Mapping and vegetation cover index from cáceres city, Mato Grosso State (MT), Brazil.

Analyzing the urban space related to its own growth allows to identify the identification of the dynamics of the natural elements and the way the intensified anthropic action shapes and at the same time degrades the landscape, which in the present study is the Pantanal biome. The objective of this study is to use high resolution images and vegetation cover indexes to analyze the urban expansion of Cáceres/MT, and to generate financial support for municipal planning and management. For the execution of this research, remote sensing images and a Geographic Information System (GIS) were used, as well as demographic census data. The urban expansion contributed to the removal of 19.62% of the vegetation and to the increase of 15.28% of anthropic use. The Caceres Vegetation Cover Index is high, with vegetation percentages above 30% occurring in 74.42% of the neighborhoods. From the date of the study on the Index of Vegetation Cover for Inhabitant (ICVH) decreased by 37.20%, remained at 32.55% and increased by 30.25%. It was concluded that an increase in the use of urban space contributed to the reduction of vegetation, as well as the decrease of the population associated to the vegetation of the neighborhoods influenced on the decrease of the ICVH.Especial - Geopantanal 2016. Na publicação: João Santos Vila da SILVA

Análise espacial do crescimento urbano de Cáceres/MT, Pantanal mato-grossense.

Resumo. Analisar o espaço urbano em relação ao seu crescimento permite identificar a dinâmica dos elementos naturais e de como a ação antrópica mais intensificada molda e ao mesmo tempo degrada a paisagem, que neste estudo relaciona-se ao bioma Pantanal. Neste trabalho o objetivo foi utilizar imagens de alta resolução dos satélites Quick Bird e Geoeye e dados censitários para investigação do crescimento urbano de Cáceres/MT, no período de 2006 a 2012, visando a geração de subsídios para o planejamento e a gestão municipal. Para a execução da pesquisa adotou-se as geotecnologias, em especial o sensoriamento remoto e o Sistema de Informação Geográfica. Os dados produzidos possibilitaram identificar que a expansão urbana contribui para a remoção 19,62% da vegetação e o aumento de 15,28% do uso antrópico. Conclui-se que houve um aumento do crescimento urbano no período observado e a utilização de geotecnologias para análise multi-temporal de uma área urbana é eficaz para averiguação dos estratos horizontais, possibilitando um planejamento para tomada de medidas de redução de impacto ambiental, principalmente no Pantanal.GeoPantanal 2016

Influência do uso da terra na conservação das massas d'água em sub-bacias do rio Queima-pé, Tangará da Serra-MT/Brasil.

Resumo: O objetivo deste trabalho foi realizar uma análise espaço-temporal do uso e cobertura da terra, através de geotecnologias, buscando diagnosticar sua influência no estado de conservação das massas de águas, referentes às cinco unidades da bacia hidrográfica do rio Queima-pé, situada no município de Tangará da Serra, MT, Brasil. Utilizou-se imagens captadas pelo sensor TM do Landsat 5, dos anos de 1984 e 2011, processadas no Spring, versão 4.3.3 do Inpe e elaborados e quantificados as classes temáticas dos mapas de uso da terra, no Arcgis versão 9.2 da Esri. Identificou-se 12 classes temáticas, destas as mais expressivas foram Pecuária, Floresta Estacional Semidecidual e Cana-de-açúcar. Em geral as massas d?água obtiveram um aumento médio de 0,05% (2,76 ha) em suas áreas, contudo presenciou-se em média 4% (185 ha) de desmatamento, relacionados ao crescimento desordenado da cultura de Cana-de-açúcar, Soja e Influência urbana, principalmente nas sub-bacias Tapera, Cedro e Santa-fé. Portanto, o estado de conservação das massas d?água nas sub-bacias possuem fatores negativos, devido ao uso intenso da terra e contato direto em alguns pontos com as atividades agropastoris e urbanas, que poderá diminuir sensivelmente a qualidade e quantidade da água, com o passar dos anos se não houver um plano de manejo e recuperação destas áreas.Geopantanal 2012

The LAGUNA design study- towards giant liquid based underground detectors for neutrino physics and astrophysics and proton decay searches

The feasibility of a next generation neutrino observatory in Europe is being considered within the LAGUNA design study. To accommodate giant neutrino detectors and shield them from cosmic rays, a new very large underground infrastructure is required. Seven potential candidate sites in different parts of Europe and at several distances from CERN are being studied: Boulby (UK), Canfranc (Spain), Fr\'ejus (France/Italy), Pyh\"asalmi (Finland), Polkowice-Sieroszowice (Poland), Slanic (Romania) and Umbria (Italy). The design study aims at the comprehensive and coordinated technical assessment of each site, at a coherent cost estimation, and at a prioritization of the sites within the summer 2010.Comment: 5 pages, contribution to the Workshop "European Strategy for Future Neutrino Physics", CERN, Oct. 200

The LAGUNA design study- towards giant liquid based underground detectors for neutrino physics and astrophysics and proton decay searches

The feasibility of a next generation neutrino observatory in Europe is being considered within the LAGUNA design study. To accommodate giant neutrino detectors and shield them from cosmic rays, a new very large underground infrastructure is required. Seven potential candidate sites in different parts of Europe and at several distances from CERN are being studied: Boulby (UK), Canfranc (Spain), Fr\'ejus (France/Italy), Pyh\"asalmi (Finland), Polkowice-Sieroszowice (Poland), Slanic (Romania) and Umbria (Italy). The design study aims at the comprehensive and coordinated technical assessment of each site, at a coherent cost estimation, and at a prioritization of the sites within the summer 2010

Microfluidic reprogramming to pluripotency of human somatic cells

Human induced pluripotent stem cells (hiPSCs) have a number of potential applications in stem cell biology and regenerative medicine, including precision medicine. However, their potential clinical application is hampered by the low efficiency, high costs, and heavy workload of the reprogramming process. Here we describe a protocol to reprogram human somatic cells to hiPSCs with high efficiency in 15 d using microfluidics. We successfully downscaled an 8-d protocol based on daily transfections of mRNA encoding for reprogramming factors and immune evasion proteins. Using this protocol, we obtain hiPSC colonies (up to 160 ± 20 mean ± s.d (n = 48)) in a single 27-mm 2 microfluidic chamber) 15 d after seeding ~1,500 cells per independent chamber and under xeno-free defined conditions. Only ~20 µL of medium is required per day. The hiPSC colonies extracted from the microfluidic chamber do not require further stabilization because of the short lifetime of mRNA. The high success rate of reprogramming in microfluidics, under completely defined conditions, enables hundreds of cells to be simultaneously reprogrammed, with an ~100-fold reduction in costs of raw materials compared to those for standard multiwell culture conditions. This system also enables the generation of hiPSCs suitable for clinical translation or further research into the reprogramming process

Sensor fusion and machine learning techniques to improve water cut measurements accuracy in multiphase application

Objectives/Scope: A Multi-Phase Flow Meter (MPFM) performs Water Liquid Ratio (WLR) estimations using a dedicated sensor relying on one physical principle (e.g. electrical impedance). The accuracy of the WLR sensor might also be dependent on the flow properties. An approach based on Machine Learning techniques and multi-sensors data fusion has been implemented to enhance the reliability and accuracy of the WLR estimations in Multi Phase application using onboard sensor measurements of a MPFM. Methods, Procedures, Process: In order to improve the estimations of multi-phase applications, we exploit the availability of historical data collected with heterogeneous sensors; the underlying idea of the proposed approach is to exploit such data with Machine Learning supervised techniques to provide accurate measures. In this work we compare modern supervised learning approaches like Random Forest, Gradient Boosting techniques and Kernel methods. The proposed methods have a relatively simple form that can be deployed also in embedded applications. Results, Observations, Conclusions: In this work, we will show through extensive experiments that the proposed approach could improve the original estimations. The algorithms underlying the proposed approach have been trained using data collected at flow loops test facilities with different flow conditions. The best model has been chosen not only for its predictive performances, but also looking at the computational time needed to make a prediction and considering its robustness to outliers. As expected, depending on the dataset numerosity, the best performing model can change: we provide experimental results for various dataset sizes in order to help practitioners choose the best regression method depending on the available data numerosity. An additional considered aspect is the computational time of the various approaches, which may be a relevant characteristic to be evaluated before rolling out productive solutions. Novel/Additive Information: To increase the accuracy of MPFM, a sensor fusion technique that benefits from the many measurements collected by the MPFM, has been developed. Many different models have been compared on: prediction performances, confidence interval, robustness to outliers, execution time. The resulting model provides enhanced estimations equipped with confidence intervals that can be used for prediction quality assessment and associated risk management