NIR spectrocospy can evaluate the crystallinity and the tensile and burst strenghts of nanocellulosic films.

The near infrared (NIR) spectroscopy presents itself as an interesting non-destructive test tool as it enables a fast, simple and reliable way for characterizing large samplings of biological materials in a short period of time. This work aimed to establish multivariate models to estimate the crystallinity indices and tensile and burst strength of cellulosic and nanocellulosic films through NIR spectroscopy. NIR spectra were recorded from the films before tensile and bursting strength, and crystallinity tests. Spectral information were correlated with reference values obtained by laboratory procedures through partial least square regression (PLS-R). The PLS-R model for estimating the crystallinity index presented a coefficient of determination in cross-validation (R2cv) of 0,94 and the ratio of performance to deviation (RPD) was 3,77. The mechanical properties of the films presented a high correlation with the NIR spectra: R2p = 0,85 (RPD = 2,23) for tensile and R2p = 0,93 (RPD = 3,40) for burst strength. The statistics associated to the models presented have shown that the NIR spectroscopy has the potential to estimate the crystallinity index and resistance properties of cellulose and nanocellulose films on in-line monitoring systems.

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Mathematical relationships for metal build-up on urban road surfaces based on traffic and land use characteristics

The study investigated the influence of traffic and land use parameters on metal build-up on urban road surfaces. Mathematical relationships were developed to predict metals originating from fuel combustion and vehicle wear. The analysis undertaken found that nickel and chromium originate from exhaust emissions, lead, copper and zinc from vehicle wear, cadmium from both exhaust and wear and manganese from geogenic sources. Land use does not demonstrate a clear pattern in relation to the metal build-up process, though its inherent characteristics such as traffic activities exert influence. The equation derived for fuel related metal load has high cross-validated coefficient of determination (Q2) and low Standard Error of Cross-Validation (SECV) values indicates that the model is reliable, while the equation derived for wear-related metal load has low Q2 and high SECV values suggesting its use only in preliminary investigations. Relative Prediction Error values for both equations are considered to be well within the error limits for a complex system such as an urban road surface. These equations will be beneficial for developing reliable stormwater treatment strategies in urban areas which specifically focus on mitigation of metal pollution.

Estimating winter crop area across seasons and regions using time-sequential MODIS imagery.

The wheat grain industry is Australia's second largest agricultural export commodity. There is an increasing demand for accurate, objective and near real-time crop production information by industry. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to capture reflectance over large areas at acceptable pixel scale, cost and accuracy. The use of multi-temporal MODIS-enhanced vegetation index (EVI) imagery to determine crop area was investigated in this article. Here the rigour of the harmonic analysis of time-series (HANTS) and early-season metric approaches was assessed when extrapolating over the entire Queensland (QLD) cropping region for the 2005 and 2006 seasons. Early-season crop area estimates, at least 4 months before harvest, produced high accuracy at pixel and regional scales with percent errors of -8.6% and -26% for the 2005 and 2006 seasons, respectively. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. The errors for specific area estimates for wheat, barley and chickpea were 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006), respectively. Area estimates of total winter crop, wheat, barley and chickpea resulted in coefficient of determination (R(2)) values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high coefficient of determination (0.87) was achieved for total winter crop area estimates in August across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. The extrapolability of these approaches to determine total and specific winter crop area estimates, well before flowering, showed good utility across larger areas and seasons. Hence, it is envisaged that this technology might be transferable to different regions across Australia.

Estimating Hydrogen Cyanide in Forage Sorghum (Sorghum bicolor) by Near-Infrared Spectroscopy

Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (MRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R-2) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R-2 = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R-2 = 0.847 and standard error of calibration (SEC) = 0.050% and a R-2 = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C=O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.

Lypsylehmän energiataseen mallintaminen tuotoskauden alkuvaiheessa

Energiataseen mallinnus on osa KarjaKompassi-hankkeeseen liittyvää kehitystyötä. Tutkielman tavoitteena oli kehittää lypsylehmän energiatasetta etukäteen ennustavia ja tuotoskauden aikana saatavia tietoja hyödyntäviä matemaattisia malleja. Selittävinä muuttujina olivat dieetti-, rehu-, maitotuotos-, koelypsy-, elopaino- ja kuntoluokkatiedot. Tutkimuksen aineisto kerättiin 12 Suomessa tehdyistä 8 – 28 laktaatioviikon pituisesta ruokintakokeesta, jotka alkoivat heti poikimisen jälkeen. Mukana olleista 344 lypsylehmästä yksi neljäsosa oli friisiläis- ja loput ayshire-rotuisia. Vanhempien lehmien päätiedosto sisälsi 2647 havaintoa (koe * lehmä * laktaatioviikko) ja ensikoiden 1070. Aineisto käsiteltiin SAS-ohjelmiston Mixed-proseduuria käyttäen ja poikkeavat havainnot poistettiin Tukeyn menetelmällä. Korrelaatioanalyysillä tarkasteltiin energiataseen ja selittävien muuttujien välisiä yhteyksiä. Energiatase mallinnettiin regressioanalyysillä. Laktaatiopäivän vaikutusta energiataseeseen selitettiin viiden eri funktion avulla. Satunnaisena tekijänä mallissa oli lehmä kokeen sisällä. Mallin sopivuutta aineistoon tarkasteltiin jäännösvirheen, selitysasteen ja Bayesin informaatiokriteerin avulla. Parhaat mallit testattiin riippumattomassa aineistossa. Laktaatiopäivän vaikutusta energiataseeseen selitti hyvin Ali-Schaefferin funktio, jota käytettiin perusmallina. Kaikissa energiatasemalleissa vaihtelu kasvoi laktaatioviikosta 12. alkaen, kun havaintojen määrä väheni ja energiatase muuttui positiiviseksi. Ennen poikimista käytettävissä olevista muuttujista dieetin väkirehuosuus ja väkirehun syönti-indeksi paransivat selitysastetta ja pienensivät jäännösvirhettä. Ruokinnan onnistumista voidaan seurata maitotuotoksen, maidon rasvapitoisuuden ja rasva-valkuaissuhteen tai EKM:n sisältävillä malleilla. EKM:n vakiointi pienensi mallin jäännösvirhettä. Elopaino ja kuntoluokka olivat heikkoja selittäjiä. Malleja voidaan hyödyntää karjatason ruokinnan suunnittelussa ja seurannassa, mutta yksittäisen lehmän energiataseen ennustamiseen ne eivät sovellu.

Onko oppimistulokset valettu betoniin? : Tutkimus Helsingin kaupunkirakenteen ja peruskoulujen oppimistulosten välisestä yhteydestä ja kouluvalintojen vaikutuksista

The aim of this study is to find out how urban segregation is connected to the differentiation in educational outcomes in public schools. The connection between urban structure and educational outcomes is studied on both the primary and secondary school level. The secondary purpose of this study is to find out whether the free school choice policy introduced in the mid-1990´s has an effect on the educational outcomes in secondary schools or on the observed relationship between the urban structure and educational outcomes. The study is quantitative in nature, and the most important method used is statistical regression analysis. The educational outcome data ranging the years from 1999 to 2002 has been provided by the Finnish National Board of Education, and the data containing variables describing the social and physical structure of Helsinki has been provided by Statistics Finland and City of Helsinki Urban Facts. The central observation is that there is a clear connection between urban segregation and differences in educational outcomes in public schools. With variables describing urban structure, it is possible to statistically explain up to 70 % of the variation in educational outcomes in the primary schools and 60 % of the variation in educational oucomes in the secondary schools. The most significant variables in relation to low educational outcomes in Helsinki are abundance of public housing, low educational status of the adult population and high numbers of immigrants in the school's catchment area. The regression model has been constructed using these variables. The lower coefficient of determination in the educational outcomes of secondary schools is mostly due to the effects of secondary school choice. Studying the public school market revealed that students selecting a secondary school outside their local catchment area cause an increase in the variation of the educational outcomes between secondary schools. When the number of students selecting a school outside their local catchment area is taken into account in the regressional model, it is possible to explain up to 80 % of the variation in educational outcomes in the secondary schools in Helsinki.

The fishery for California market squid (Loligo opalescens) (Cephalopoda: Myopsida), from 1981 through 2003

The California market squid (Loligo opalescens) has been harvested since the 1860s and it has become the largest fishery in California in terms of tonnage and dollars since 1993. The fishery began in Monterey Bay and then shifted to southern California, where effort has increased steadily since 1983. The California Department of Fish and Game (CDFG) collects information on landings of squid, including tonnage, location, and date of capture. We compared landings data gathered by CDFG with sea surface temperature (SST), upwelling index (UI), the southern oscillation index (SOI), and their respective anomalies. We found that the squid fishery in Monterey Bay expends twice the effort of that in southern California. Squid landings decreased substantially following large El Niño events in 1982−83 and 1997−98, but not following the smaller El Niño events of 1987 and 1992. Spectral analysis revealed autocorrelation at annual and 4.5-year intervals (similar to the time period between El Niño cycles). But this analysis did not reveal any fortnightly or monthly spawning peaks, thus squid spawning did not correlate with tides. A paralarvae density index (PDI) for February correlated well with catch per unit of effort (CPUE) for the following November recruitment of adults to the spawning grounds. This stock– recruitment analysis was significant for 2000−03 (CPUE=8.42+0.41PDI, adjusted coefficient of determination, r2=0.978, P=0.0074). Surveys of squid paralarvae explained 97.8% of the variance for catches of adult squid nine months later. The regression of CPUE on PDI could be used to manage the fishery. Catch limits for the fishery could be set on the basis of paralarvae abundance surveyed nine months earlier.

Aplicação do modelo hidrológico SWMM na gestão das águas pluviais urbanas: estudo de caso da bacia hidrográfica do Rio Morto, Rio de Janeiro.

O crescimento da população e dos núcleos urbanos durante o século XX, sobretudo nos países em desenvolvimento, contribuiu para o aumento das áreas impermeáveis das bacias hidrográficas, com impactos importantes nos sistemas de drenagem urbana e na ocorrência de enchentes associadas. As enchentes trazem prejuízos materiais, na saúde e sociais. Recentemente, têm sido propostas práticas conservacionistas e medidas compensatórias, que buscam contribuir para o controle das enchentes urbanas, através do retardo do pico e amortecimento dos hidrogramas. Modelos matemáticos hidrológicos-hidráulicos permitem a simulação da adoção destas medidas de controle, demonstrando e otimizando sua localização. Esta dissertação apresenta os resultados da aplicação do modelo hidrológico Storm Water Management Model (SWMM) à bacia hidrográfica de estudo e representativa do rio Morto localizada em área peri-urbana em Jacarepaguá na cidade do Rio de Janeiro, com área de 9,41 km. O processamento do modelo SWMM foi realizado com o apoio da interface Storm and Sanitary Analysis (SSA), integrada ao sistema AutoCAD Civil 3D. Além da verificação da adequabilidade do modelo à representação dos sistemas hidrológico e hidráulico na bacia, foram desenvolvidos estudos para dois cenários como medidas de controle de enchentes: cenário 1, envolvendo implantação de um reservatório de detenção e, cenário 2, considerando a implantação de reservatórios de águas pluviais nos lotes. Os hidrogramas resultantes foram comparados ao hidrograma resultante da simulação nas condições atuais. Além disso, foram avaliados os custos associados a cada um dos cenários usando o sistema de orçamento da Empresa Rio Águas da PCRJ. Nas simulações foram adotadas a base cartográfica, e os dados climatológicos e hidrológicos previamente observados no contexto do projeto HIDROCIDADES, Rede de Pesquisa BRUM/FINEP, na qual este estudo se insere. Foram representados os processos de geração e propagação do escoamento superficial e de base. Durante o processo de calibração, realizou-se a análise de sensibilidade dos parâmetros, resultando como parâmetros mais sensíveis os relativos às áreas impermeáveis, especialmente o percentual de área impermeável da bacia (Ai). A calibração foi realizada através do ajuste manual de sete parâmetros do escoamento superficial e cinco do escoamento de base para três eventos. Foram obtidos coeficientes de determinação entre 0,52 e 0,64, e a diferença entre os volumes escoados e observados entre 0,60% e 4,96%. Para a validação do modelo foi adotado um evento pluviométrico excepcional observado na cidade em abril de 2010, que à época causou enchentes e grandes transtornos na cidade. Neste caso, o coeficiente de determinação foi igual a 0,78 e a diferença entre volumes foi de 15%. As principais distorções entre hidrogramas observados e simulados foram verificados para as vazões máximas. Em ambos os cenários as enchentes foram controladas. A partir destes estudos, pôde-se concluir que o melhor custo-benefício foi o cenário 2. Para este cenário, foi observado maiores amortecimento e retardo da vazão de pico do hidrograma, igual a 21,51% da vazão simulada para as condições atuais da bacia. Os custos de implantação orçados para os reservatórios de lote ficaram 52% a menos do que o do reservatório de detenção.

Estudo de níveis de ozônio troposférico usando métodos de estatística univariada e multivariada para duas localidades da cidade do Rio de Janeiro

Com cada vez mais intenso desenvolvimento urbano e industrial, atualmente um desafio fundamental é eliminar ou reduzir o impacto causado pelas emissões de poluentes para a atmosfera. No ano de 2012, o Rio de Janeiro sediou a Rio +20, a Conferência das Nações Unidas sobre Desenvolvimento Sustentável, onde representantes de todo o mundo participaram. Na época, entre outros assuntos foram discutidos a economia verde e o desenvolvimento sustentável. O O3 troposférico apresenta-se como uma variável extremamente importante devido ao seu forte impacto ambiental, e conhecer o comportamento dos parâmetros que afetam a qualidade do ar de uma região, é útil para prever cenários. A química das ciências atmosféricas e meteorologia são altamente não lineares e, assim, as previsões de parâmetros de qualidade do ar são difíceis de serem determinadas. A qualidade do ar depende de emissões, de meteorologia e topografia. Os dados observados foram o dióxido de nitrogênio (NO2), monóxido de nitrogênio (NO), óxidos de nitrogênio (NOx), monóxido de carbono (CO), ozônio (O3), velocidade escalar vento (VEV), radiação solar global (RSG), temperatura (TEM), umidade relativa (UR) e foram coletados através da estação móvel de monitoramento da Secretaria do Meio Ambiente (SMAC) do Rio de Janeiro em dois locais na área metropolitana, na Pontifícia Universidade Católica (PUC-Rio) e na Universidade do Estado do Rio de Janeiro (UERJ) no ano de 2011 e 2012. Este estudo teve três objetivos: (1) analisar o comportamento das variáveis, utilizando o método de análise de componentes principais (PCA) de análise exploratória, (2) propor previsões de níveis de O3 a partir de poluentes primários e de fatores meteorológicos, comparando a eficácia dos métodos não lineares, como as redes neurais artificiais (ANN) e regressão por máquina de vetor de suporte (SVM-R), a partir de poluentes primários e de fatores meteorológicos e, finalmente, (3) realizar método de classificação de dados usando a classificação por máquina de vetor suporte (SVM-C). A técnica PCA mostrou que, para conjunto de dados da PUC as variáveis NO, NOx e VEV obtiveram um impacto maior sobre a concentração de O3 e o conjunto de dados da UERJ teve a TEM e a RSG como as variáveis mais importantes. Os resultados das técnicas de regressão não linear ANN e SVM obtidos foram muito próximos e aceitáveis para o conjunto de dados da UERJ apresentando coeficiente de determinação (R2) para a validação, 0,9122 e 0,9152 e Raiz Quadrada do Erro Médio Quadrático (RMECV) 7,66 e 7,85, respectivamente. Quanto aos conjuntos de dados PUC e PUC+UERJ, ambas as técnicas, obtiveram resultados menos satisfatórios. Para estes conjuntos de dados, a SVM mostrou resultados ligeiramente superiores, e PCA, SVM e ANN demonstraram sua robustez apresentando-se como ferramentas úteis para a compreensão, classificação e previsão de cenários da qualidade do ar

A habitat-use model to determine essential fish habitat for juvenile brown shrimp (Farfantepenaeus aztecus) in Galveston Bay, Texas

A density prediction model for juvenile brown shrimp (Farfantepenaeus aztecus) was developed by using three bottom types, five salinity zones, and four seasons to quantify patterns of habitat use in Galveston Bay, Texas. Sixteen years of quantitative density data were used. Bottom types were vegetated marsh edge, submerged aquatic vegetation, and shallow nonvegetated bottom. Multiple regression was used to develop density estimates, and the resultant formula was then coupled with a geographical information system (GIS) to provide a spatial mosaic (map) of predicted habitat use. Results indicated that juvenile brown shrimp (<100 mm) selected vegetated habitats in salinities of 15−25 ppt and that seagrasses were selected over marsh edge where they co-occurred. Our results provide a spatially resolved estimate of high-density areas that will help designate essential fish habitat (EFH) in Galveston Bay. In addition, using this modeling technique, we were able to provide an estimate of the overall population of juvenile brown shrimp (<100 mm) in shallow water habitats within the bay of approximately 1.3 billion. Furthermore, the geographic range of the model was assessed by plotting observed (actual) versus expected (model) brown shrimp densities in three other Texas bays. Similar habitat-use patterns were observed in all three bays—each having a coefficient of determination >0.50. These results indicate that this model may have a broader geographic application and is a plausible approach in refining current EFH designations for all Gulf of Mexico estuaries with similar geomorphological and hydrological characteristics.

New Zealand chironomids as proxies for human-induced and natural environmental change: Transfer functionsfor temperature and lake production (chlorophyll a)

The analysis of chironomid taxa and environmental datasets from 46 New Zealand lakes identified temperature (February mean air temperature) and lake production (chlorophyll a (Chl a)) as the main drivers of chironomid distribution. Temperature was the strongest driver of chironomid distribution and consequently produced the most robust inference models. We present two possible temperature transfer functions from this dataset. The most robust model (weighted averaging-partial least squares (WA-PLS), n = 36) was based on a dataset with the most productive (Chl a > 10 lg l)1) lakes removed. This model produced a coefficient of determination (r2 jack) of 0.77, and a root mean squared error of prediction (RMSEPjack) of 1.31C. The Chl a transfer function (partial least squares (PLS), n = 37) was far less reliable, with an r2 jack of 0.49 and an RMSEPjack of 0.46 Log10lg l)1. Both of these transfer functions could be improved by a revision of the taxonomy for the New Zealand chironomid taxa, particularly the genus Chironomus. The Chironomus morphotype was common in high altitude, cool, oligotrophic lakes and lowland, warm, eutrophic lakes. This could reflect the widespread distribution of one eurythermic species, or the collective distribution of a number of different Chironomus species with more limited tolerances. The Chl a transfer function could also be improved by inputting mean Chl a values into the inference model rather than the spot measurements that were available for this study.

A thin-layer chromatography-bioautographic method for detecting dipeptidyl peptidase IV inhibitors in plants

A thin-layer chromatography (TLC)-bioautographic method was developed with the aim to detect dipeptidyl peptidase IV (DPP IV) inhibitors from plant extracts. The basic principle of the method is that the enzyme (DPP IV) hydrolyzes substrate (Gly-Pro-p-nitroaniline) into p-nitroaniline (pNA), which diazotizes with sodium nitrite, and then reacts with N-(1-naphthyl) ethylenediamine dihydrochloride in turn to form a rose-red azo dye which provides a rose-red background on the TLC plates. The DPP IV inhibitors showed white spots on the background as they blocked enzymolysis of the substrate to produce pNA. The method was validated with respect to selectivity, sensitivity, linearity, precision, recovery, and stability after optimizing key parameters including plate type, time and temperature of incubation, concentration of substrate, enzyme and derivatization reagents, and absorption wavelength. The results showed good lineary within amounts over 0.01–0.1 μg range for the positive control, diprotin A, with the coefficient of determination (r2) = 0.9668. The limits of detection (LOD) and quantification (LOQ) were 5 and 10 ng, respectively. The recoveries ranged from 98.9% to 107.5%. The averages of the intra- and inter-plate reproducibility were in the range of 4.1–9.7% and 7.6–14.7%, respectively. Among the nine methanolic extracts of medicinal herbs screened for DPP IV inhibitors by the newly developed method, Peganum nigellastrum Bunge was found to have one white active spot, which was then isolated and identified as harmine. By spectrophotometric method, harmine hydrochloride was found to have DPP-IV inhibitory activity of 32.4% at 10 mM comparing to that of 54.8% at 50 μM for diprotin A.

Elaboração e estruturação de um modelo de rating interno

Temos vindo a assistir nos últimos anos a uma evolução no que respeita à avaliação do risco de crédito. As constantes alterações de regulamentação bancária, que resultam dos Acordos de Basileia, têm vindo a impor novas normas que condicionam a quantidade e a qualidade do risco de crédito que as Instituições de Crédito podem assumir nos seus balanços. É de grande importância as Instituições de Crédito avaliarem o risco de crédito, as garantias e o custo de capital, pois têm um impacto direto na sua gestão nomeadamente quanto à afetação de recursos e proteção contra perdas. Desta forma, pretende-se com o presente trabalho elaborar e estruturar um modelo de rating interno através de técnicas estatísticas, assim como identificar as variáveis estatisticamente relevantes no modelo considerado. Foi delineada uma metodologia de investigação mista, considerando na primeira parte do trabalho uma pesquisa qualitativa e na segunda parte uma abordagem quantitativa. Através da análise documental, fez-se uma abordagem dos conceitos teóricos e da regulamentação que serve de base ao presente trabalho. No estudo de caso, o modelo de rating interno foi desenvolvido utilizando a técnica estatística designada de regressão linear múltipla. A amostra considerada foi obtida através da base de dados SABI e é constituída por cem empresas solventes, situadas na zona de Paredes, num horizonte temporal de 2011-2013. A nossa análise baseou-se em três cenários, correspondendo cada cenário aos dados de cada ano (2011, 2012 e 2013). Para validar os pressupostos do modelo foram efetuados testes estatísticos de Durbin Watson e o teste de significância - F (ANOVA). Por fim, para obtermos a classificação de rating de cada variável foi aplicada a técnica dos percentis. Pela análise dos três cenários considerados, verificou-se que o cenário dois foi o que obteve maior coeficiente de determinação. Verificou-se ainda que as variáveis independentes, rácio de liquidez geral, grau de cobertura do ativo total pelo fundo de maneio e rácio de endividamento global são estatisticamente relevantes.

Neuron participation in a synchrony-encoding assembly

Affiliation: Svetlana Shumikhina &Stéphane Molotchnikoff : Département de Sciences Biologiques, Université de Montréal