Virulência de Aphanocladium album (Preuss) Gams e Verticillium lecanii (Zimm.) Viégas (Deuteromycotina: Hyphomycetes) para o Percevejo-de-renda da seringueira, Leptopharsa heveae (Drake & Poor) (Hemiptera: Tingidae)

The lace bug, Leptopharsa heveae is an insect that causes serious damage on rubber trees. In laboratory condition, the strains of Verticillium lecanii (ARSEF 6430, 6431 e 6432) and Aphanocladium album (ARSEF 6433) were tested on third-and fifth-instar nymphs and adults of L. heveae to evaluate their virulence using 2.4 x 10(5) and 2.4 x 10(7) conidia/mL. The bioassays were carried out using Petri dishes whose inner bottoms were covered with damp filter papers. Each Petri dish contained five insects and one rubber tree leaflet. The plates were covered with PVC film to provide high relative humidity, maintained at 26 +/- 0.5 degrees C and a photophase of 14 hours. The Probit analysis was calculated from mortality date of insects killed by fungi. In the highest concentrations, ARSEF 6430 was more virulent for third instar nymphs, and the LT50 was 1.9 days. For the fifth instar, the strains ARSEF 6430, 6433 and 6432 showed similar virulence with LT50 of 2.6, 2.6 and 3.2 days, respectively. For adults, ARSEF 6431 was the most virulent strain with the LT50 recorded at 2.0 days. The smallest concentration did not always cause more than 50% mortality.

Patogenicidade de Lecanicillium lecanii (ZIMM.) Zare & GAMS ao ácaro rajado Tetranychus urticae (Acari:Tetranychidae) e sua compatibilidade a agrotóxicos e organismos biocontroladores utilizados na cultura do crisântemo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Notas sobre GAMS

Notas sobre el programa GAMS (incluye archivo *.gms con los ejemplos).

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models.

Modelling discard ogives from Irish demersal fisheries

Annual discard ogives were estimated using generalized additive models (GAMs) for four demersal fish species: whiting, haddock, megrim, and plaice. The analysis was based on data collected on board commercial vessels and at Irish fishing ports from 1995 to 2003. For all species the most important factors influencing annual discard ogives were fleet (combination of gear, fishing ground, and targeted species), mean length of the catch and year, and, for megrim, also minimum landing size. The length at which fish are discarded has increased since 2000 for haddock, whiting, and plaice. In contrast, discarded length has decreased for megrim, accompanying a reduction in minimum landing size in 2000.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.

Biokaasun tuotannon ja lietelannan jakeistamisen taloudelliset vaikutukset nautatilalle

Työn tavoitteena on tutkia nautatilojen lannan käsittelytapojen taloudellista kannattavuutta. Tutkimuksessa verrataan lietelannan vaihtoehtoisia käsittelytapoja, joita ovat (a) lietteen levittäminen peltoon ilman esikäsittelyä, (b) lietelannan jakeistaminen typpipitoiseen nestejakeeseen ja fosforipitoiseen kuivajakeeseen sekä (c) lannan hyödyntäminen biokaasun tuotannossa. Aineistona tutkimuksessa käytetään aiempia tutkimustuloksia sekä Vieremällä sijaitsevien nautatilojen (21 kpl) tietoja. Menetelmänä käytetään lineaarista ohjelmointia ja malli ratkaistaan GAMS -ohjelmaa käyttämällä. Teoriana tutkimuksessa on tuotanto- ja kustannusteoria. Tutkimuksessa tarkastellaan kuinka eläintiheys, peltolohkorakenne sekä kustannusten muutokset vaikuttavat lannankäsittelyyn. Tutkimuksessa havaitaan, että kun kaikkea lietettä ei voida levittää tilan hallinnassa oleville pelloille, on lietteen jakeistaminen kannattavaa, jos jakeistuskustannus ei ylitä 1,2 €/m3. Sijoituslevityksen havaitaan olevan hajalevitystä kannattavampaa, paitsi typen hinnan ollessa hyvin alhainen hajalevitys on kannattavampi vaihtoehto. Korkea typpikustannus ja lietteen jakeistuskustannus tekevät biokaasun tuotannosta kannattavan vaihtoehdon, jos biokaasulaitos sijaitsee hyvin lähellä tilaa. Lietteen jakeistaminen on kannattavaa alueilla, joilla lannanlevityspinta-alasta on niukkuutta. Jakeistettavaa lietettä on kuitenkin oltava vähintään 20 000 m3/a, jotta jakeistuslaitteen hankkiminen on kannattavaa. Biokaasun tuotannon ei katsota olevan tällä hetkellä kannattava lannan käsittelyvaihtoehto, koska laitoksen olisi saatava tarvittava raaka-ainemäärä kaikissa tapauksissa alle kahden kilometrin säteeltä, eikä tämän katsota onnistuvan. Vieremän tiloista ainoastaan yksi tila sijaitsee riittävän lähellä suunniteltua laitoksen sijaintipaikkaa.

Periglasiaalisten ilmiöiden alueellinen mallintaminen subarktisessa ympäristössä

Periglacial processes act on cold, non-glacial regions where the landscape deveploment is mainly controlled by frost activity. Circa 25 percent of Earth's surface can be considered as periglacial. Geographical Information System combined with advanced statistical modeling methods, provides an efficient tool and new theoretical perspective for study of cold environments. The aim of this study was to: 1) model and predict the abundance of periglacial phenomena in subarctic environment with statistical modeling, 2) investigate the most import factors affecting the occurence of these phenomena with hierarchical partitioning, 3) compare two widely used statistical modeling methods: Generalized Linear Models and Generalized Additive Models, 4) study modeling resolution's effect on prediction and 5) study how spatially continous prediction can be obtained from point data. The observational data of this study consist of 369 points that were collected during the summers of 2009 and 2010 at the study area in Kilpisjärvi northern Lapland. The periglacial phenomena of interest were cryoturbations, slope processes, weathering, deflation, nivation and fluvial processes. The features were modeled using Generalized Linear Models (GLM) and Generalized Additive Models (GAM) based on Poisson-errors. The abundance of periglacial features were predicted based on these models to a spatial grid with a resolution of one hectare. The most important environmental factors were examined with hierarchical partitioning. The effect of modeling resolution was investigated with in a small independent study area with a spatial resolution of 0,01 hectare. The models explained 45-70 % of the occurence of periglacial phenomena. When spatial variables were added to the models the amount of explained deviance was considerably higher, which signalled a geographical trend structure. The ability of the models to predict periglacial phenomena were assessed with independent evaluation data. Spearman's correlation varied 0,258 - 0,754 between the observed and predicted values. Based on explained deviance, and the results of hierarchical partitioning, the most important environmental variables were mean altitude, vegetation and mean slope angle. The effect of modeling resolution was clear, too coarse resolution caused a loss of information, while finer resolution brought out more localized variation. The models ability to explain and predict periglacial phenomena in the study area were mostly good and moderate respectively. Differences between modeling methods were small, although the explained deviance was higher with GLM-models than GAMs. In turn, GAMs produced more realistic spatial predictions. The single most important environmental variable controlling the occurence of periglacial phenomena was mean altitude, which had strong correlations with many other explanatory variables. The ongoing global warming will have great impact especially in cold environments on high latitudes, and for this reason, an important research topic in the near future will be the response of periglacial environments to a warming climate.

Matrix method for eigenstructure assignment - The multi-input case with application

The eigenvalue and eigenstructure assignment procedure has found application in a wide variety of control problems. In this paper a method for assigning eigenstructure to a linear time invariant multi-input system is proposed. The algorithm determines a matrix that has eigenvalues and eigenvectors at the desired locations. It is obtained from the knowledge of the open-loop system and the desired eigenstructure. Solution of the matrix equation, involving unknown controller gams, open-loop system matrices, and desired eigenvalues and eigenvectors, results hi the state feedback controller. The proposed algorithm requires the closed-loop eigenvalues to be different from those of the open-loop case. This apparent constraint can easily be overcome by a negligible shift in the values. Application of the procedure is illustrated through the offset control of a satellite supported, from an orbiting platform, by a flexible tether.

Use of generalised additive models to categorise continuous variables in clinical prediction

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Aplicação de técnicas de otimização no gerenciamento da deposição em redes de trocadores de calor

Deposição é um fenômeno indesejável que ocorre na superfície dos trocadores de calor ao longo de sua operação, ocasionando redução na efetividade térmica e aumento da resistência ao escoamento nestes equipamentos. Estes efeitos trazem grandes consequências econômicas e ambientais, devido ao aumento dos custos operacionais (energia adicional é requerida), aumento dos custos de projeto (demanda por equipamentos de maior área de troca térmica), limitações hidráulicas (que pode levar a uma diminuição da carga processada) e aumento das emissões (aumento da queima de combustíveis fósseis para suprir a energia adicional requerida). Neste contexto, o presente trabalho tem por objetivo fornecer ferramentas computacionais robustas que apliquem técnicas de otimização para o gerenciamento da deposição em redes de trocadores de calor, visando minimizar os seus efeitos negativos. Estas ferramentas foram desenvolvidas utilizando programação matemática no ambiente computacional GAMS, e três abordagens distintas para a resolução do problema da deposição foram pesquisadas. Uma delas consiste na identificação do conjunto ótimo de trocadores de calor a serem limpos durante uma parada para manutenção da planta, visando restaurar a carga térmica nesses equipamentos através da remoção dos depósitos existentes. Já as duas outras abordagens consistem em otimizar a distribuição das vazões das correntes ao longo de ramais paralelos, uma de forma estacionária e a outra de forma dinâmica, visando maximizar a recuperação de energia ao longo da rede. O desempenho destas três abordagens é ilustrado através de um conjunto de exemplos de redes de trocadores de calor, onde os ganhos reais obtidos com estas ferramentas de otimização desenvolvidas são demonstrados

Seasonal plankton dynamics along a cross-shelf gradient off northern Spain

The development of population models able to reproduce the dynamics of zooplankton is a central issue when trying to understand how a changing environment would affect zooplankton in the future. Using 10 years of monthly data on phytoplankton and zooplankton abundance in the Bay of Biscay from the IEO's RADIALES time-series programme, we built non-parametric Generalized Additive Models (GAMs) able to reproduce the dynamics of plankton on the basis of environmental factors (nutrients, temperature, upwelling and photoperiod). We found that the interaction between these two plankton components is approximately linear, whereas the effects of environmental factors are non-linear. With the inclusion of the environmental variability, the main seasonal and inter-annual dynamic patterns observed within the studied plankton assemblage indicate the prevalence of bottom-up regulatory control. The statistically deduced models were used to simulate the dynamics of the phytoplankton and zooplankton. A good agreement between observations and simulations was obtained, especially for zooplankton. We are presently developing spatio-temporal GAM models for the North Sea based on the Continuous Plankton Recorder database.

Quantifying the impact of environmental variables upon catch-per-unit-effort of the Blue Shark Prionace glauca in the Western English Channel

The effect of environmental variables on blue shark Prionace glauca catch per unit effort (CPUE) in a recreational fishery in the western English Channel, between June and September 1998–2011, was quantified using generalized additive models (GAMs). Sea surface temperature (SST) explained 1·4% of GAM deviance, and highest CPUE occurred at 16·7° C, reflecting the optimal thermal preferences of this species. Surface chlorophyll a concentration (CHL) significantly affected CPUE and caused 27·5% of GAM deviance. Additionally, increasing CHL led to rising CPUE, probably due to higher productivity supporting greater prey biomass. The density of shelf-sea tidal mixing fronts explained 5% of GAM deviance, but was non-significant, with increasing front density negatively affecting CPUE. Time-lagged frontal density significantly affected CPUE, however, causing 12·6% of the deviance in a second GAM and displayed a positive correlation. This outcome suggested a delay between the evolution of frontal features and the subsequent accumulation of productivity and attraction of higher trophic level predators, such as P. glauca.

Quantifying the impact of environmental variables upon catch-per-unit-effort of the Blue Shark Prionace glauca in the Western English Channel

The effect of environmental variables on blue shark Prionace glauca catch per unit effort (CPUE) in a recreational fishery in the western English Channel, between June and September 1998–2011, was quantified using generalized additive models (GAMs). Sea surface temperature (SST) explained 1·4% of GAM deviance, and highest CPUE occurred at 16·7° C, reflecting the optimal thermal preferences of this species. Surface chlorophyll a concentration (CHL) significantly affected CPUE and caused 27·5% of GAM deviance. Additionally, increasing CHL led to rising CPUE, probably due to higher productivity supporting greater prey biomass. The density of shelf-sea tidal mixing fronts explained 5% of GAM deviance, but was non-significant, with increasing front density negatively affecting CPUE. Time-lagged frontal density significantly affected CPUE, however, causing 12·6% of the deviance in a second GAM and displayed a positive correlation. This outcome suggested a delay between the evolution of frontal features and the subsequent accumulation of productivity and attraction of higher trophic level predators, such as P. glauca.