914 resultados para Combination of short term inflation forecast models
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OBJECTIVE High altitude-related hypoxia induces pulmonary vasoconstriction. In Fontan patients without a contractile subpulmonary ventricle, an increase in pulmonary artery pressure is expected to decrease circulatory output and reduce exercise capacity. This study investigates the direct effects of short-term high altitude exposure on pulmonary blood flow (PBF) and exercise capacity in Fontan patients. METHODS 16 adult Fontan patients (mean age 28±7 years, 56% female) and 14 matched controls underwent cardiopulmonary exercise testing with measurement of PBF with a gas rebreathing system at 540 m (low altitude) and at 3454 m (high altitude) within 12 weeks. RESULTS PBF at rest and at exercise was higher in controls than in Fontan patients, both at low and high altitude. PBF increased twofold in Fontan patients and 2.8-fold in the control group during submaximal exercise, with no significant difference between low and high altitude (p=0.290). A reduction in peak oxygen uptake at high compared with low altitude was observed in Fontan patients (22.8±5.1 and 20.5±3.8 mL/min/kg, p<0.001) and the control group (35.0±7.4 and 29.1±6.5 mL/min/kg, p<0.001). The reduction in exercise capacity was less pronounced in Fontan patients compared with controls (9±12% vs 17±8%, p=0.005). No major adverse clinical event was observed. CONCLUSIONS Short-term high altitude exposure has no negative impact on PBF and exercise capacity in Fontan patients when compared with controls, and was clinically well tolerated. TRIAL REGISTRATION NUMBER NCT02237274: Results.
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The mammalian binaural cue of interaural time difference (ITD) and cross-correlation have long been used to determine the point of origin of a sound source. The ITD can be defined as the different points in time at which a sound from a single location arrives at each individual ear [1]. From this time difference, the brain can calculate the angle of the sound source in relation to the head [2]. Cross-correlation compares the similarity of each channel of a binaural waveform producing the time lag or offset required for both channels to be in phase with one another. This offset corresponds to the maximum value produced by the cross-correlation function and can be used to determine the ITD and thus the azimuthal angle θ of the original sound source. However, in indoor environments, cross-correlation has been known to have problems with both sound reflections and reverberations. Additionally, cross-correlation has difficulties with localising short-term complex noises when they occur during a longer duration waveform, i.e. in the presence of background noise. The crosscorrelation algorithm processes the entire waveform and the short-term complex noise can be ignored. This paper presents a technique using thresholding which enables higher-localisation abilities for short-term complex sounds in the midst of background noise. To determine the success of this thresholding technique, twenty-five sounds were recorded in a dynamic and echoic environment. The twenty-five sounds consist of hand-claps, finger-clicks and speech. The proposed technique was compared to the regular cross-correlation function for the same waveforms, and an average of the azimuthal angles determined for each individual sample. The sound localisation ability for all twenty-five sound samples is as follows: average of the sampled angles using cross-correlation: 44%; cross-correlation technique with thresholding: 84%. From these results, it is clear that this proposed technique is very successful for the localisation of short-term complex sounds in the midst of background noise and in a dynamic and echoic indoor environment.
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An important assumption in the statistical analysis of the financial market effects of the central bank’s large scale asset purchase program is that the "long-term debt stock variables were exogenous to term premia". We test this assumption for a small open economy in a currency union over the period 2000M3 to 2015M10, via the determinants of short- term financing relative to long-term financing. Empirical estimations indicate that the maturity composition of debt does not respond to the level of interest rate or to the term structure. These findings suggest a lower adherence to the cost minimization mandate of debt management. However, we find that volatility and relative market size respectively decrease and increase short-term financing relative to long-term financing, while it decreases with an increase in government indebtedness.
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Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants
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Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants
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Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short-term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short-term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.
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Ground-source heat pump (GSHP) systems represent one of the most promising techniques for heating and cooling in buildings. These systems use the ground as a heat source/sink, allowing a better efficiency thanks to the low variations of the ground temperature along the seasons. The ground-source heat exchanger (GSHE) then becomes a key component for optimizing the overall performance of the system. Moreover, the short-term response related to the dynamic behaviour of the GSHE is a crucial aspect, especially from a regulation criteria perspective in on/off controlled GSHP systems. In this context, a novel numerical GSHE model has been developed at the Instituto de Ingeniería Energética, Universitat Politècnica de València. Based on the decoupling of the short-term and the long-term response of the GSHE, the novel model allows the use of faster and more precise models on both sides. In particular, the short-term model considered is the B2G model, developed and validated in previous research works conducted at the Instituto de Ingeniería Energética. For the long-term, the g-function model was selected, since it is a previously validated and widely used model, and presents some interesting features that are useful for its combination with the B2G model. The aim of the present paper is to describe the procedure of combining these two models in order to obtain a unique complete GSHE model for both short- and long-term simulation. The resulting model is then validated against experimental data from a real GSHP installation.
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Background: The dust mite Blomia tropicalis is an important source of aeroallergens in tropical areas. Although a mouse model for B. tropicalis extract (BtE)-induced asthma has been described, no study comparing different mouse strains in this asthma model has been reported. The relevance and reproducibility of experimental animal models of allergy depends on the genetic background of the animal, the molecular composition of the allergen and the experimental protocol. Objectives: This work had two objectives. The first was to study the anti-B. tropicalis allergic responses in different mouse strains using a short-term model of respiratory allergy to BtE. This study included the comparison of the allergic responses elicited by BtE with those elicited by ovalbumin in mice of the strain that responded better to BtE sensitization. The second objective was to investigate whether the best responder mouse strain could be used in an experimental model of allergy employing relatively low BtE doses. Methods: Groups of mice of four different syngeneic strains were sensitized subcutaneously with 100 mu g of BtE on days 0 and 7 and challenged four times intranasally, at days 8, 10, 12, and 14, with 10 mu g of BtE. A/J mice, that were the best responders to BtE sensitization, were used to compare the B. tropicalis-specific asthma experimental model with the conventional experimental model of ovalbumin (OVA)-specific asthma. A/J mice were also sensitized with a lower dose of BtE. Results: Mice of all strains had lung inflammatory-cell infiltration and increased levels of anti-BtE IgE antibodies, but these responses were significantly more intense in A/J mice than in CBA/J, BALB/c or C57BL/6J mice. Immunization of A/J mice with BtE induced a more intense airway eosinophil influx, higher levels of total IgE, similar airway hyperreactivity to methacholine but less intense mucous production, and lower levels of specific IgE, IgG1 and IgG2 antibodies than sensitization with OVA. Finally, immunization with a relatively low BtE dose (10 mu g per subcutaneous injection per mouse) was able to sensitize A/J mice, which were the best responders to high-dose BtE immunization, for the development of allergy-associated immune and lung inflammatory responses. Conclusions: The described short-term model of BtE-induced allergic lung disease is reproducible in different syngeneic mouse strains, and mice of the A/J strain was the most responsive to it. In addition, it was shown that OVA and BtE induce quantitatively different immune responses in A/J mice and that the experimental model can be set up with low amounts of BtE.
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This paper presents results of laboratory testing of unrestrained drying shrinkage during a period of 154 days of different concrete mixtures from the Brazilian production line that utilize ground granulated blast-furnace slag in their compositions. Three concrete mixtures with water/cement ratio of 0.78(M1), 0.41(M2), and 0.37(M3) were studied. The obtained experimental data were compared with the analytical results from prediction models available in the literature: the ACI 209 model (ACI), the B3 model (B3), the Eurocode 2 model (EC2), the GL 2000 model (GL), and the Brazilian NBR 6118 model (NBR), and an analysis of the efficacy of these models was conducted utilizing these experimental data. In addition, the development of the mechanical properties (compressive strength and modulus of elasticity) of the studied concrete mixtures was also measured in the laboratory until 126 days. From this study, it could be concluded that the ACI and the GL were the models that most approximated the experimental drying shrinkage data measured during the analyzed period of time.
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Background and Purpose-This report describes trends in the key indices of cerebrovascular disease over 6 years from the end of the 1980s in a geographically defined segment of the city of Perth, Western Australia. Methods-Identical methods were used to find and assess all cases of suspected stroke in a population of approximately 134 000 residents in a triangular area of the northern suburbs of Perth. Case fatality was measured as vital status at 28 days after the onset of symptoms. Data for first-ever strokes and for all strokes for equivalent periods of 12 months in 1989-1990 and 1995-1996 were compared by age-standardized rates and proportions and Poisson regression. Results-There were 355 strokes in 328 patients and 251 first-ever strokes (71%) for 1989-1990 and 290 events in 281 patients and 213 first-ever strokes (73%) for 1995-1996. In Poisson models including age and period, overall trends in the incidence of both first-ever strokes (rate ratio = 0.75; 95% confidence limits, 0.63, 0.90) and all strokes (rate ratio = 0.73; 95% confidence limits, 0.62, 0.85) were obviously significant, but only the changes in men were independently significant. Case fatality did not change, and the balance between hemorrhagic and occlusive strokes in 1995-1996 was almost indistinguishable from that observed in 1989-1990. Conclusions-Our results, which are the only longitudinal population-based data available for Australia for key indices of stroke, suggest that it is a change in the frequency of stroke, rather than its outcome, that is chiefly responsible nationally for the fall in mortality from cerebrovascular disease.
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In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.
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The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.