969 resultados para Multiple structural breaks


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Resumen tomado de la publicaci??n

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This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.

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A comprehensive study of the complexes A4[U(NCS)8] (A = Cs, Et4N, nBu4N) and A3[UO2(NCS)5] (A = Cs, Et4N) is described, with the crystal structures of [nBu4N]4[U(NCS)8]·2MeCN and Cs3[UO2(NCS)5]·O0.5 reported. The magnetic properties of square antiprismatic Cs4[U(NCS)8] and cubic [Et4N]4[U(NCS)8] have been probed by SQUID magnetometry. The geometry has an important impact on the low-temperature magnetic moments: at 2 K, μeff = 1.21 μB and 0.53 μB, respectively. Electronic absorption and photoluminescence spectra of the uranium(IV) compounds have been measured. The redox chemistry of [Et4N]4[U(NCS)8] has been explored using IR and UV–vis spectroelectrochemical methods. Reversible 1-electron oxidation of one of the coordinated thiocyanate ligands occurs at +0.22 V vs Fc/Fc+, followed by an irreversible oxidation to form dithiocyanogen (NCS)2 which upon back reduction regenerates thiocyanate anions coordinating to UO22+. NBO calculations agree with the experimental spectra, suggesting that the initial electron loss of [U(NCS)8]4– is delocalized over all NCS– ligands. Reduction of the uranyl(VI) complex [Et4N]3[UO2(NCS)5] to uranyl(V) is accompanied by immediate disproportionation and has only been studied by DFT methods. The bonding in [An(NCS)8]4– (An = Th, U) and [UO2(NCS)5]3– has been explored by a combination of DFT and QTAIM analysis, and the U–N bonds are predominantly ionic, with the uranyl(V) species more ionic that the uranyl(VI) ion. Additionally, the U(IV)–NCS ion is more ionic than what was found for U(IV)–Cl complexes.

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This project constructs a structural model of the United States Economy. This task is tackled in two separate ways: first econometric methods and then using a neural network, both with a structure that mimics the structure of the U.S. economy. The structural model tracks the performance of U.S. GDP rather well in a dynamic simulation, with an average error of just over 1 percent. The neural network performed well, but suffered from some theoretical, as well as some implementation issues.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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After ionising radiation double-strand breaks (dsb) are lethal if not repaired or misrepaired. Cell killing is greatly enhanced by hyperthermia and it is questioned here whether heat not only affects dsb repair capacity but also fidelity in a chromosomal context. dsb repair experiments were designed so as to mainly score non-homologous end joining, while homologous recombination was largely precluded. Human male G0 fibroblasts were either preheated (45°C, 20 min) or not before X-irradiation. dsb induction and repair were measured by conventional gel electrophoresis and an assay combining restriction digestion using a rare cutting enzyme (NotI) and Southern hybridisation, which detects large chromosomal rearrangements (>100 kb). dsb induction rate in an X-chromosomal NotI fragment was 4.8 × 10–3 dsb/Gy/Mb. Similar values were found for the genome overall and also when cells were preheated. After 50 Gy, fibroblasts were competent to largely restore the original restriction fragment size. Five per cent of dsb remained non-rejoined and 14% were misrejoined. Correct restitution of restriction fragments occurred preferably during the first hour but continued at a slow rate for 12–16 h. In addition, dsb appeared to misrejoin throughout the entire repair period. After hyperthermia the fractions of non-rejoined and misrejoined dsb were similarly increased to 13 and 51%, respectively. It is suggested that heat increases the probability of dsb being incorrectly rejoined but it is not likely to interfere with one dsb repair pathway in particular.

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The thymidine analog fialuridine deoxy-2-fluoro-beta-D-arabinofuranosyl)-5-iodouracil (FIAU) was toxic in trials for chronic hepatitis B infection. One mechanism postulated that defective mtDNA replication was mediated through inhibition of DNA polymerase-gamma (DNA pol-gamma), by FIAU triphosphate (FIALTP) or by triphosphates of FIAU metabolites. Inhibition kinetics and primer-extension analyses determined biochemical mechanisms of FIAU, 1-(2-deoxy-2-fluoro-beta-D-arabinofuranosyl) -5-methyluracil (FAU), 1-(2-deoxy-2-fluoro-beta-D-arabinofuranosyl)uracil triphosphate (TP) inhibition of DNA pol-gamma. dTMP incorporation by DNA pol-gamma was inhibited competitively by FIAUTP, FMAUTP, and FAUTP (K1=0.015, 0.03, and 1.0 microM, respectively). By using oliginucleotide template-primers. DNA pol-gamma incorporated each analog into DNA opposite a single adenosine efficiently without effects on DNA chain elongation. Incorporation of multiple adjacent analogs at positions of consecutive adenosines dramatically impaired chain elongation by DNA pol-gamma. Effects of FIAU, FMAU, and FAU on HepG2 cell mmtDNA abundance and ultrastructure were determined. After 14 days, mtDNA decreased by 30% with 20 microM FIAU or 20 microM FMAU and decreased less than 10% with 100 microM FAU. FIAU and FMAU disrupted mitochondria and caused accumulation of intracytoplasmic lipid droplets. Biochemical and cell biological findings suggest that FIAU and its metabolites inhibit mtDNA replication, most likely at positions of adenosine tracts, leading to decreased mtDNA and mitochondrial ultrastructural defects.

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Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

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This paper uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridge. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change before and after damage are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of proposed structure with six damage scenarios. It is concluded that the modal strain energy method is competent for application on multiple-girder composite bridge, as evidenced through the example treated in this paper.

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Qualitative research methods require transparency to ensure the ‘trustworthiness’ of the data analysis. The intricate processes of organizing, coding and analyzing the data are often rendered invisible in the presentation of the research findings, which requires a ‘leap of faith’ for the reader. Computer assisted data analysis software can be used to make the research process more transparent, without sacrificing rich, interpretive analysis by the researcher. This article describes in detail how one software package was used in a poststructural study to link and code multiple forms of data to four research questions for fine-grained analysis. This description will be useful for researchers seeking to use qualitative data analysis software as an analytic tool.

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The early stages of the building design process are when the most far reaching decisions are made regarding the configuration of the proposed project. This paper examines methods of providing decision support to building designers across multiple disciplines during the early stage of design. The level of detail supported is at the massing study stage where the basic envelope of the project is being defined. The block outlines on the building envelope are sliced into floors. Within a floor the only spatial divisions supported are the “user” space and the building core. The building core includes vertical transportation systems, emergency egress and vertical duct runs. The current focus of the project described in the paper is multi-storey mixed use office/residential buildings with car parking. This is a common type of building in redevelopment projects within and adjacent to the central business districts of major Australian cities. The key design parameters for system selection across the major systems in multi-storey building projects - architectural, structural, HVAC, vertical transportation, electrical distribution, fire protection, hydraulics and cost – are examined. These have been identified through literature research and discussions with building designers from various disciplines. This information is being encoded in decision support tools. The decision support tools communicate through a shared database to ensure that the relevant information is shared across all of the disciplines. An internal data model has been developed to support the very early design phase and the high level system descriptions required. A mapping to IFC 2x2 has also been defined to ensure that this early information is available at later stages of the design process.

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Assessing the structural health state of urban infrastructure is crucial in terms of infrastructure sustainability. This chapter uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridges. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely, the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change, before and after damage, are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of a proposed structure with six damage scenarios. It is concluded that the modal strain energy method is capable of application to multiple-girder composite bridges, as evidenced through the example treated in this chapter.

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This paper presents the feasibility of using structural modal strain energy as a parameter employed in correlation- based damage detection method for truss bridge structures. It is an extension of the damage detection method adopting multiple damage location assurance criterion. In this paper, the sensitivity of modal strain energy to damage obtained from the analytical model is incorporated into the correlation objective function. Firstly, the sensitivity matrix of modal strain energy to damage is conducted offline, and for an arbitrary damage case, the correlation coefficient (objective function) is calculated by multiplying the sensitivity matrix and damage vector. Then, a genetic algorithm is used to iteratively search the damage vector maximising the correlation between the corresponding modal strain energy change (hypothesised) and its counterpart in measurement. The proposed method is simulated and compared with the conventional methods, e.g. frequency-error method, coordinate modal assurance criterion and multiple damage location assurance criterion using mode shapes on a numerical truss bridge structure. The result demonstrates the modal strain energy correlation method is able to yield acceptable damage detection outcomes with less computing efforts, even in a noise contaminated condition.

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While a number of factors have been highlighted in the innovation adoption literature, little is known about whether different factors are related to innovation adoption in differently-sized firms. We used preliminary case studies of small, medium and large firms to ground our hypotheses, which were then tested using a survey of 94 firms. We found that external stakeholder pressure and non-financial readiness were related to innovation adoption in SMEs; but that for large firms, adoption was related to the opportunity to innovate. It may be that the difficulties of adopting innovations, including both the financial cost and the effort involved, are too great for SMEs to overcome unless there is either a compelling need (external pressure) or enough in-house capability (non-financial readiness). This suggests that SMEs are more likely to have innovation “pushed” onto them while large firms are more likely to “pull” innovations when they have the opportunity.