924 resultados para nuisance parameter


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Arctic lowland landscapes have been modified by thermokarst lake processes throughout the Holocene. Thermokarst lakes form as a result of ice-rich permafrost degradation and they may expand over time through thermal and mechanical shoreline erosion. We studied proximal and distal sedimentary records from a thermokarst lake located on the Arctic Coastal Plain of northern Alaska to reconstruct the impact of catchment dynamics and morphology on the lacustrine depositional environment and to quantify carbon accumulation in thermokarst lake sediments. Short cores were collected for analysis of pollen, sedimentological and geochemical proxies. Radiocarbon and Pb/Cs dating, as well as extrapolation of measured historic lake expansion rates, were applied to estimate a minimum lake age of ~ 1,400 calendar years BP. The pollen record is in agreement with the young lake age as it does not include evidence of the "alder high" that occurred in the region ~ 4.0 cal ka BP. The lake most likely initiated from a remnant pond in a drained thermokarst lake basin (DTLB) and deepened rapidly as evidenced by accumulation of laminated sediments. Increasing oxygenation of the water column as shown by higher Fe/Ti and Fe/S ratios in the sediment indicate shifts in ice regime with increasing water depth. More recently, the sediment source changed as the thermokarst lake expanded through lateral permafrost degradation, alternating from redeposited DTLB sediments, to increased amounts of sediment from eroding, older upland deposits, followed by a more balanced combination of both DTLB and upland sources. The characterizing shifts in sediment sources and depositional regimes in expanding thermokarst lakes were therefore archived in the thermokarst lake sedimentary record. This study also highlights the potential for Arctic lakes to recycle old carbon from thawing permafrost and thermokarst processes.

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Single-molecule manipulation experiments of molecular motors provide essential information about the rate and conformational changes of the steps of the reaction located along the manipulation coordinate. This information is not always sufficient to define a particular kinetic cycle. Recent single-molecule experiments with optical tweezers showed that the DNA unwinding activity of a Phi29 DNA polymerase mutant presents a complex pause behavior, which includes short and long pauses. Here we show that different kinetic models, considering different connections between the active and the pause states, can explain the experimental pause behavior. Both the two independent pause model and the two connected pause model are able to describe the pause behavior of a mutated Phi29 DNA polymerase observed in an optical tweezers single-molecule experiment. For the two independent pause model all parameters are fixed by the observed data, while for the more general two connected pause model there is a range of values of the parameters compatible with the observed data (which can be expressed in terms of two of the rates and their force dependencies). This general model includes models with indirect entry and exit to the long-pause state, and also models with cycling in both directions. Additionally, assuming that detailed balance is verified, which forbids cycling, this reduces the ranges of the values of the parameters (which can then be expressed in terms of one rate and its force dependency). The resulting model interpolates between the independent pause model and the indirect entry and exit to the long-pause state model

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The authors thank Professor Iber^e Luiz Caldas for the suggestions and encouragement. The authors F.F.G.d.S., R.M.R., J.C.S., and H.A.A. acknowledge the Brazilian agency CNPq and state agencies FAPEMIG, FAPESP, and FAPESC, and M.S.B. also acknowledges the EPSRC Grant Ref. No. EP/I032606/1.

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In this work we explore optimising parameters of a physical circuit model relative to input/output measurements, using the Dallas Rangemaster Treble Booster as a case study. A hybrid metaheuristic/gradient descent algorithm is implemented, where the initial parameter sets for the optimisation are informed by nominal values from schematics and datasheets. Sensitivity analysis is used to screen parameters, which informs a study of the optimisation algorithm against model complexity by fixing parameters. The results of the optimisation show a significant increase in the accuracy of model behaviour, but also highlight several key issues regarding the recovery of parameters.

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This paper examines assumptions about future prices used in real estate applications of DCF models. We confirm both the widespread reliance on an ad hoc rule of increasing period-zero capitalization rates by 50 to 100 basis points to obtain terminal capitalization rates and the inability of the rule to project future real estate pricing. To understand how investors form expectations about future prices, we model the spread between the contemporaneously period-zero going-in and terminal capitalization rates and the spread between terminal rates assigned in period zero and going-in rates assigned in period N. Our regression results confirm statistical relationships between the terminal and next holding period going-in capitalization rate spread and the period-zero discount rate, although other economically significant variables are statistically insignificant. Linking terminal capitalization rates by assumption to going-in capitalization rates implies investors view future real estate pricing with myopic expectations. We discuss alternative specifications devoid of such linkage that align more with a rational expectations view of future real estate pricing.

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Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPMr). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to succesfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.

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Thesis (Ph.D.)--University of Washington, 2016-08

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A deterministic model of tuberculosis in Cameroon is designed and analyzed with respect to its transmission dynamics. The model includes lack of access to treatment and weak diagnosis capacity as well as both frequency-and density-dependent transmissions. It is shown that the model is mathematically well-posed and epidemiologically reasonable. Solutions are non-negative and bounded whenever the initial values are non-negative. A sensitivity analysis of model parameters is performed and the most sensitive ones are identified by means of a state-of-the-art Gauss-Newton method. In particular, parameters representing the proportion of individuals having access to medical facilities are seen to have a large impact on the dynamics of the disease. The model predicts that a gradual increase of these parameters could significantly reduce the disease burden on the population within the next 15 years.

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This article shows a general way to implement recursive functions calculation by linear tail recursion. It emphasizes the use of tail recursion to perform computations efficiently.