Relative populations and toxin production by Aspergillus flavus and Fusarium verticillioides in artificially inoculated corn at various stages of development under field conditions

The presence, development and production of mycotoxins by Aspergillus flavus and Fusarium verticillioides were studied in corn ears under field conditions after artificial contamination of corn silks. The planted area was divided into five treatments: T1, inoculated with A.flavus solution containing 1 x 10(8) spores, ears covered; T2, inoculated with F. verticillioides solution containing 1 X 10(8) spores, ears covered; T3, inoculated with E verticillioides plus A. flavus solution containing 1 x 10(8) spores of each, ears covered; T4, sprayed with sterile phosphate-buffered saline, ears covered; TS, non-sprayed silks, uncovered ears. Soil and air samples were also collected and analysed for the occurrence of fungi. Water activity, relative air humidity, rainfall and temperature were determined to assess the correlation between abiotic factors and the presence of fungi in the samples. Contamination with the inoculated fungus predominated in T1 and T2. In the other treatments, F. verticillioides was the most frequently isolated contaminant irrespective of treatment. Considering the production of mycotoxins, a positive relation between the production of fumonisins B-1 and B-2 and the frequency of F. verticillioides was statistically verified in all treatments. (C) 2007 Society of Chemical Industry.

Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

Nonexistence of global solutions for a class of complex vector fields on two-torus

The goal of this paper is study the global solvability of a class of complex vector fields of the special form L = partial derivative/partial derivative t + (a + ib)(x)partial derivative/partial derivative x, a, b epsilon C(infinity) (S(1) ; R), defined on two-torus T(2) congruent to R(2)/2 pi Z(2). The kernel of transpose operator L is described and the solvability near the characteristic set is also studied. (c) 2008 Elsevier Inc. All rights reserved.

New insights on the Lewis acidity of diorganolead(IV) compounds: Diphenyllead(IV) complexes with N,O,S-chelating ligands

The reactions of PbPh2(OAC)(2) with alkylglyoxylate thiosemicarbazones (HRGTSC, R = Et, Bu) afforded complexes of the type [PbPh2(GTSC)] center dot H2O, [PbPh2(RGTSC)(2)] and [PbPh2Cl(BUGTSC)]. The structures of HRGTSC (R = Me, Et, Bu), [PbPh2(OAc)(RGTSC)](R = Me, Et, Bu), [PbPh2Cl(BuGTSC)] and [PbPh2(GTSC)] center dot H2O have been studied by X-ray diffraction. [PbPh2(OAc)(RGTSC)] and [PbPh2(GTSC)] center dot H2O have [PbC2NO3S] kernels and the coordination sphere of the metal is pentagonal bipyramidal. [PbPh2Cl(BuGTSC)] has a [PbC2NOSCI] kernel and the coordination geometry around lead is pentagonal bipyramidal with one vacant site. Analysis of the bond distances in [PbPh2(GTSC)] center dot H2O suggests a significant affinity between diphenyllead(IV) and carboxylate donor groups, supporting a borderline acidic character for this organometallic cation. H-1 and C-13 NMR spectra in DMSO-d(6) suggest the partial dissociation of the acetate in [PbPh2(OAc)(RGTSC)] solutions and indicate some differences in the coordination mode of the two RGTSC(-) ligands in [PbPh2(RGTSC)(2)] complexes. (C) 2007 Elsevier Ltd. All rights reserved.

Xyloglucan nano-aggregates: Physico-chemical characterisation in buffer solution and potential application as a carrier for camptothecin, an anti-cancer drug

In this work, native xyloglucan was extracted from Tamarindus indica seeds (XGT), and its properties in phosphate buffer solution (PBS) were evaluated in comparison with a commercial tamarind kernel powder (TKP). The physico-chemical characteristics of the polysaccharides such as molar mass, critical concentration and intrinsic viscosity were determined. Furthermore, using spectroscopic and microscopy techniques, it was observed that the XGs tested can be considered macromolecules able to aggregate as nano-entities of 60-140 nm. The XGT tended to an ordered and compact spherical conformation determined by the Huggins constant, circular dichroism, atomic force microscopy and transmission electron microscopy. After the determination of the properties in PBS the XGs, at concentrations of 25% above their critical aggregation concentration, were used to encapsulate camptothecin, an anti-cancer drug. The XGT sample showed an encapsulation efficiency of 42% and first-order drug delivery kinetics. These results demonstrated the importance of knowledge of the physico-chemical properties of polysaccharides, for example, to better conduct their biotechnological applications as drug carriers. (C) 2010 Elsevier Ltd. All rights reserved.

Dually discrete spaces

A neighbourhood assignment in a space X is a family O = {O-x: x is an element of X} of open subsets of X such that X is an element of O-x for any x is an element of X. A set Y subset of X is a kernel of O if O(Y) = U{O-x: x is an element of Y} = X. We obtain some new results concerning dually discrete spaces, being those spaces for which every neighbourhood assignment has a discrete kernel. This is a strictly larger class than the class of D-spaces of [E.K. van Douwen, W.F. Pfeffer, Some properties of the Sorgenfrey line and related spaces, Pacific J. Math. 81 (2) (1979) 371-377]. (c) 2008 Elsevier B.V. All rights reserved.

Boutet de Monvel`s calculus and groupoids I

Can Boutet de Monvel`s algebra on a compact manifold with boundary be obtained as the algebra Psi(0)(G) of pseudodifferential operators on some Lie groupoid G? If it could, the kernel G of the principal symbol homomorphism would be isomorphic to the groupoid C*-algebra C*(G). While the answer to the above question remains open, we exhibit in this paper a groupoid G such that C*(G) possesses an ideal I isomorphic to G. In fact, we prove first that G similar or equal to Psi circle times K with the C*-algebra Psi generated by the zero order pseudodifferential operators on the boundary and the algebra K of compact operators. As both Psi circle times K and I are extensions of C(S*Y) circle times K by K (S*Y is the co-sphere bundle over the boundary) we infer from a theorem by Voiculescu that both are isomorphic.

Thin films of xyloglucans for BSA adsorption

In this work. XG extracted from Tamarindus indica (XGT) and Copaifera langsdorffii (XGC) seeds were deposited onto Si wafers as thin films. The characteristics of XGT and XGC adsorbed layers were compared with a commercial XG sample (TKP, Tamarind kernel powder) by ellipsometry, and atomic force microscopy (AFM). Moreover, the adsorption of oxidized derivative of XGT (To60) onto amino-terminated Si wafers and the immobilization of bovine serum albumin (BSA) onto polysaccharides covered wafers, as a function of pH, were also investigated. The XG samples presented molar ratios Glc:Xyl:Gal of 2.4:2.1:1 (XGC) 2.8: 23: 1 (XGT) and 1.91.91 (TKP). The structure of XGT and XGC was determined by O-methy alditol acetate derivatization and showed similar features, but XGC confirmed the presence of more alpha-D-Xyl branches due to more beta-D-Gal ends. XGT deposited onto Si adsorbed as fibers and small entities uniformly distributed, as evidenced by AFM, while TPK and XGC formed larger aggregates. The thickness of To60 onto amino-terminated surface was similar to that determined for XGT onto Si wafers. A maximum in the adsorbed amount of BSA occurred close to its isoelectric point (5.5). These findings indicate that XGT and To60 are potential materials for the development of biomaterials and biotechnological devices. (C) 2008 Elsevier B.V. All rights reserved.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

A finite sample improvement of the fixed effects estimator : applied to technical infficiency

The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.

Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Em direção a agentes pedagógicos com dimensões afetivas

O contexto desta tese é a Inteligência Artificial aplicada à Educação, especificamente a área dos Sistemas Tutores Inteligentes (STI). Apesar das características multidisciplinares e interdisciplinares, a preocupação maior do trabalho se dá quanto aos aspectos computacionais. A multidisciplinaridade está na relação entre os aspectos educacionais, filosóficos e psicológicos inerentes a toda construção de um software educacional, e a interdisciplinaridade acontece no relacionamento da IA com a Informática na Educação. Esta tese propõe o uso de aspectos afetivos como apoio à decisão de ação por parte de um STI. As nossas hipóteses fundamentais são: um sistema de ensino e aprendizagem computacional deve levar em consideração fatores afetivos tornando mais flexível a interação; e a arquitetura de um sistema computacional de interação em tempo real com agentes humanos deve prever explicitamente, em sua arquitetura básica, as crenças e o raciocínio afetivos. Para demonstrar essas idéias, foi definida uma arquitetura para apoiar um STI de modo a reconhecer alguns fatores afetivos, representativos de estratégias de ação de agentes humanos em interação com sistemas. Esse reconhecimento é realizado através de construções retiradas dos comportamentos observáveis do agente humano em contextos determinados. A arquitetura prevê um Sistema Multiagente para executar a percepção de fatores afetivos e da conduta do aluno em interação e de um agente pedagógico, representando o tutor. O agente tutor é modelado através de estados mentais e é responsável pelo raciocínio de alto nível. O modelo computacional de agentes de Móra [MÓR2000] foi utilizado para implementar o “kernel cognitivo” (termo cunhado por Móra e Giraffa [GIR99] que designa a parte responsável pela deliberação). O “kernel cognitivo” decide que ações tomar para um conjunto de características de uma avaliação pedagógica. A utilização de fatores afetivos e da avaliação cognitiva de situações emocionais permite a flexibilização das estratégias quanto à adaptabilidade a agentes humanos. Particularmente, foi adotado o enfoque cognitivo para análise de situações, baseado em teorias cognitivistas sobre emoções. O uso de tecnologia multiagente, no enfoque mentalístico, especificamente BDI (Belief, Desire, Intention) e da ferramenta X-BDI, permite a formalização e construção de um tutor atuante na avaliação pedagógica. A modelagem do aluno passa a ser constituída de aspectos qualitativos e quantitativos. Estudos de casos são apresentados, em situações que consideram os fatores afetivos e nas mesmas situações sem estas considerações. As decisões do tutor para agir são analisadas e confrontadas. Os resultados mostram um impacto positivo na adaptabilidade e ação pedagógica do tutor, sendo coerente com as teorias modernas [SAL97],[DAM2000] sobre as emoções que as consideram partes fundamentais para agir. A maior contribuição desta tese está na agregação de raciocínio sobre a afetividade envolvida em situações de ensino aprendizagem de agentes humanos e artificiais e avança dentro da perspectiva de pesquisa do grupo de IA da UFRGS, quanto ao desenvolvimento de Ambientes de Ensino e Aprendizagem modelados com tecnologia multiagente, com o uso da metáfora de estados mentais.