Biologia da mosca-branca (Bemisia argentifolii) em tomate e repolho.

bitstream/item/103104/1/pa-1.pd

Avaliação da resistência a Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae) e Euxesta sp. (Diptera: Otitidae) em linhagens de milho-doce.

Sixteen tines of sweet com were evaluated for resistance to Helicoverpa zea (Boddie) and Euxesta sp. Artificial and natural infestation of H. zea (Boddie) were used. The tines DCOl and DC03 were resistant to both pests. No difference was observed between artificial and natural infestation

Diagnostico precoce de ataque de S. Frugiperda em milho com dados de espectroscopia de fluorescencia induzida por laser.

Evento on line. MECAI 2021

Accelerating networks

Evolving out-of-equilibrium networks have been under intense scrutiny recently. In many real-world settings the number of links added per new node is not constant but depends on the time at which the node is introduced in the system. This simple idea gives rise to the concept of accelerating networks, for which we review an existing definition and -- after finding it somewhat constrictive -- offer a new definition. The new definition provided here views network acceleration as a time dependent property of a given system, as opposed to being a property of the specific algorithm applied to grow the network. The defnition also covers both unweighted and weighted networks. As time-stamped network data becomes increasingly available, the proposed measures may be easily carried out on empirical datasets. As a simple case study we apply the concepts to study the evolution of three different instances of Wikipedia, namely, those in English, German, and Japanese, and find that the networks undergo different acceleration regimes in their evolution.Comment: 12 pages, 8 figure

Indicações do manejo de pragas para percevejos.

Epocas de aparecimento; Determinacao da intensidade populacional; Metodos de amostragem; Ocorrencia nas diferentes cultivares; Controle; Biologia; Consideracoes finais.bitstream/item/23229/1/Doc9.pd

Recomendações de inseticidas para utilização no programa de manejo de pragas da soja - safra 1981/82 - nos Estados do Paraná, São Paulo e Mato Grosso do Sul.

bitstream/item/53974/1/11.pd

Structural constraints in complex networks

We present a link rewiring mechanism to produce surrogates of a network where both the degree distribution and the rich--club connectivity are preserved. We consider three real networks, the AS--Internet, the protein interaction and the scientific collaboration. We show that for a given degree distribution, the rich--club connectivity is sensitive to the degree--degree correlation, and on the other hand the degree--degree correlation is constrained by the rich--club connectivity. In particular, in the case of the Internet, the assortative coefficient is always negative and a minor change in its value can reverse the network's rich--club structure completely; while fixing the degree distribution and the rich--club connectivity restricts the assortative coefficient to such a narrow range, that a reasonable model of the Internet can be produced by considering mainly the degree distribution and the rich--club connectivity. We also comment on the suitability of using the maximal random network as a null model to assess the rich--club connectivity in real networks.Comment: To appear in New Journal of Physics (www.njp.org

Mean clustering coefficients: the role of isolated nodes and leafs on clustering measures for small-world networks

Many networks exhibit the small-world property of the neighborhood connectivity being higher than in comparable random networks. However, the standard measure of local neighborhood clustering is typically not defined if a node has one or no neighbors. In such cases, local clustering has traditionally been set to zero and this value influenced the global clustering coefficient. Such a procedure leads to underestimation of the neighborhood clustering in sparse networks. We propose to include $\theta$ as the proportion of leafs and isolated nodes to estimate the contribution of these cases and provide a formula for estimating a clustering coefficient excluding these cases from the Watts and Strogatz (1998 Nature 393 440-2) definition of the clustering coefficient. Excluding leafs and isolated nodes leads to values which are up to 140% higher than the traditional values for the observed networks indicating that neighborhood connectivity is normally underestimated. We find that the definition of the clustering coefficient has a major effect when comparing different networks. For metabolic networks of 43 organisms, relations changed for 58% of the comparisons when a different definition was applied. We also show that the definition influences small-world features and that the classification can change from non-small-world to small-world network. We discuss the use of an alternative measure, disconnectedness D, which is less influenced by leafs and isolated nodes.Comment: final version of the manuscrip