SIG aplicado na caracterização morfométrica da microbacia do Ribeirão Morro Grande - Bofete, SP

Esse trabalho objetivou a aplicação de geoprocessamento na caracterização morfométrica da microbacia do Ribeirão Morro Grande – Bofete (SP) através do Sistema de Informação Geográfica – Selva, visando à preservação, racionalização do seu uso e recuperação ambiental. A microbacia apresenta uma área de 4049ha e está localizada entre os paralelos 22o 50' 05" a 22o 54' 26" de latitude S e 48o 22' 29" a 48o 26' 36" de longitude W Gr. A base cartográfica utilizada foi a carta planialtimétrica de Bofete (SP), em escala 1:50.000 (IBGE, 1968) na extração das curvas de nível, da hidrografia e da topografia, em ambiente de Sistema de Informações Geográficas - Idrisi Selva, para determinação dos índices morfométricos. Os resultados mostram que os baixos valores da densidade de drenagem, associados à presença de rochas permeáveis, facilitam a infiltração da água no solo, diminuindo o escoamento superficial e o risco de erosão e da degradação ambiental, bem como o baixo valor do fator de forma amparado pelo índice de circularidade indica que a microbacia tende a ser mais alongada com menor susceptibilidade à ocorrência de enchentes mais acentuadas. O parâmetro ambiental coeficiente de rugosidade permitiu classificar a microbacia para vocação com floresta e reflorestamento.

Absorção de micronutrientes em plantas de pimentão submetidas a diferentes lâminas de irrigação

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Geoprocessamento aplicado na caracterização morfométrica da microbacia do Ribeirão Descalvado - Botucatu (SP)

The objective of this study was to evaluate geoprocessing to morphometrically characterize the Ribeirão Descalvado micro watershed – Botucatu, SP by the Geographic Information System (GIS) – Selva for preservation, rationalization of its use and environmental restoration. The micro watershed is 2,228.61 ha between the geographic coordinates: 22° 50' 05" to 22° 54' 26" latitude S and 48° 22' 29" to 48° 26' 36" longitude W Gr. The cartographic basis was the planialtimetric chart of Botucatu (SP), 1: 50000 scale (IBGE, 1969), used for extraction of level, hydrography and topography curves to determine morphometric indices. The results showed that low values of drainage density associated with the presence of permeable rocks facilitates ground water infiltration which decreases surface runoff, erosion risks and environmental degradation. The low value of the shape factor supported by the circularity index shows that the micro watershed is more elongated and at lower risk of more pronounced floods. The roughness coefficient environmental parameter classified the micro watershed for forest and reforestation.

Relação entre a resposta espectral da vegetação em diferentes cultivos e níveis freáticos em área de recarga do Sistema Aquífero Guarani

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelos agrometeorológicos para previsão da produtividade e qualidade de bebida natural do cafeeiro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Intensidade do estresse hídrico modula a expressão de aquaporinas durante a reidratação em Sorghum bicolor

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desempenho de métodos de estimativa da evapotranspiração de referência nas localidades de Frederico Westphalen e Palmeira das Missões, RS

A estimativa da evapotranspiração de referência (ETo), usada no balanço hídrico, possibilita quantificar o armazenamento de água no solo, auxiliando no manejo da irrigação. O objetivo do trabalho foi comparar métodos mais simples de estimativa da evapotranspiração de referência com o método Penman-Monteith (FAO), nas escalas diária e de 5, 10, 15 e 30 dias, e mensal, para os municípios de Frederico Westphalen e Palmeira das Missões, no RS. Os métodos avaliados tenderam a melhorar a eficiência com o aumento da escala temporal de estudo, mantendo o mesmo desempenho para ambas as localidades. Os maiores e menores valores de ETo ocorreram nos meses de dezembro e junho, respectivamente. A maioria dos métodos subestimou os valores de ETo. Em qualquer escala temporal, os métodos de Makking e da Radiação FAO24 podem substituir o modelo de Penman-Monteith.

Modified crop model estimation of depleted and potential soybean yield

Despite the great importance of soybeans in Brazil, there have been few applications of soybean crop modeling on Brazilian conditions. Thus, the objective of this study was to use modified crop models to estimate the depleted and potential soybean crop yield in Brazil. The climatic variable data used in the modified simulation of the soybean crop models were temperature, insolation and rainfall. The data set was taken from 33 counties (28 Sao Paulo state counties, and 5 counties from other states that neighbor São Paulo). Among the models, modifications in the estimation of the leaf area of the soybean crop, which includes corrections for the temperature, shading, senescence, CO2, and biomass partition were proposed; also, the methods of input for the model's simulation of the climatic variables were reconsidered. The depleted yields were estimated through a water balance, from which the depletion coefficient was estimated. It can be concluded that the adaptation soybean growth crop model might be used to predict the results of the depleted and potential yield of soybeans, and it can also be used to indicate better locations and periods of tillage.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Avaliação da remoção de matéria orgânica e nutrientes por sorção em solos residuais

A recarga artificial de aquíferos (RAQ) com águas residuais tratadas (ART) é uma prática que pode contribuir para a reposição de volumes de água no solo, que pode ser muito vantajoso em áreas com déficit hídrico ou com sobre-exploração de águas subterrâneas. No entanto, as cargas residuais das ART (p.e. matéria orgânica, nutrientes, metais pesados e microrganismos patogênicos) podem constituir uma desvantagem para a qualidade da água subterrânea, se o solo apresentar condições desfavoráveis para a sua infiltração. Realizaram-se ensaios laboratoriais em batelada para avaliar a remoção de matéria orgânica e nutrientes (formas de nitrogênio e fósforo) na componente fina de um solo residual granítico, proveniente de uma zona previamente selecionada para a infiltração de ART, localizada no nordeste da região da Beira Interior (Quinta de Gonçalo Martins, Guarda, Portugal). Os resultados dos ensaios de sorção em batelada mostram uma boa remoção de P-PO4, por complexação e precipitação, o que indica que o solo apresenta capacidade reativa para remover a carga residual de fosfato das ART. Após realização dos ensaios em batelada, as propriedades do solo mantiveram-se praticamente inalteradas.

Risk assessment for public health from human interaction with ornamental waters

Water is essential to life; nevertheless ingestion of contaminated water could result in death caused by waterborne diseases such as cholera. Pathogens present in the water can cause diseases, other than those resulting from water ingestion, being registered an increase in the number of case reports in recent years. It is not clear if this increase is due either to a better case reporting system or to an increase in microorganism’s virulence. The generalized use of antibiotics in agriculture and animal farming contributed to their dissemination in the environment which promotes microorganism selection and emergence of resistant strains. This phenomenon can be enhanced by the ability of microorganism to persist within complex communities known as biofilms. In the present work we aim to characterize the microbial population present in ornamental waters and perform a risk assessment for public health resulting from human interaction with it.

Fungal Contamination of Sandpits from Recreational Parks and Schools: a Potential Risk for Human Health

Sandpits used by children are frequently visited by wild life which constitutes a source of fungal pathogens and allergenic fungi. This study aimed to take an unannounced snapshot of the urban levels of fungal contaminants in sands, using for this purpose two public recreational parks, three elementary schools and two kindergartens. All samples were from Lisbon and neighboring municipalities and were tested for fungi of clinical interest. Potentially pathogenic fungi were isolated from all samples besides one. Fusarium dimerum (32.4%) was found to be the dominant species in one park and Chrysonilia spp. in the other (46.6%). Fourteen different species and genera were detected and no dermatophytes were found. Of a total of 14 species and genera, the fungi most isolated from the samples of the elementary schools were Penicillium spp. (74%), Cladophialophora spp. (38%) and Cladosporium spp. (90%). Five dominant species and genera were isolated from the kindergartens. Penicillium spp. was the only genus isolated in one, though with remarkably high counts (32500 colony forming units per gram). In the other kindergarten Penicillium spp. were also the most abundant species, occupying 69% of all the fungi found. All of the samples exceeded the Maximum Recommended Value (MRV) for beach sand defined by Brandão et al. 2011, which are currently the only quantitative guidelines available for the same matrix. The fungi found confirm the potential risk of exposure of children to keratinophilic fungi and demonstrates that regular cleaning or replacing of sand needs to be implemented in order to minimize contamination.

Classic and new approaches to evaluate risk assessment for public health related to exposure to Halogenated Polycyclic Aromatic Hydrocarbons present in ludic waters

Risk assessment for public health related to exposure to Halogenated Polycyclic Aromatic Hydrocarbons present in ludic waters

Relação entre a biodiversidade de plantas e os serviços do ecossistema na caatinga

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desempenho de uma lagoa de infiltração na absorção de cheias e na recarga de aquífero

A type of macro drainage solution widely used in urban areas with predomi-nance of closed catchments (basins without outlet) is the implementation of detention and infiltration reservoirs (DIR). This type of solution has the main function of storing surface runoff and to promote soil infiltration and, consequently, aquifer recharge. The practice is to avoid floods in the drainage basin low-lying areas. The catchment waterproofing reduces the distributed groundwater recharge in urban areas, as is the case of Natal city, RN. However, the advantage of DIR is to concentrate the runoff and to promote aquifer recharge to an amount that can surpass the distributed natu-ral recharge. In this paper, we proposed studying a small urban drainage catchment, named Experimental Mirassol Watershed (EMW) in Natal, RN, whose outlet is a DIR. The rainfall-runoff transformation processes, water accumulation in DIR and the pro-cess of infiltration and percolation in the soil profile until the free aquifer were mod-eled and, from rainfall event observations, water levels in DIR and free aquifer water level measurements, and also, parameter values determination, it is was enabled to calibrate and modeling these combined processes. The mathematical modeling was carried out from two numerical models. We used the rainfall-runoff model developed by RIGHETTO (2014), and besides, we developed a one-dimensional model to simu-late the soil infiltration, percolation, redistribution soil water and groundwater in a combined system to the reservoir water balance. Continuous simulation was run over a period of eighteen months in time intervals of one minute. The drainage basin was discretized in blocks units as well as street reaches and the soil profile in vertical cells of 2 cm deep to a total depth of 30 m. The generated hydrographs were transformed into inlet volumes to the DIR and then, it was carried out water balance in these time intervals, considering infiltration and percolation of water in the soil profile. As a re-sult, we get to evaluate the storage water process in DIR as well as the infiltration of water, redistribution into the soil and the groundwater aquifer recharge, in continuous temporal simulation. We found that the DIR has good performance to storage excess water drainage and to contribute to the local aquifer recharge process (Aquifer Dunas / Barreiras).