885 resultados para Multiple-model filter


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Purpose (1) To identify work related stressors that are associated with psychiatric symptoms in a Swiss sample of policemen and (2) to develop a model for identifying officers at risk for developing mental health problems. Method The study design is cross sectional. A total of 354 male police officers answered a questionnaire assessing a wide spectrum of work related stressors. Psychiatric symptoms were assessed using the "TST questionnaire" (Langner in J Health Hum Behav 4, 269-276, 1962). Logistic regression with backward procedure was used to identify a set of variables collectively associated with high scores for psychiatric symptoms. Results A total of 42 (11.9%) officers had a high score for psychiatric symptoms. Nearly all potential stressors considered were significantly associated (at P < 0.05) with a high score for psychiatric symptoms. A significant model including 6 independent variables was identified: lack of support from superior and organization OR = 3.58 (1.58-8.13), self perception of bad quality work OR = 2.99 (1.35-6.59), inadequate work schedule OR = 2.84 (1.22-6.62), high mental/intellectual demand OR = 2.56 (1.12-5.86), age (in decades) OR = 1.82 (1.21-2.73), and score for physical environment complaints OR = 1.30 (1.03-1.64). Conclusions Most of work stressors considered are associated with psychiatric symptoms. Prevention should target the most frequent stressors with high association to symptoms. Complaints of police officers about stressors should receive proper consideration by the management of public administration. Such complaints might be the expression of psychiatric caseness requiring medical assistance. Particular attention should be given to police officers complaining about many stressors identified in this study's multiple model. [Authors]

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ABSTRACT Five experiments were conducted to evaluate the hypothesis that Solanum americanum density and time of coexistence affect the quality of processing tomato fruit. The tomato crop was established using either the direct drilling or the transplanting technique. The factors evaluated consisted of weed density (from 0 up to 6 plants m-2) and time of weed interference (early bloom stage, full flowering stage, fruit filling, and harvest time). The effects of competition on tomato fruit quality were analysed using a multiple model. Tomato variables evaluated included industrial fruit types (which depended on ripeness and disease infection) and soluble solids level(obrix). Tomato fruit quality is dependent on the factors tested. Under low densities (< 6 plants m-2) of S. americanum there was a small impact on the quality of the tomato fruits. The percentage of grade A (mature fruit with red color and without pathogen infection) tomato fruits is the variable most affect by the independent variables. The impact of these independent variables on the percentage of grade C (green and/or with more than 15% disease infection) tomato yield was of smaller magnitude and in an inverse trend as the observed for grade A. The level of soluble solids was influenced by the weed interference on only two experiments, but the impact was of small magnitude. The impact of the results on current and future crop management practices is discussed.

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A case of long-range transport of a biomass burning plume from Alaska to Europe is analyzed using a Lagrangian approach. This plume was sampled several times in the free troposphere over North America, the North Atlantic and Europe by three different aircraft during the IGAC Lagrangian 2K4 experiment which was part of the ICARTT/ITOP measurement intensive in summer 2004. Measurements in the plume showed enhanced values of CO, VOCs and NOy, mainly in form of PAN. Observed O3 levels increased by 17 ppbv over 5 days. A photochemical trajectory model, CiTTyCAT, was used to examine processes responsible for the chemical evolution of the plume. The model was initialized with upwind data and compared with downwind measurements. The influence of high aerosol loading on photolysis rates in the plume was investigated using in situ aerosol measurements in the plume and lidar retrievals of optical depth as input into a photolysis code (Fast-J), run in the model. Significant impacts on photochemistry are found with a decrease of 18% in O3 production and 24% in O3 destruction over 5 days when including aerosols. The plume is found to be chemically active with large O3 increases attributed primarily to PAN decomposition during descent of the plume toward Europe. The predicted O3 changes are very dependent on temperature changes during transport and also on water vapor levels in the lower troposphere which can lead to O3 destruction. Simulation of mixing/dilution was necessary to reproduce observed pollutant levels in the plume. Mixing was simulated using background concentrations from measurements in air masses in close proximity to the plume, and mixing timescales (averaging 6.25 days) were derived from CO changes. Observed and simulated O3/CO correlations in the plume were also compared in order to evaluate the photochemistry in the model. Observed slopes change from negative to positive over 5 days. This change, which can be attributed largely to photochemistry, is well reproduced by multiple model runs even if slope values are slightly underestimated suggesting a small underestimation in modeled photochemical O3 production. The possible impact of this biomass burning plume on O3 levels in the European boundary layer was also examined by running the model for a further 5 days and comparing with data collected at surface sites, such as Jungfraujoch, which showed small O3 increases and elevated CO levels. The model predicts significant changes in O3 over the entire 10 day period due to photochemistry but the signal is largely lost because of the effects of dilution. However, measurements in several other BB plumes over Europe show that O3 impact of Alaskan fires can be potentially significant over Europe.

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This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

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The presented work deals with the calibration of a 2D numerical model for the simulation of long term bed load transport. A settled basin along an alpine stream was used as a case study. The focus is to parameterise the used multi fractional transport model such that a dynamically balanced behavior regarding erosion and deposition is reached. The used 2D hydrodynamic model utilizes a multi-fraction multi-layer approach to simulate morphological changes and bed load transport. The mass balancing is performed between three layers: a top mixing layer, an intermediate subsurface layer and a bottom layer. Using this approach bears computational limitations in calibration. Due to the high computational demands, the type of calibration strategy is not only crucial for the result, but as well for the time required for calibration. Brute force methods such as Monte Carlo type methods may require a too large number of model runs. All here tested calibration strategies used multiple model runs utilising the parameterization and/or results from previous run. One concept was to reset to initial bed elevations after each run, allowing the resorting process to convert to stable conditions. As an alternative or in combination, the roughness was adapted, based on resulting nodal grading curves, from the previous run. Since the adaptations are a spatial process, the whole model domain is subdivided in homogeneous sections regarding hydraulics and morphological behaviour. For a faster optimization, the adaptation of the parameters is made section wise. Additionally, a systematic variation was done, considering results from previous runs and the interaction between sections. The used approach can be considered as similar to evolutionary type calibration approaches, but using analytical links instead of random parameter changes.

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In this work a modification on ANFIS (Adaptive Network Based Fuzzy Inference System) structure is proposed to find a systematic method for nonlinear plants, with large operational range, identification and control, using linear local systems: models and controllers. This method is based on multiple model approach. This way, linear local models are obtained and then those models are combined by the proposed neurofuzzy structure. A metric that allows a satisfactory combination of those models is obtained after the structure training. It results on plant s global identification. A controller is projected for each local model. The global control is obtained by mixing local controllers signals. This is done by the modified ANFIS. The modification on ANFIS architecture allows the two neurofuzzy structures knowledge sharing. So the same metric obtained to combine models can be used to combine controllers. Two cases study are used to validate the new ANFIS structure. The knowledge sharing is evaluated in the second case study. It shows that just one modified ANFIS structure is necessary to combine linear models to identify, a nonlinear plant, and combine linear controllers to control this plant. The proposed method allows the usage of any identification and control techniques for local models and local controllers obtaining. It also reduces the complexity of ANFIS usage for identification and control. This work has prioritized simpler techniques for the identification and control systems to simplify the use of the method

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Background: The pathogenesis of pulmonary hypertension (PH) in hemodialysis is still unclear. The aim of thisstudy was to identify the risk factors associated with the presence of PH in chronic hemodialysis patients and toverify whether these factors might explain the highest mortality among them.Methods: We conducted a retrospective study of hemodialysis patients who started treatment from August 2001to October 2007 and were followed up until April 2011 in a Brazilian referral medical school. According to theresults of echocardiography examination, patients were allocated in two groups: those with PH and those withoutPH. Clinical parameters, site and type of vascular access, bioimpedance, and laboratorial findings were comparedbetween the groups and a logistic regression model was elaborated. Actuarial survival curves were constructed andhazard risk to death was evaluated by Cox regression analysis.Results: PH > 35 mmHg was found in 23 (30.6%) of the 75 patients studied. The groups differed in extracellularwater, ventricular thickness, left atrium diameter, and ventricular filling. In a univariate analysis, extracellular waterwas associated with PH (relative risk = 1.194; 95% CI of 1.006 1.416; p = 0.042); nevertheless, in a multiple model,only left atrium enlargement was independently associated with PH (relative risk =1.172; 95% CI of 1.010 1.359;p = 0.036). PH (hazard risk = 3.008; 95% CI of 1.285 7.043; p = 0.011) and age (hazard risk of 1.034 per year of age;95% CI of 1.000 7.068; p = 0.047) were significantly associated with mortality in a multiple Cox regression analysis.However, when albumin was taken in account the only statistically significant association was between albuminlevel and mortality (hazard risk = 0.342 per g/dL; 95% CI of 0.119 0.984; p = 0.047) while the presence of PH lost itsstatistical significance (p = 0.184). Mortality was higher in patients with PH (47.8% vs 25%) who also had astatistically worse survival after the sixth year of follow up.Conclusions: PH in hemodialysis patients is associated with parameters of volume overload that sheds light on itspathophysiology. Mortality is higher in hemodialysis patients with PH and the low albumin level can explain thisassociation.© 2012 Greenfield et al.; licensee BioMed Central Ltd.

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Pós-graduação em Fisiopatologia em Clínica Médica - FMB

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Objective: This study assessed the relation of child oral health-related quality of life with school performance and school absenteeism. Methods: We followed a cross-sectional design with a multistage random sample of 312 12-year-old schoolchildren living in Brazil. The participants completed the child perceptions questionnaire (CPQ1114) that provides information about psychological factors, while their parents or guardians answered questions on their socioeconomic status measured by parents' education level and household income. A dental examination of each child provided information on the prevalence of caries and dental trauma. Data on school performance, which included the results of baseline Brazilian language (Portuguese) tests, and school absenteeism (school days missed) were obtained from the school register. Multilevel linear regression was used to investigate the association among psychological and socioeconomic status and children's school performance. Results: In the multiple model, after adjusting for individual covariates, being a girl was associated with higher school performance (P < 0.05), whereas low household income (P < 0.05), higher mean of CPQ1114 (P < 0.05), and higher school days missed (P < 0.001) were identified as individual determinants of lower school performance. When the school-level covariates were included in the model, the association between subjects' level characteristics and school performance still persisted. Conclusion: Children's school performance and absence were influenced by psychological and socioeconomic conditions.

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This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets.

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Background: Identifying local similarity between two or more sequences, or identifying repeats occurring at least twice in a sequence, is an essential part in the analysis of biological sequences and of their phylogenetic relationship. Finding such fragments while allowing for a certain number of insertions, deletions, and substitutions, is however known to be a computationally expensive task, and consequently exact methods can usually not be applied in practice. Results: The filter TUIUIU that we introduce in this paper provides a possible solution to this problem. It can be used as a preprocessing step to any multiple alignment or repeats inference method, eliminating a possibly large fraction of the input that is guaranteed not to contain any approximate repeat. It consists in the verification of several strong necessary conditions that can be checked in a fast way. We implemented three versions of the filter. The first is simply a straightforward extension to the case of multiple sequences of an application of conditions already existing in the literature. The second uses a stronger condition which, as our results show, enable to filter sensibly more with negligible (if any) additional time. The third version uses an additional condition and pushes the sensibility of the filter even further with a non negligible additional time in many circumstances; our experiments show that it is particularly useful with large error rates. The latter version was applied as a preprocessing of a multiple alignment tool, obtaining an overall time (filter plus alignment) on average 63 and at best 530 times smaller than before (direct alignment), with in most cases a better quality alignment. Conclusion: To the best of our knowledge, TUIUIU is the first filter designed for multiple repeats and for dealing with error rates greater than 10% of the repeats length.

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In this paper the continuous Verhulst dynamic model is used to synthesize a new distributed power control algorithm (DPCA) for use in direct sequence code division multiple access (DS-CDMA) systems. The Verhulst model was initially designed to describe the population growth of biological species under food and physical space restrictions. The discretization of the corresponding differential equation is accomplished via the Euler numeric integration (ENI) method. Analytical convergence conditions for the proposed DPCA are also established. Several properties of the proposed recursive algorithm, such as Euclidean distance from optimum vector after convergence, convergence speed, normalized mean squared error (NSE), average power consumption per user, performance under dynamics channels, and implementation complexity aspects, are analyzed through simulations. The simulation results are compared with two other DPCAs: the classic algorithm derived by Foschini and Miljanic and the sigmoidal of Uykan and Koivo. Under estimated errors conditions, the proposed DPCA exhibits smaller discrepancy from the optimum power vector solution and better convergence (under fixed and adaptive convergence factor) than the classic and sigmoidal DPCAs. (C) 2010 Elsevier GmbH. All rights reserved.

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Multiple sampling is widely used in vadose zone percolation experiments to investigate the extent in which soil structure heterogeneities influence the spatial and temporal distributions of water and solutes. In this note, a simple, robust, mathematical model, based on the beta-statistical distribution, is proposed as a method of quantifying the magnitude of heterogeneity in such experiments. The model relies on fitting two parameters, alpha and zeta to the cumulative elution curves generated in multiple-sample percolation experiments. The model does not require knowledge of the soil structure. A homogeneous or uniform distribution of a solute and/or soil-water is indicated by alpha = zeta = 1, Using these parameters, a heterogeneity index (HI) is defined as root 3 times the ratio of the standard deviation and mean. Uniform or homogeneous flow of water or solutes is indicated by HI = 1 and heterogeneity is indicated by HI > 1. A large value for this index may indicate preferential flow. The heterogeneity index relies only on knowledge of the elution curves generated from multiple sample percolation experiments and is, therefore, easily calculated. The index may also be used to describe and compare the differences in solute and soil-water percolation from different experiments. The use of this index is discussed for several different leaching experiments. (C) 1999 Elsevier Science B.V. All rights reserved.

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We describe a method for multiple indicator dilution studies in the isolated perfused human placental lobule developed to investigate the relationships between changes in pressure and flow and solute clearance. A peripheral lobule of a human placenta is perfused with a tissue culture-based medium and the perfusate oxygen tension, arterial and venous pressures, pH and perfusion temperature continuously monitored by a computerized system. Flow rates are readily changed. Bolus injections of vascular, extracellular and water space markers, and study compounds can be made into either maternal or fetal circulations, and precisely timed outflow fractions can be collected with computer-controlled fraction collectors, allowing simultaneous determination of concentration-time profiles of each marker. (C) 1997 Elsevier Science Inc.