Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação

PEDRO, Edilson da Silva. Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação. 2008. 226f. Tese (Doutorado em Política Científica e Tecnológica) - Universidade Estadual de Campinas, Campinas, 2008.

Parâmetros citogenéticos de espécies comerciais de carangidae (perciformes), com vistas a sua empregabilidade na conservação biológica e piscicultura marinha

Worldwide, families Carangidae and Rachycentridae represent one of the groups most important commercial fish, used for food, and great potential for marine aquaculture. However, the genetic bases that can underpin the future cultivation of these species, cytogenetic between these aspects are very weak. The chromosomal patterns have provided basic data for the exploration of biotechnological processes aimed at handling chromosomal genetic improvement, such as induction of polyploidy, androgenesis and ginogenesis, as well as obtaining monosex stocks and interspecific hybridizations. This paper presents a comprehensive cytogenetic survey in 10 species, seven of the family Carangidae and the monotypic family Rachycentridae. Classical cytogenetic analysis and in situ mapping of multigene sequences were employed, and additionally for the genus Selene and morphotypes of Caranx lugubris, comparisons were made using geometric morphometrics. In general, conservative species exhibit a marked chromosome number (2n=48). Although present in large part, different karyotypic form, retain many characteristics typical of chromosomal Order Perciformes, the high number of elements monobrachyal, Ag-NORs/18S rDNA sites and heterochromatin simply reduced, preferably centromeric. The main mechanisms involved in karyotypic diversification are the pericentric inversions, with secondary action of centric fusions. In addition to physical mapping and chromosome detail for the species are presented and discussed patterns of intra-and interspecific diversity, cytotaxonomic markers. This data set provides a better understanding of these patterns caryoevolutyonary groups and conditions for the development of protocols based on Biotechnology for chromosomal manipulation Atlantic these species

Análise comparativa dos métodos de fatores ponderados na seleção de áreas para aterros sanitários

This study presents a comparative analysis of methodologies about weighted factors considered in the selection of areas for deployment of Sanitary Landfills, applying the methodologies of classification criteria with scoring bands Gomes, Coelho, Erba & Veronez (2000); Waquil et al, 2000. That means, we have the Scoring System used by Union of Municipalities of Bahia and the Quality Index Landfill Waste (IQR) which are applyed for this study in Massaranduba Sanitary Landfill located in the municipality of Ceará Mirim /RN, northeastern of Brazil. The study was conducted in order to classify the methodologies and to give support for future studies on environmental management segment, with main goal to propose suited methodologies which allow safety and rigor during the selection, deployment and management of sanitary landfill, in the Brazilian municipalities, in order to help them in the process to extinction of their dumps, in according of Brazilian Nacional Plan of Solid Waste. During this investigation we have studied the characteristics of the site as morphological, hydrogeological, environmental and socio-economic to permit the installation. We consider important to mention the need of deployment from Rio Grande do Norte State Secretary of Environment and Water (SEMARH), Institute of Sustainable Development and Environment of RN (IDEMA), as well, from Federal and Municipal Governments a public policies for the integrated management of urban solid waste that address environmental preservation and improvement of health conditions of the population of the Rio Grande do Norte

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Voleibol brasileiro : parâmetros fenotípicos e genotípicos na seleção de atletas

Objective: Identify phenotype and genotype parameters of female volleyball players at different performance levels to help in player selection. Methods: We identified characteristics of phenotype and genotype using the somatotype method (Heath Carter); anthropometry (weight, height and fat percentage); dermatoglyphics (Cummins and Midlo s method) as well as applying physical quality tests (Shuttle Run to assess agility and the Sargent Jump Test adapted for spike and block reach). The sample was composed of 179 players (54 from national teams and 125 from state teams). Results: Somatotype was similar among the performance levels in the mesomorphic component. The Height and ectomorphic component were greater in national team players as was spike and block reach. The vertical jump height for the spike was similar between the national under-17 team and the state teams observed, but in the block jump the lower level players were better. The dermatoglyphics characteristics identified were similar among the groups studied. Conclusions: The results of the variables studied show that somatotype, height, spike reach and block reach are fundamental parameters in player selection and in the specific characteristics of each game position of this sport. This paper proposes a multidisciplinary approach applicable in the fields of physical education, medicine and nutrition

Aspectos de patogenicidade e relacionamento genético de isolados clínicos vaginas e anais de Candida albicans oriundos de pacientes com candidíase vulvovaginal

Vulvovaginal candidiasis (VVC) is one of the most common causes of vaginitis and affects about 75% of women of reproductive age. The majority of cases (80 to 90%) are due to C. albicans, the most virulent species of the genus Candida. Virulence attributes are scarcely investigated and the source of infection remains uncertain. Objective: This study aimed to evaluate the virulence factors and genotypes of clinical isolates of C. albicans sequentially obtained from the anus and vagina of patients with sporadic and recurrent VVC. Materials and methods: We analyzed 62 clinical isolates of C. albicans (36 vaginal and 26 anal strains). Direct examination of vaginal and anal samples and colony forming units (CFU) counts were performed. Yeasts were identified using the chromogenic media CHROMagar Candida® and by classical methodology, and phenotypically characterized regarding to virulence factors, including the ability to adhere to epithelial cells, proteinase activity, morphogenesis and biofilm formation. The genotypes of the strains were investigated with ABC genotyping, microsatellite genotyping with primer M13 and RAPD. Results: We found 100% agreement between direct examination and culture of vaginal samples. Filamentous forms were present in most of the samples of vaginal secretion, which presented CFU counts significantly higher than the samples of anal secretion. There was no statistically significant difference between virulence factors of infecting vaginal isolates and those presented by colonizing anal isolates; as well as for the comparison of the vaginal isolates from patients with different clinical conditions (sporadic or recurrent VVC). There was a decrease in the ability to adhere to HBEC, morphogenesis and biofilm formation of the vaginal isolates during the progress of infection. There was an association between the ability to express different virulence factors and the clinical manifestations presented by the patients. Genotype A was the most prevalent (93.6%), followed by genotype C (6.4%). We found maintenance of the same ABC genotype and greater prevalence of microevolution for the vaginal strains of C. albicans sequentially obtained. Vaginal and anal isolates of C. albicans obtained simultaneously from the same patient presented the same ABC genotype and high genetic relatedness. Conclusion: It is noteworthy that the proliferation of yeast and bud-to-hypha transition are important for the establishment of CVV. The expression of virulence factors is important for the pathogenesis of VVC, although it does not seem to be determinant in the transition from colonization to infection or to the installation of recurrent condition. Genotype A seems to be dominant over the others in both vaginal and anal isolates of patients with VVC. The most common scenario was microevolution of the strains of C. albicans in the vaginal environment. It is suggested that the anal reservoir constituted a possible source of vaginal infection, in most cases assessed

Uma abordagem híbrida para o problema de seleção de fornecedores em cadeia de suprimentos

This work presents a hybrid approach for the supplier selection problem in Supply Chain Management. We joined decision-making philosophy by researchers from business school and researchers from engineering in order to deal with the problem more extensively. We utilized traditional multicriteria decision-making methods, like AHP and TOPSIS, in order to evaluate alternatives according decision maker s preferences. The both techiniques were modeled by using definitions from the Fuzzy Sets Theory to deal with imprecise data. Additionally, we proposed a multiobjetive GRASP algorithm to perform an order allocation procedure between all pre-selected alternatives. These alternatives must to be pre-qualified on the basis of the AHP and TOPSIS methods before entering the LCR. Our allocation procedure has presented low CPU times for five pseudorandom instances, containing up to 1000 alternatives, as well as good values for all considered objectives. This way, we consider the proposed model as appropriate to solve the supplier selection problem in the SCM context. It can be used to help decision makers in reducing lead times, cost and risks in their supply chain. The proposed model can also improve firm s efficiency in relation to business strategies, according decision makers, even when a large number of alternatives must be considered, differently from classical models in purchasing literature

Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process

Seleção seqüencial de descritores por análise da semântica para recuperação de imagens baseada no conteúdo

With the rapid growth of databases of various types (text, multimedia, etc..), There exist a need to propose methods for ordering, access and retrieve data in a simple and fast way. The images databases, in addition to these needs, require a representation of the images so that the semantic content characteristics are considered. Accordingly, several proposals such as the textual annotations based retrieval has been made. In the annotations approach, the recovery is based on the comparison between the textual description that a user can make of images and descriptions of the images stored in database. Among its drawbacks, it is noted that the textual description is very dependent on the observer, in addition to the computational effort required to describe all the images in database. Another approach is the content based image retrieval - CBIR, where each image is represented by low-level features such as: color, shape, texture, etc. In this sense, the results in the area of CBIR has been very promising. However, the representation of the images semantic by low-level features is an open problem. New algorithms for the extraction of features as well as new methods of indexing have been proposed in the literature. However, these algorithms become increasingly complex. So, doing an analysis, it is natural to ask whether there is a relationship between semantics and low-level features extracted in an image? and if there is a relationship, which descriptors better represent the semantic? which leads us to a new question: how to use descriptors to represent the content of the images?. The work presented in this thesis, proposes a method to analyze the relationship between low-level descriptors and semantics in an attempt to answer the questions before. Still, it was observed that there are three possibilities of indexing images: Using composed characteristic vectors, using parallel and independent index structures (for each descriptor or set of them) and using characteristic vectors sorted in sequential order. Thus, the first two forms have been widely studied and applied in literature, but there were no records of the third way has even been explored. So this thesis also proposes to index using a sequential structure of descriptors and also the order of these descriptors should be based on the relationship that exists between each descriptor and semantics of the users. Finally, the proposed index in this thesis revealed better than the traditional approachs and yet, was showed experimentally that the order in this sequence is important and there is a direct relationship between this order and the relationship of low-level descriptors with the semantics of the users

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Separação cega de fontes lineares e não lineares usando algoritmo genético, redes neurais artificiais RBF e negentropia de Rényi como medida de independência

Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and Rényi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations

Estudo avaliativo de um algoritmo genético auto-organizável e multiobjetivo utilizando aprendizado de máquina para aplicações de telecomunicações

This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Determinação de parâmetros (sólidos solúveis, pH e acidez titulável) em ameixas intactas usando espectroscopia no infravermelho próximo e seleção de comprimento de onda

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums

Uma Análise de métodos de distriubuição de atributos em comitês de classificadores

The objective of the researches in artificial intelligence is to qualify the computer to execute functions that are performed by humans using knowledge and reasoning. This work was developed in the area of machine learning, that it s the study branch of artificial intelligence, being related to the project and development of algorithms and techniques capable to allow the computational learning. The objective of this work is analyzing a feature selection method for ensemble systems. The proposed method is inserted into the filter approach of feature selection method, it s using the variance and Spearman correlation to rank the feature and using the reward and punishment strategies to measure the feature importance for the identification of the classes. For each ensemble, several different configuration were used, which varied from hybrid (homogeneous) to non-hybrid (heterogeneous) structures of ensemble. They were submitted to five combining methods (voting, sum, sum weight, multiLayer Perceptron and naïve Bayes) which were applied in six distinct database (real and artificial). The classifiers applied during the experiments were k- nearest neighbor, multiLayer Perceptron, naïve Bayes and decision tree. Finally, the performance of ensemble was analyzed comparatively, using none feature selection method, using a filter approach (original) feature selection method and the proposed method. To do this comparison, a statistical test was applied, which demonstrate that there was a significant improvement in the precision of the ensembles