Desempenho de sulcadores no desenvolvimento e produtividade da soja em solo submetido a diferentes níveis de compactação

On the national scene, soybean crop occupies a prominent position in cultivated area and volume production, being cultivated largely in the no tillage system. This system, due to the intense traffic of machines and implements on its surface has caused soil compaction problems, which has caused the yield loss of crops. In order to minimize this effect the seeder-drill uses the systems to opening the furrow by shank or the double disc type. The use of the shank has become commonplace for allowing the disruption of the compacted surface layer, however requires greater energy demand and may cause excessive tillage in areas where there is not observed high levels of compaction. Thus, this study aimed to evaluate the effects of furrowers mechanisms and levels of soil compacting on traction requirement by a seeder-drill and on the growing and productivity of soybean in an Oxisol texture clay, in a two growing seasons. The experimental design consisted of randomized blocks with split plots with the main plots composed of four levels of soil compaction (N0 – no tillage without additional compaction, N1, N2 and N3 – no tillage subjected to compaction through two, four and six passes with tractor, respectively) corresponding to densities of soil 1.16, 1.20, 1.22 and 1.26 g cm-3, and subplots by two furrowers mechanisms (shank and double disc) with four replicates. To evaluate the average, maximum and specific traction force requested by the seeder-drill, was used a load cell, with capacity of 50 kN and sensitivity of 2 mV V-1, coupled between the tractor and seeder-drill, whose data are stored in a datalogger system model CR800 of Campbell Scientific. In addition, were evaluated the bulk density, soil mechanical resistance to penetration, sowing depth, depth and groove width, soil area mobilized, emergence speed index, emergence operation, final plant stand, stem diameter, plant height, average number of seeds per pod, weight of 1,000 seeds, number of pods per plant and crop productivity. Data were subjected to analysis of variance, the mean of furrowers were compared by Tukey test (p≤0.05), while for the factor soil compaction, polynomial regression analysis was adopted, selected models by the criterion of greater R2 and significance (p≤0.05) of equation parameters. Regardless of the crop season, penetration resistance increase as soil compaction levels up to around 0.20 m deep, and bulk density influenced the sowing quality parameters, however, did not affect the crop yield. In the first season, there was a higher productivity with the use of the shank type. In the second crop season, the shank demanded greater energetic requirement with the increase of bulk density and opposite situation with the double disc. The locking of sowing lines allow better performance of the shank to break the compacted layer.

Desempenho de sementes de soja revestidas com biopolímeros

The use of biopolymers that help to fix pesticides efficiently and degrade easily without harming the environment, and still improve the physiological performance of field soybean seed may bring contributions to the soybean yield. This study aimed to evaluate the effect of cassava starch polymers (AM), sodium alginate (ALG) and polyvinyl alcohol (PVOH), in the concentrations 2, 4 and 6 g / 100 ml of solution, in the physiological attributes of seeds soy, seed speed soaking and performance of soybean seeds after three months of storage. The soybean variety used was the NK 7059 RR. The experimental design used for the three studies was a factorial with 48 experimental units: 3 polymers (AM, ALG and PVOH), 4 different concentrations (0%, 2%, 4% and 6%), with four replications, in a completely randomized design. It was observed the level of significance of the factors and their interactions, applying the test F. The polymers were evaluated by the Tukey test at 5% probability, and the concentrations were evaluated by polynomial regression. The witness obtained better results for most variables studied. Among the polymers, the best coating was observed PVOH because it was the less viscous polymer and visually not served as a substrate for microorganisms. However, also, satisfactory results were obtained for the AM and ALG polymers at a concentration of 2%. There was not interference of the polymers studied with regard to reduction of imbibition rate of soybean seeds. The hydrophilicity of polymers, mainly the AM and ALG accelerated soaking seeds harming germination at concentrations 4% and 6%. In general, the higher the concentration of polymers tended to worse results.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Metodologia para a suavização de dados biomecanicos por função não parametrica ponderada local robusta

Universidade Estadual de Campinas. Faculdade de Educação Física

Avaliação da satisfação dos utentes dos serviços farmacêuticos da Unidade Local de Saúde do Norte Alentejano

Mestrado em Intervenção Sócio-Organizacional na Saúde - Área de especialização: Políticas de Administração e Gestão de Serviços de Saúde.

Desenvolvimento de um serviço local de Saúde Mental em Famalicão

RESUMO: O desenvolvimento de serviços locais adequados deve ser baseado numa avaliação sistemática das necessidades e resultados obtidos nos cuidados a uma população de indivíduos identificados como apresentando uma doença mental na área de referenciação do serviço. Neste sentido foram utilizados os seguintes métodos: dados epidemiológicos acerca das necessidades locais e taxas de utilização de serviços a nível nacional e local, este último com base no case-register. Os diagnósticos de maior prevalência em ambulatório são as perturbações de humor e as perturbações neuróticas de stress ou somatoformes, com uma preponderância de doenças mentais comuns (depressão e ansiedade) em serviços de psiquiatria. Constatam-se baixas taxas de abandono da consulta (12%). A idade, a doença e a escolaridade estão correlacionados com o risco de drop-out, mas utilizada a regressão logística, a idade e a escolaridade perdem o seu significado estatístico. Encontram-se taxas reduzidas de drop-out dos indivíduos com psicose ou perturbações bipolares, em virtude da intervenção activa da equipa. Os custos de transporte, a distância ao local de consulta e o tempo de espera para a primeira consulta são barreiras no acesso aos cuidados a nível local. Os cuidadores não se sentem apoiados pela rede de suporte social e queixam-se sobretudo da acessibilidade, mas exibem elevadas taxas de satisfação com os serviços prestados. Decidiu-se apostar numa organização do serviço baseada na comunidade, com intervenções baseadas na evidência, dando prioridade ao doente mental grave e à qualidade dos cuidados.----------- ABSTRACT: The development of appropriate local services should be based on a systematic assessment of the needs and outcomes of the population of individuals identified as mentally ill within the service’s catchment area. A number of methods may be used as proxies in assessing local needs for services, such as service utilization rates found nationally and locally, by case-register. The most prevalent diagnoses in ambulatory care are mood disorders and neurotic, stress and somatoform disorders, with a majority of common mental disorders (depression and anxiety) in psychiatric services. Low dropout rates (12%) are found in ambulatory care. Age, disease and education are correlated with the risk of drop-out, but after using logistic regression, age and education lose their statistical significance. Low drop-out rates are found in individuals with psychosis or bipolar disorders, because the active intervention from the team. The costs of transportation, distance and the waiting time for the first consultation are barriers in access of care locally. Carers do not feel supported by the network of social support and complain primarily of accessibility, but exhibit high levels of satisfaction with the services provided. It was decided to invest in a service organization based in the community with evidence-based interventions, giving priority to severe mental illness and quality of care.

Tritordeum, wheat and triticale yield components under multi-local mediterranean drought conditions

The species x location interaction was of great importance in explaining the behaviour of genetic material. The study presented here shows, for the first time, the performance, under field conditions of the new tritordeum species, compared to wheat and triticale in a wide range of Mediterranean countries (Spain, Lebanon and Tunisia). The results obtained revealed that despite the diversity of environmental conditions, the main differences in yield were due to genotypes, especially to differences between species. The multi-local study with different growth conditions revealed important information about the water availability effect on yield. In the lowest yielding environments (Tunisia rainfed), Tritordeum and triticale yields were equivalent. However under better growth conditions (Spain), tritordeum yield was shown to be lower than wheat and triticale. Interestingly, when water limitation was extended during the pre-anthesis period, differences in tritordeum versus wheat-triticale yield rate were larger than when water stress occurred during anthesis. These variations were explained by the fact that kernel weight has been found as the limiting factor for yield determination in tritordeum, and a delay in the anthesis date may have been the cause for the low kernel weight and low yield under Mediterranean drought conditions. Such differences in yield between tritordeum and wheat or triticale could be explained by the fact that tritordeum is a relatively new species and far fewer resources have been devoted to its improvement when compared to wheat and triticale. Our results suggest that breeding efforts should be directed to an earlier anthesis date and a longer grain filling period. tritordeum proved to have possibilities to be grown under drought environments as a new crop, since its performance was quite close to wheat and triticale. Besides, it has qualitative added values that may improve farmers' income per unit land.

Interactive local bandwidth choice

A tool for user choice of the local bandwidth function for a kernel density estimate is developed using KDE, a graphical object-oriented package for interactive kernel density estimation written in LISP-STAT. The bandwidth function is a cubic spline, whose knots are manipulated by the user in one window, while the resulting estimate appears in another window. A real data illustration of this method raises concerns, because an extremely large family of estimates is available.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Modelo de análise de regressão periódica da precipitação mensal, da bacia atlântico sudeste, no Estado do Paraná

Este trabalho consistiu da realização da análise de regressão periódica dos totais mensais de precipitação de oito localidades do Estado do Paraná. Foram derivados modelos matemáticos que descrevem o transcurso desta variável, considerando um período de doze meses. A análise foi desenvolvida através da álgebra matricial e realizada a expansão em série de Fourier de uma função f(t), periódica e definida para o intervalo. Os dados originais foram ajustados para meses de 30 dias, transformados para normalizar a distribuição. Posteriormente foram determinados todos os componentes harmônicos das funções referentes a cada um dos oito locais. Após a verificação da normalidade dos componentes harmônicos, realizou-se a análise de variância, o que permitiu identificar os componentes harmônicos significativos, que foram escolhidos para fazer parte da equação de regressão correspondente a cada local. Estas equações explicaram de 89,56% a 99,60% da variação devida a meses, considerando o conjunto das localidades estudadas. A probabilidade de ocorrência de um ano conforme o modelo, expressa em porcentagem, variou de 14,19% a 68,42%, enquanto a probabilidade de ocorrência de um ano conforme a média variou de 0,02% a 1,87%.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.