilizados 480 pintos de ambos os sexos, distribuídos em delineamento inteiramente casualizado, em arranjo fatorial 4 X 2 (níveis de lisina X sexo), com três repetições, com 20 aves por unidade experimental. Uma ração basal foi formulada para atender às exigências das aves, exceto em lisina. Essa ração foi suplementada com L-lisina HCl em substituição ao ácido L-glutâmico, resultando em rações experimentais isonitrogênicas e isoenergéticas contendo 0,85; 0,97; 1,09 e 1,21% de lisina digestível. As respostas de ganho de peso foram ajustadas de acordo com os níveis de lisina da ração pelos modelos Linear Reponse Plateau (LRP), segmentado de duas inclinações, polinomial quadrático e exponencial. A primeira intersecção da equação quadrática com o platô do LRP também foi utilizado para estimar o nível ótimo. Os níveis de lisina digestível estimados pelos modelos LRP, segmentado e quadrático, foram 0,999; 1,010 e 1,116%, respectivamente. Na combinação do modelo quadrático com o LRP, a estimativa da exigência de lisina digestível foi de 1,041%. O modelo exponencial proporcionou estimativa de 1,066%, considerando 95% da resposta assintótica. Com base nos custos com alimentação, esse mesmo modelo gerou estimativas de 1,000 e 1,030% quando o custo do quilograma de L-lisina HCl foi R$ 8,50 e R$ 6,50, respectivamente. Considerando as limitações de cada um dos modelos propostos, o procedimento para estimar as exigências de lisina digestível pela primeira intersecção da equação quadrática com o platô do LRP foi o mais adequado para melhorar o ganho de peso das aves quando variáveis econômicas não foram consideradas.

Níveis nutricionais de fósforo disponível para aves de corte ISA Label criadas em semiconfinamento

Foram realizados três ensaios para determinar os níveis nutricionais de fósforo disponível (Pd) para machos e fêmeas da linhagem ISA Label nas fases inicial (1 a 28 dias), crescimento (28 a 56 dias) e final (56 a 84 dias) criadas em semiconfinamento. em cada ensaio, 480 aves com idade correspondente à fase de criação foram alojadas em 24 unidades experimentais contendo áreas de abrigo e de pastejo. O delineamento experimental utilizado foi o inteiramente casualizado, em esquema fatorial 4 × 2 (níveis de Pd e sexos) com três repetições de 20 aves. Os níveis de fósforo disponível avaliados foram: 0,25; 0,36; 0,47 e 0,58% na fase inicial; 0,18; 0,31; 0,44 e 0,57% na fase de crescimento; e 0,14; 0,27; 0,40 e 0,53% na fase final. Foram avaliados o ganho de peso, consumo de ração, consumo de Pd, conversão alimentar, teores de fósforo, cálcio e cinzas na tíbia e resistência à quebra óssea. de acordo com os resultados, o nível ótimo de Pd na ração na fase inicial, para machos e fêmeas são de 0,39 e 0,49%, que correspondem ao consumo de 3,94 e 3,96 g de Pd/ave, respectivamente. Para a fase de crescimento, recomenda-se 0,35% de Pd na ração para aves de ambos os sexos, que correspondem a consumo de 8,45 e 6,70 g de Pd/ave. Na fase final, recomendam-se os níveis de 0,32 e 0,30% de Pd, que correspondem a consumos de 12 e 9,5 g de Pd/ave para machos e fêmeas, respectivamente.

Contribuições aos Processos de Clustering com Base em Métricas não-Euclidianas

In this work we present a new clustering method that groups up points of a data set in classes. The method is based in a algorithm to link auxiliary clusters that are obtained using traditional vector quantization techniques. It is described some approaches during the development of the work that are based in measures of distances or dissimilarities (divergence) between the auxiliary clusters. This new method uses only two a priori information, the number of auxiliary clusters Na and a threshold distance dt that will be used to decide about the linkage or not of the auxiliary clusters. The number os classes could be automatically found by the method, that do it based in the chosen threshold distance dt, or it is given as additional information to help in the choice of the correct threshold. Some analysis are made and the results are compared with traditional clustering methods. In this work different dissimilarities metrics are analyzed and a new one is proposed based on the concept of negentropy. Besides grouping points of a set in classes, it is proposed a method to statistical modeling the classes aiming to obtain a expression to the probability of a point to belong to one of the classes. Experiments with several values of Na e dt are made in tests sets and the results are analyzed aiming to study the robustness of the method and to consider heuristics to the choice of the correct threshold. During this work it is explored the aspects of information theory applied to the calculation of the divergences. It will be explored specifically the different measures of information and divergence using the Rényi entropy. The results using the different metrics are compared and commented. The work also has appendix where are exposed real applications using the proposed method

Uma Aplicação do Algoritmo QT Clustering para Marcação Colaborativa de Pontos Perigosos em Vias Públicas

This work proposes a collaborative system for marking dangerous points in the transport routes and generation of alerts to drivers. It consisted of a proximity warning system for a danger point that is fed by the driver via a mobile device equipped with GPS. The system will consolidate data provided by several different drivers and generate a set of points common to be used in the warning system. Although the application is designed to protect drivers, the data generated by it can serve as inputs for the responsible to improve signage and recovery of public roads

Desenvolvimento de um Sistema de Visão Global para uma Frota de Mini-Robôs Móveis

Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second

Self-Organizing Maps as a New Tool for Classification of Plants at Lower Hierarchical Levels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Hybrid evolutionary algorithm for the Capacitated Centered Clustering Problem

The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS. (C) 2010 Elsevier Ltd. All rights reserved.

Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Mating system parameters at hierarchical levels of fruits, individuals and populations in the Brazilian insect-pollinated tropical tree, Tabebuia roseo-alba (Bignoniaceae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Algoritmos de agrupamento aplicados a dados de expressão gênica de câncer: um estudo comparativo

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using classic clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. This work presents the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods

Comitês de grupamento aplicados a dados de expressão gênica

The main goal of this work is to investigate the suitability of applying cluster ensemble techniques (ensembles or committees) to gene expression data. More specifically, we will develop experiments with three diferent cluster ensembles methods, which have been used in many works in literature: coassociation matrix, relabeling and voting, and ensembles based on graph partitioning. The inputs for these methods will be the partitions generated by three clustering algorithms, representing diferent paradigms: kmeans, ExpectationMaximization (EM), and hierarchical method with average linkage. These algorithms have been widely applied to gene expression data. In general, the results obtained with our experiments indicate that the cluster ensemble methods present a better performance when compared to the individual techniques. This happens mainly for the heterogeneous ensembles, that is, ensembles built with base partitions generated with diferent clustering algorithms

Investigando a combinação de técnicas de aprendizado semissupervisionado e classificação hierárquica multirrótulo

Data classification is a task with high applicability in a lot of areas. Most methods for treating classification problems found in the literature dealing with single-label or traditional problems. In recent years has been identified a series of classification tasks in which the samples can be labeled at more than one class simultaneously (multi-label classification). Additionally, these classes can be hierarchically organized (hierarchical classification and hierarchical multi-label classification). On the other hand, we have also studied a new category of learning, called semi-supervised learning, combining labeled data (supervised learning) and non-labeled data (unsupervised learning) during the training phase, thus reducing the need for a large amount of labeled data when only a small set of labeled samples is available. Thus, since both the techniques of multi-label and hierarchical multi-label classification as semi-supervised learning has shown favorable results with its use, this work is proposed and used to apply semi-supervised learning in hierarchical multi-label classication tasks, so eciently take advantage of the main advantages of the two areas. An experimental analysis of the proposed methods found that the use of semi-supervised learning in hierarchical multi-label methods presented satisfactory results, since the two approaches were statistically similar results

Hierarchical status and colour preference in Nile tilapia (Oreochromis niloticus)

We studied the colour preference of isolated Nile tilapia (Oreochromis niloticus) and whether previous residence or body size can affect environmental colour choice. In the first phase, a cylindrical tank was divided into five differently coloured compartments (yellow, blue, green, white and red), a single fish was introduced into the tank and the frequency at which this fish visited each compartment was recorded over a 2-day study period. An increasingly larger fish (approx +2 cm in length each time) was then added into the tank on each of days 3, 5 and 7 (=four fish in the tank by day 7), and the frequency at which each fish visited the different compartments of the tank was observed twice a day to obtain visit frequency data on the differently sized fishes. This experiment was replicated six times. In the first phase, the solitary fish established residence inside the yellow compartment on the first and second days. Following the introduction of a larger fish, the smaller fish was displaced from the occupied compartment. Nile tilapia possibly shows this preference for yellow as a function of its visual spectral sensitivity and/or the spectral characteristics of its natural environment. Moreover, body size is an important factor in determining hierarchical dominance and territorial defence, and dominant fish chose the preferred environmental colour compartment as their territory.

Toward a hierarchical concept of plant stress

A number of attempts have been made to obtain a clear definition of biological stress. However, in spite of the efforts, some controversies on the concept of plant stress remain. The current versions are centered either on the cause (stress factor) or on the effect (stress response) of environmental stress. The objective of this study was to contribute to the definition of stress, using a hierarchical approach. Thus, we have performed an analysis of the most usual stress concepts and tested the relevance of considering different observation scales in a study on plant response to water deficit. Seedlings of Eucalyptus grandis were grown in vitro at water potentials ranging from -0.16 to -0.6 MPa, and evaluated according to growth and biochemical parameters. Data were analyzed through principal component analysis (PCA), which pointed to a hierarchical organization in plant responses to environmental disturbances. Growth parameters (height and dry weight) are more sensitive to water deficit than biochemical ones (sugars, proline, and protein), suggesting that higher hierarchical levels were more sensitive to environmental constraints than lower hierarchical ones. We suggest that before considering an environmental fluctuation as stressful, it is necessary to take into account different levels of plant response, and that the evaluation of the effects of environmental disturbances on an organism depends on the observation scale being used. Hence, a more appropriate stress concept should consider the hierarchical organization of the biological systems, not only for a more adequate theoretical approach, but also for the improvement of practical studies on plants under stress.