924 resultados para Model II regression


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A major obstacle to processing images of the ocean floor comes from the absorption and scattering effects of the light in the aquatic environment. Due to the absorption of the natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion, and, as the vehicle moves, induce shadows in the scene. For this reason, the first step towards application of standard computer vision techniques to underwater imaging requires dealing first with these lighting problems. This paper analyses and compares existing methodologies to deal with low-contrast, nonuniform illumination in underwater image sequences. The reviewed techniques include: (i) study of the illumination-reflectance model, (ii) local histogram equalization, (iii) homomorphic filtering, and, (iv) subtraction of the illumination field. Several experiments on real data have been conducted to compare the different approaches

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An understanding of the role of organic nitrogen (N) pools in the N supply of eucalyptus plantations is essential for the development of strategies that maximize the efficient use of N for this crop. This study aimed to evaluate the distribution of organic N pools in different compartments of the soil-plant system and their contributions to the N supply in eucalyptus plantations at different ages (1, 3, 5, and 13 years). Three models were used to estimate the contributions of organic pools: Model I considered N pools contained in the litterfall, N pools in the soil microbial biomass and available soil N (mineral N); Model II considered the N pools in the soil, potentially mineralizable N and the export of N through wood harvesting; and Model III (N balance) was defined as the difference between the initial soil N pool (0-10 cm) and the export of N, taking the application of N fertilizer into account. Model I showed that N pools could supply 27 - 70 % of the N demands of eucalyptus trees at different ages. Model II suggested that the soil N pool may be sufficient for 4 - 5 rotations of 5 years. According to the N balance, these N pools would be sufficient to meet the N demands of eucalyptus for more than 15 rotations of 5 years. The organic pools contribute with different levels of N and together are sufficient to meet the N demands of eucalyptus for several rotations.

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Delta(9)-Tetrahydrocannabinol (THC) is frequently found in the blood of drivers suspected of driving under the influence of cannabis or involved in traffic crashes. The present study used a double-blind crossover design to compare the effects of medium (16.5 mg THC) and high doses (45.7 mg THC) of hemp milk decoctions or of a medium dose of dronabinol (20 mg synthetic THC, Marinol on several skills required for safe driving. Forensic interpretation of cannabinoids blood concentrations were attempted using the models proposed by Daldrup (cannabis influencing factor or CIF) and Huestis and coworkers. First, the time concentration-profiles of THC, 11-hydroxy-Delta(9)-tetrahydrocannabinol (11-OH-THC) (active metabolite of THC), and 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol (THCCOOH) in whole blood were determined by gas chromatography-mass spectrometry-negative ion chemical ionization. Compared to smoking studies, relatively low concentrations were measured in blood. The highest mean THC concentration (8.4 ng/mL) was achieved 1 h after ingestion of the strongest decoction. Mean maximum 11-OH-THC level (12.3 ng/mL) slightly exceeded that of THC. THCCOOH reached its highest mean concentration (66.2 ng/mL) 2.5-5.5 h after intake. Individual blood levels showed considerable intersubject variability. The willingness to drive was influenced by the importance of the requested task. Under significant cannabinoids influence, the participants refused to drive when they were asked whether they would agree to accomplish several unimportant tasks, (e.g., driving a friend to a party). Most of the participants reported a significant feeling of intoxication and did not appreciate the effects, notably those felt after drinking the strongest decoction. Road sign and tracking testing revealed obvious and statistically significant differences between placebo and treatments. A marked impairment was detected after ingestion of the strongest decoction. A CIF value, which relies on the molar ratio of main active to inactive cannabinoids, greater than 10 was found to correlate with a strong feeling of intoxication. It also matched with a significant decrease in the willingness to drive, and it matched also with a significant impairment in tracking performances. The mathematic model II proposed by Huestis et al. (1992) provided at best a rough estimate of the time of oral administration with 27% of actual values being out of range of the 95% confidence interval. The sum of THC and 11-OH-THC blood concentrations provided a better estimate of impairment than THC alone. This controlled clinical study points out the negative influence on fitness to drive after medium or high dose oral THC or dronabinol.

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Sickness absence (SA) is an important social, economic and public health issue. Identifying and understanding the determinants, whether biological, regulatory or, health services-related, of variability in SA duration is essential for better management of SA. The conditional frailty model (CFM) is useful when repeated SA events occur within the same individual, as it allows simultaneous analysis of event dependence and heterogeneity due to unknown, unmeasured, or unmeasurable factors. However, its use may encounter computational limitations when applied to very large data sets, as may frequently occur in the analysis of SA duration. To overcome the computational issue, we propose a Poisson-based conditional frailty model (CFPM) for repeated SA events that accounts for both event dependence and heterogeneity. To demonstrate the usefulness of the model proposed in the SA duration context, we used data from all non-work-related SA episodes that occurred in Catalonia (Spain) in 2007, initiated by either a diagnosis of neoplasm or mental and behavioral disorders. As expected, the CFPM results were very similar to those of the CFM for both diagnosis groups. The CPU time for the CFPM was substantially shorter than the CFM. The CFPM is an suitable alternative to the CFM in survival analysis with recurrent events,especially with large databases.

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We propose a novel multifactor dimensionality reduction method for epistasis detection in small or extended pedigrees, FAM-MDR. It combines features of the Genome-wide Rapid Association using Mixed Model And Regression approach (GRAMMAR) with Model-Based MDR (MB-MDR). We focus on continuous traits, although the method is general and can be used for outcomes of any type, including binary and censored traits. When comparing FAM-MDR with Pedigree-based Generalized MDR (PGMDR), which is a generalization of Multifactor Dimensionality Reduction (MDR) to continuous traits and related individuals, FAM-MDR was found to outperform PGMDR in terms of power, in most of the considered simulated scenarios. Additional simulations revealed that PGMDR does not appropriately deal with multiple testing and consequently gives rise to overly optimistic results. FAM-MDR adequately deals with multiple testing in epistasis screens and is in contrast rather conservative, by construction. Furthermore, simulations show that correcting for lower order (main) effects is of utmost importance when claiming epistasis. As Type 2 Diabetes Mellitus (T2DM) is a complex phenotype likely influenced by gene-gene interactions, we applied FAM-MDR to examine data on glucose area-under-the-curve (GAUC), an endophenotype of T2DM for which multiple independent genetic associations have been observed, in the Amish Family Diabetes Study (AFDS). This application reveals that FAM-MDR makes more efficient use of the available data than PGMDR and can deal with multi-generational pedigrees more easily. In conclusion, we have validated FAM-MDR and compared it to PGMDR, the current state-of-the-art MDR method for family data, using both simulations and a practical dataset. FAM-MDR is found to outperform PGMDR in that it handles the multiple testing issue more correctly, has increased power, and efficiently uses all available information.

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Diplomityön tavoitteena oli laatia putkivirtaukselle laskentamalli, joka huomioi turbulenssin vaimentamiseen käytetyn lisäaineen vaikutuksen. Kirjallisuusosassa käsitellään turbulenssiin ja putkiston painehäviön laskentaan liittyviä asioita. Lisäksi käydään läpi virtausvastusta alentavien aineiden (DRA-aineiden) ominaisuuksia, vaikutusmekanismia ja niiden vaikutusta virtausominaisuuksiin sekä laskentamalleihin. Kokeellisessa osassa laadittiin kaksi Excel-pohjaista laskentamallia, joilla huomioitiin lisäaineen vaikutus putkiston virtausmäärien parantumiseen ja painehäviöihin. Malli I laskee ensin virtausvastuksen alenemisen (DR-efektin) annetuista lähtötiedoista. DR-efektien perusteella saadaan laskettua putkiston painehäviötiedot. Malli II laskee ensin virtausvastuskertoimet ilman DRA-ainetta ja DRA-aineen kanssa. Tämän jälkeen malli II laskee DR-efektin. Mallien vaatimat lähtötiedot koostuvat virtausmääristä, fluidin ominaisuuksista, polymeerin ominaisuuksista, lisäaineen annostelutiedoista ja putkiston tiedoista.

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Mergers and acquisitions (M&A) have played very important role in restructuring the pulp and paper industry (PPI). The poor performance and fragmented nature of the industry, overcapacity problems, and globalisation have driven companies to consolidate. The objective of this thesis was to examine how PPI acquirers’ have performed subsequent M&As and whether the deal characteristics have had any impact on performance. Based on the results it seems that PPI companies have not been able to enhance their performance in the long run after M&As although the per-formance of acquiring firms has remained above the industry median, and deal characteristics or the amount of premiums paid do not seem to have had any effect. The statistical significance of the results was tested with change model and regression analysis. Performance was assessed with accrual, cash flow, and market based indicators. Results are congruent with behavioural theory: managers and investors seem to be overoptimistic in determining the synergies from M&As.

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This work evaluated eight hypsometric models to represent tree height-diameter relationship, using data obtained from the scaling of 118 trees and 25 inventory plots. Residue graphic analysis and percent deviation mean criteria, qui-square test precision, residual standard error between real and estimated heights and the graybill f test were adopted. The identity of the hypsometric models was also verified by applying the F(Ho) test on the plot data grouped to the scaling data. It was concluded that better accuracy can be obtained by using the model prodan, with h and d1,3 data measured in 10 trees by plots grouped into these scaling data measurements of even-aged forest stands.

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Experiential marketing is increasingly seen as a new magical key to consumers’ hearts. Brands are turning brick-and-mortar stores into state of the art retail spaces where memorable experiences and strong brand relationships are hoped to be born. Around the globe, several brands have opened up a special format of stores – the experience store. Although many speculations on the positive effects of experiences have been presented, few studies have provided empirical, quantified evidence for the link between store experiences and brand success. In consequence, research was needed to find out whether experience stores truly are so special. The purpose of this thesis was to investigate whether store experiences are capable of building brands and influencing store performance. For this purpose, empirical research was conducted in the Samsung Experience Store Helsinki. As main constructs of the study, store experience, brand equity, store performance, and product class involvement were measured, along with relevant background variables. Data was collected with an electronic survey from actual customers of the store, resulting in a sample of 131 respondents. Partial least squares structural equations modeling (PLS) was used for the analysis of the research model. Also, regression analysis was conducted to account for mediation and moderation effects. The results showed that store experiences do positively influence first, store performance, and second, separate dimensions of brand equity (that is, brand awareness, brand personality, and brand loyalty). Also, the effect of store experiences on store performance was found to be mediated by brand equity. Interestingly, customers’ product class involvement was detected to moderate the effect of store experience on store performance. That is, those who were highly involved with electronics had greater store experiences, and also displayed a stronger linkage between store experience and store performance. The results encourage marketers to continue with efforts to create great experiences for their customers. Experience stores can – and should be seen – as both powerful brand building tools and profitable sales channels. The creation of exceptional experiences can act as an important function of physical stores in the face of severe online competition.

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The objective of the present study was to estimate the contribution of environmental pollutants to hospital admissions for cardiovascular disease. A time series ecological study was conducted on subjects aged over 60 years and living in São José dos Campos, Brazil, with a population near 700,000 inhabitants. Hospital admission data of public health patients (SUS) were obtained from DATASUS for the period between January 1, 2004 and December 31, 2006, according to the ICD-10 diagnoses I20 to I22 and I24. Particulate matter with less than 10 µm in aerodynamic diameter, sulfur dioxide and ozone were the pollutants examined, and the control variables were mean temperature and relative humidity. Data on pollutants were obtained from the São Paulo State Sanitary Agency. The generalized linear model Poisson regression with lags of up to 5 days was used. There were 1303 hospital admissions during the period. Exposure to particulate matter was significantly associated with hospitalization for cardiovascular disease 3 days after exposure (RR = 1.006; 95%CI = 1.000 to 1.010) and an increase of 16 µg/m³ was associated with a 10% increase in risk of hospitalization; other pollutants were not associated with hospitalization. Thus, it was possible to identify the role of exposure to particulate matter as an environmental pollutant in hospitalization for cardiovascular disease in a medium-sized city inSoutheastern Brazil.

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Although the link between macroeconomic news announcements and exchange rates is well documented in recent literature, this connection may be unstable. By using a broad set of macroeconomic news announcements and high frequency forex data for the Euro/Dollar, Pound/Dollar and Yen/Dollar from Nov 1, 2004 to Mar 31, 2014, we obtain two major findings with regards to this instability. First, many macroeconomic news announcements exhibit unstable effects with certain patterns in foreign exchange rates. These news effects may change in magnitude and even in their sign over time, over business cycles and crises within distinctive contexts. This finding is robust because the results are obtained by applying a Two-Regime Smooth Transition Regression Model, a Breakpoints Regression Model, and an Efficient Test of Parameter Instability which are all consistent with each other. Second, when we explore the source of this instability, we find that global risks and the reaction by central bank monetary policy to these risks to be possible factors causing this instability.

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In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

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Les fichiers video (d'animation) sont dans un format Windows Media (.wmv)

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Chaque jour, des décisions doivent être prises quant à la quantité d'hydroélectricité produite au Québec. Ces décisions reposent sur la prévision des apports en eau dans les bassins versants produite à l'aide de modèles hydrologiques. Ces modèles prennent en compte plusieurs facteurs, dont notamment la présence ou l'absence de neige au sol. Cette information est primordiale durant la fonte printanière pour anticiper les apports à venir, puisqu'entre 30 et 40% du volume de crue peut provenir de la fonte du couvert nival. Il est donc nécessaire pour les prévisionnistes de pouvoir suivre l'évolution du couvert de neige de façon quotidienne afin d'ajuster leurs prévisions selon le phénomène de fonte. Des méthodes pour cartographier la neige au sol sont actuellement utilisées à l'Institut de recherche d'Hydro-Québec (IREQ), mais elles présentent quelques lacunes. Ce mémoire a pour objectif d'utiliser des données de télédétection en micro-ondes passives (le gradient de températures de brillance en position verticale (GTV)) à l'aide d'une approche statistique afin de produire des cartes neige/non-neige et d'en quantifier l'incertitude de classification. Pour ce faire, le GTV a été utilisé afin de calculer une probabilité de neige quotidienne via les mélanges de lois normales selon la statistique bayésienne. Par la suite, ces probabilités ont été modélisées à l'aide de la régression linéaire sur les logits et des cartographies du couvert nival ont été produites. Les résultats des modèles ont été validés qualitativement et quantitativement, puis leur intégration à Hydro-Québec a été discutée.

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The thesis deals with analysis of some Stochastic Inventory Models with Pooling/Retrial of Customers.. In the first model we analyze an (s,S) production Inventory system with retrial of customers. Arrival of customers from outside the system form a Poisson process. The inter production times are exponentially distributed with parameter µ. When inventory level reaches zero further arriving demands are sent to the orbit which has capacity M(<∞). Customers, who find the orbit full and inventory level at zero are lost to the system. Demands arising from the orbital customers are exponentially distributed with parameter γ. In the model-II we extend these results to perishable inventory system assuming that the life-time of each item follows exponential with parameter θ. The study deals with an (s,S) production inventory with service times and retrial of unsatisfied customers. Primary demands occur according to a Markovian Arrival Process(MAP). Consider an (s,S)-retrial inventory with service time in which primary demands occur according to a Batch Markovian Arrival Process (BMAP). The inventory is controlled by the (s,S) policy and (s,S) inventory system with service time. Primary demands occur according to Poissson process with parameter λ. The study concentrates two models. In the first model we analyze an (s,S) Inventory system with postponed demands where arrivals of demands form a Poisson process. In the second model, we extend our results to perishable inventory system assuming that the life-time of each item follows exponential distribution with parameter θ. Also it is assumed that when inventory level is zero the arriving demands choose to enter the pool with probability β and with complementary probability (1- β) it is lost for ever. Finally it analyze an (s,S) production inventory system with switching time. A lot of work is reported under the assumption that the switching time is negligible but this is not the case for several real life situation.