954 resultados para Forecast errors


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Abstract In a few rare diseases, specialised studies in cerebrospinal fluid (CSF) are required to identify the underlying metabolic disorder. We aimed to explore the possibility of detecting key synaptic proteins in the CSF, in particular dopaminergic and gabaergic, as new procedures that could be useful for both pathophysiological and diagnostic purposes in investigation of inherited disorders of neurotransmission. Dopamine receptor type 2 (D2R), dopamine transporter (DAT) and vesicular monoamine transporter type 2 (VMAT2) were analysed in CSF samplesfrom 30 healthy controls (11 days to 17 years) by western blot analysis. Because VMAT2 was the only protein with intracellular localisation, and in order to compare results, GABA vesicular transporter, which is another intracellular protein, was also studied. Spearman’s correlation and Student’s t tests were applied to compare optical density signals between different proteins. All these synaptic proteins could be easily detected and quantified in the CSF. DAT, D2R and GABA VT expression decrease with age, particularly in the first months of life, reflecting the expected intense synaptic activity and neuronal circuitry formation. A statistically significant relationship was found between D2R and DAT expression, reinforcing the previous evidence of DAT regulation by D2R. To our knowledge, there are no previous studies on human CSF reporting a reliable analysis of these proteins. These kinds of studies could help elucidate new causes of disturbed dopaminergic and gabaergic transmission as well as understanding different responses to L-dopa in inherited disorders affecting dopamine metabolism. Moreover, this approach to synaptic activity in vivo can be extended to different groups of proteins and diseases.

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A PhD Dissertation, presented as part of the requirements for the Degree of Doctor of Philosophy from the NOVA - School of Business and Economics

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This work presents the archaeometallurgical study of a group of metallic artefacts found in Moinhos de Golas site, Vila Real (North of Portugal), that can generically be attributed to Proto-history (1st millennium BC, Late Bronze Age and Iron Age). The collection is composed by 35 objects: weapons, ornaments and tools, and others of difficult classification, as rings, bars and one small thin bent sheet. Some of the objects can typologically be attributed to Late Bronze Age, others are of more difficult specific attribution. The archaeometallurgical study involved x-ray digital radiography, elemental analysis by micro-energy dispersive X-ray fluorescence spectrometry and scanning electron microscopy with energy dispersive spectroscopy, microstructural observations by optical microscopy and scanning electron microscopy. The radiographic images revealed structural heterogeneities frequently related with the degradation of some artefacts and the elemental analysis showed that the majority of the artefacts was produced in a binary bronze alloy (Cu-Sn) (73%), being others produced in copper (15%) and three artefacts in brass (Cu-Zn(-Sn-Pb)). Among each type of alloy there’s certain variability in the composition and in the type of inclusions. The microstructural observations revealed that the majority of the artefacts suffered cycles of thermo-mechanical processing after casting. The diversity of metals/alloys identified was a discovery of great interest, specifically due to the presence of brasses. Their presence can be interpreted as importations related to the circulation of exogenous products during the Proto-history and/or to the deposition of materials during different moments at the site, from the transition of Late Bronze Age/Early Iron Age (Orientalizing period) onwards, as during the Roman period.

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Dissertação de mestrado em Biofísica e Bionanossistemas

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Dissertação de mestrado em Estatística

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There are two significant reasons for the uncertainties of water demand. On one hand, an evolving technological world is plagued with accelerated change in lifestyles and consumption patterns; and on the other hand, intensifying climate change. Therefore, with an uncertain future, what enables policymakers to define the state of water resources, which are affected by withdrawals and demands? Through a case study based on thirteen years of observation data in the Zayandeh Rud River basin in Isfahan province located in Iran, this paper forecasts a wide range of urban water demand possibilities in order to create a portfolio of plans which could be utilized by different water managers. A comparison and contrast of two existing methods are discussed, demonstrating the Random Walk Methodology, which will be referred to as the â On uncertainty pathâ , because it takes the uncertainties into account and can be recommended to managers. This On Uncertainty Path is composed of both dynamic forecasting method and system simulation. The outcomes show the advantage of such methods particularly for places that climate change will aggravate their water scarcity, such as Iran.

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OBJECTIVE: To access the incidence of diagnostic errors in the initial evaluation of children with cardiac murmurs. METHODS: We evaluated our 7-years of experience in a public pediatric cardiology outpatient clinic. Of 3692 patients who were sent to the hospital, 2603 presented with a heart murmur and were investigated. Patients for whom a disagreement existed between the initial and final diagnoses were divided into the following 2 groups: G1 (n=17) with an initial diagnosis of an innocent murmur and a final diagnosis of cardiopathy, and G2 (n=161) with an initial diagnosis of cardiopathy and a final diagnosis of a normal heart. RESULTS: In G1, the great majority of patients had cardiac defects with mild hemodynamic repercussions, such as small ventricular septal defect and mild pulmonary stenosis. In G2, the great majority of structural defects were interventricular communication, atrial septal defect and pulmonary valve stenosis. CONCLUSION: A global analysis demonstrated that diagnostic error in the initial evaluation of children with cardiac murmurs is real, reaching approximately 6% of cases. The majority of these misdiagnoses were in patients with an initial diagnosis of cardiopathy, which was not confirmed through later complementary examinations. Clinical cardiovascular examination is an excellent resource in the evaluation of children suspected of having cardiopathy. Immediate outpatient discharge of children with an initial diagnosis of an innocent heart murmur seems to be a suitable approach.

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Los eventos transitorios únicos analógicos (ASET, Analog Single Event Transient) se producen debido a la interacción de un ión pesado o un protón de alta energía con un dispositivo sensible de un circuito analógico. La interacción del ión con un transistor bipolar o de efecto de campo MOS induce pares electrón-hueco que provocan picos que pueden propagarse a la salida del componente analógico provocando transitorios que pueden inducir fallas en el nivel sistema. Los problemas más graves debido a este tipo de fenómeno se dan en el medioambiente espacial, muy rico en iones pesados. Casos típicos los constituyen las computadoras de a bordo de satélites y otros artefactos espaciales. Sin embargo, y debido a la continua contracción de dimensiones de los transistores (que trae aparejado un aumento de sensibilidad), este fenómeno ha comenzado a observarse a nivel del mar, provocado fundamentalmente por el impacto de neutrones atmosféricos. Estos efectos pueden provocar severos problemas a los sistemas informáticos con interfaces analógicas desde las que obtienen datos para el procesamiento y se han convertido en uno de los problemas más graves a los que tienen que hacer frente los diseñadores de sistemas de alta escala de integración. Casos típicos son los Sistemas en Chip que incluyen módulos de procesamiento de altas prestaciones como las interfaces analógicas.El proyecto persigue como objetivo general estudiar la susceptibilidad de sistemas informáticos a ASETs en sus secciones analógicas, proponiendo estrategias para la mitigación de los errores.Como objetivos específicos se pretende: -Proponer nuevos modelos de ASETs basados en simulaciones en el nivel dispositivo y resueltas por el método de elementos finitos.-Utilizar los modelos para identificar las secciones más propensas a producir errores y consecuentemente para ser candidatos a la aplicación de técnicas de endurecimiento a radiaciones.-Utilizar estos modelos para estudiar la naturaleza de los errores producidos en sistemas de procesamiento de datos.-Proponer soluciones novedosas para la mitigación de estos efectos en los mismos circuitos analógicos evitando su propagación a las secciones digitales.-Proponer soluciones para la mitigación de los efectos en el nivel sistema.Para llevar a cabo el proyecto se plantea un procedimiento ascendente para las investigaciones a realizar, comenzando por descripciones en el nivel físico para posteriormente aumentar el nivel de abstracción en el que se encuentra modelado el circuito. Se propone el modelado físico de los dispositivos MOS y su resolución mediante el Método de Elementos Finitos. La inyección de cargas en las zonas sensibles de los modelos permitirá determinar los perfiles de los pulsos de corriente que deben inyectarse en el nivel circuito para emular estos efectos. Estos procedimientos se realizarán para los distintos bloques constructivos de las interfaces analógicas, proponiendo estrategias de mitigación de errores en diferentes niveles.Los resultados esperados del presente proyecto incluyen hardware para detección de errores y tolerancia a este tipo de eventos que permitan aumentar la confiabilidad de sistemas de tratamiento de la información, así como también nuevos datos referentes a efectos de la radiación en semiconductores, nuevos modelos de fallas transitorias que permitan una simulación de estos eventos en el nivel circuito y la determinación de zonas sensibles de interfaces analógicas típicas que deben ser endurecidas para radiación.

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We present a model of learning in which agents learn from errors. If an action turns out to be an error, the agent rejects not only that action but also neighboring actions. We find that, keepng memory of his errors, under mild assumptions an acceptable solution is asymptotically reached. Moreover, one can take advantage of big errors for a faster learning.

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This paper studies behavior in experiments with a linear voluntary contributions mechanism for public goods conducted in Japan, the Netherlands, Spain and the USA. The same experimental design was used in the four countries. Our 'contribution function' design allows us to obtain a view of subjects' behavior from two complementary points of view. If yields information about situations where, in purely pecuniary terms, it is a dominant strategy to contribute all the endowment and about situations where it is a dominant strategy to contribute nothing. Our results show, first, that differences in behavior across countries are minor. We find that when people play "the same game" they behave similarly. Second, for all four countries our data are inconsistent with the explanation that subjects contribute only out of confusion. A common cooperative motivation is needed to explain the date.

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Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

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This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model’s posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we extend Ireland’s (2001, 2004) hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and co-movements of the model’s errors not explained by the basic RBC model. The results of marginal likelihood ratio tests reveal that the more general model of the errors significantly improves the model’s fit relative to the VAR and AR alternatives. Moreover, despite setting the RBC model a more difficult task under the VARMA specification, our analysis, based on forecast error and spectral decompositions, suggests that the RBC model is still capable of explaining a significant fraction of the observed variation in macroeconomic aggregates in the post-war U.S. economy.

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Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.