Estimation of Turbulent Fluxes Using the Flux-Variance Method over an Alpine Meadow Surface in the Eastern Tibetan Plateau

The flux-variance similarity relation and the vertical transfer of scalars exhibit dissimilarity over different types of surfaces, resulting in different parameterization approaches of relative transport efficiency among scalars to estimate turbulent fluxes using the flux-variance method. We investigated these issues using eddycovariance measurements over an open, homogeneous and flat grassland in the eastern Tibetan Plateau in summer under intermediate hydrological conditions during rainy season. In unstable conditions, the temperature, water vapor, and CO2 followed the flux-variance similarity relation, but did not show in precisely the same way due to different roles （active or passive） of these scalars. Similarity constants of temperature, water vapor and CO2 were found to be 1.12, 1.19 and 1.17, respectively. Heat transportation was more eft% cient than water vapor and CO2. Based on the estimated sensible heat flux, five parameterization methods of relative transport efficiency of heat to water vapor and CO2 were examined to estimate latent heat and CO2 fluxes. The strategy of local determination of flux-variance similarity relation is recommended for the estimation of latent heat and CO2 fluxes. This approach is better for representing the averaged relative transport efficiency, and technically easier to apply, compared to other more complex ones.

Evaluation of Third-Order Method for the Tests of Variance Component in Linear Mixed Models

Mixed models provide a wide range of applications including hierarchical modeling and longitudinal studies. The tests of variance component in mixed models have long been a methodological challenge because of its boundary conditions. It is well documented in literature that the traditional first-order methods: likelihood ratio statistic, Wald statistic and score statistic, provide an excessively conservative approximation to the null distribution. However, the magnitude of the conservativeness has not been thoroughly explored. In this paper, we propose a likelihood-based third-order method to the mixed models for testing the null hypothesis of zero and non-zero variance component. The proposed method dramatically improved the accuracy of the tests. Extensive simulations were carried out to demonstrate the accuracy of the proposed method in comparison with the standard first-order methods. The results show the conservativeness of the first order methods and the accuracy of the proposed method in approximating the p-values and confidence intervals even when the sample size is small.

Robust Variance Components Estimation in the PERG Mixed Distributions of Empirical Variances—PEROBVC Method

A mixed distribution of empirical variances, composed of two distributions the basic and contaminating ones, and referred to as PERG mixed distribution of empirical variances, is considered. In the paper a robust inverse problem solution is given, namely a (new) robust method for estimation of variances of both distributions—PEROBVC Method, as well as the estimates for the numbers of observations for both distributions and, in this way also the estimate of contamination degree.

THE ESTIMATION OF VARIANCE AND COVARIANCE COMPONENTS FOR GPS BASELINE NETWORK BY MINQUE METHOD

This paper studies the estimation of variance and covariance compo-nents for GPS baseline network by MINQUE method.The fundamental rule forselecting variance-covariance model has been presented,and the alternative algo-rithm which simultaneouly estimates fixed variance components and scalled vari-ance components of the distance,azimuth and geodetic height difference for a GPSbaseline vector has been developed.

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

A note on the estimation of variance for big BAF sampling

Background:The double sampling method known as“big BAF sampling”has been advocated as a way to reduce sampling effort while still maintaining a reasonably precise estimate of volume.A well-known method for variance determination,Bruce’s method,is customarily used because the volume estimator takes the form of a product of random variables.However,the genesis of Bruce’s method is not known to most foresters who use the method in practice.Methods:We establish that the Taylor series approximation known as the Delta method provides a plausible explanation for the origins of Bruce’s method.Simulations were conducted on two different tree populations to ascertain the similarities of the Delta method to the exact variance of a product.Additionally,two alternative estimators for the variance of individual tree volume-basal area ratios,which are part of the estimation process,were compared within the overall variance estimation procedure.Results:The simulation results demonstrate that Bruce’s method provides a robust method for estimating the variance of inventories conducted with the big BAF method.The simulations also demonstrate that the variance of the mean volume-basal area ratios can be computed using either the usual sample variance of the mean or the ratio variance estimators with equal accuracy,which had not been shown previously for Big BAF sampling.Conclusions:A plausible explanation for the origins of Bruce’s method has been set forth both historically and mathematically in the Delta Method.In most settings,there is evidently no practical difference between applying the exact variance of a product or the Delta method—either can be used.A caution is articulated concerning the aggregation of tree-wise attributes into point-wise summaries in order to test the correlation between the two as a possible indicator of the need for further covariance augmentation.

Optimization of the gas separation performance of polyurethane–zeolite 3A and ZSM-5 mixed matrix membranes using response surface methodology

In the present work, the response surface method software was used with five measurement levels with three factors.These were applied for the optimization of operating parameters that affected gas separation performance of polyurethane–zeolite 3A, ZSM-5 mixed matrix membranes.The basis of the experiments was a rotatable central composite design(CCD).The three independent variables studied were: zeolite content(0–24 wt%), operating temperature(25–45 ℃) and operating pressure(0.2–0.1 MPa).The effects of these three variables on the selectivity and permeability membranes were studied by the analysis of variance(ANOVA).Optimal conditions for the enhancement of gas separation performances of polyurethane–3A zeolite were found to be 18 wt%, 30 ℃ and 0.8 MPa respectively.Under these conditions, the permeabilities of carbon dioxide, methane, oxygen and nitrogen gases were measured at 138.4, 22.9, 15.7 and 6.4 Barrer respectively while the CO_2/CH_4, CO_2/N_2 and O_2/N_2 selectivities were 5.8, 22.5 and 2.5, respectively.Also, the optimal conditions for improvement of the gas separation performance of polyurethane–ZSM 5 were found to be 15.64 wt%, 30 ℃ and 4 bar.The permeabilities of these four gases(i.e.carbon dioxide, methane, oxygen and nitrogen) were 164.7, 21.2, 21.5 and 8.1 Barrer while the CO_2/CH_4, CO_2/N_2 and O_2/N_2 selectivities were 7.8, 20.6 and 2.7 respectively.

Comprehensive Study on the Estimation of the Variance Components of Traverse Nets

This paper advances a new simplified formula for estimating variance components ,sums up the basic law to calculate the weights of observed values and a circulation method using the increaments of weights when estimating the variance components of traverse nets,advances the charicteristic roots method to estimate the variance components of traveres nets and presents a practical method to make two real and symmetric matrices two diagonal ones.

An approximate point-based alternative for the estimation of variance under big BAF sampling

Background:A new variance estimator is derived and tested for big BAF(Basal Area Factor)sampling which is a forest inventory system that utilizes Bitterlich sampling(point sampling)with two BAF sizes,a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation.Methods:The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means.The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula.Results:Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature.In simulations the new estimator performed well and comparably to existing variance formulas.Conclusions:A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees,an assumption required by all previous big BAF variance estimation formulas.Although this correlation was negligible on the simulation stands used in this study,it is conceivable that the correlation could be significant in some forest types,such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition.We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area.We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1n where n is the number of sample points.

Time Discretized Variational Iteration Method for the Stochastic Volatility Process with Jumps

A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean reversion and volatility clustering between returns and volatility with uphill movements in price asserts. Thus, in this article, we propose to solve the SVPJ model numerically through a discretized variational iteration method (DVIM) to obtain sample paths for the state variable and variance process at various timesteps and replications in order to estimate the expected jump times at various iterates resulting from executing the DVIM as n increases. These jumps help in estimating the degree of randomness in the financial market. It was observed that the average computed expected jump times for the state variable and variance process is moderated by the parameters (variance process through mean reversion), Θ (long-run mean of the variance process), σ (volatility variance process) and λ (constant intensity of the Poisson process) at each iterate. For instance, when = 0.0, Θ = 0.0, σ = 0.0 and λ = 1.0, the state variable cluttered maximally compared to the variance process with less volatility cluttering with an average computed expected jump times of 52.40607869 as n increases in the DVIM scheme. Similarly, when = 3.99, Θ = 0.014, σ = 0.27 and λ = 0.11, the stochastic jumps for the state variable are less cluttered compared to the variance process with maximum volatility cluttering as n increases in the DVIM scheme. In terms of option pricing, the value 52.40607869 suggest a better bargain compared to the value 20.40344029 due to the fact that it yields less volatility rate. MAPLE 18 software was used for all computations in this research.

Comparison of the local pivotal method and systematic sampling for national forest inventories

Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Variance Reduction Technique for Estimating Value-at-Risk based on the Cross - Entropy

Value-at-Risk （VaR） estimation via Monte Carlo （MC） simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high portfolio losses （more general risk measure） based on the Cross - Entropy importance sampling is developed. This algorithm can easily be applied in any light- or heavy-tailed case without an extra adaptation. Besides, it does not loose in the performance in comparison to other known methods. A numerical study in both cases is performed and the variance reduction rate is compared with other known methods. The problem of VaR estimation using procedures for estimating the probability of high portfolio losses is also discussed.

New Configurations of the Fuzzy Fractional Differential Boussinesq Model with Application in Ocean Engineering and Their Analysis in Statistical Theory

The fractional-order Boussinesq equations(FBSQe)are investigated in this work to see if they can effectively improve the situation where the shallow water equation cannot directly handle the dispersion wave.The fuzzy forms of analytical FBSQe solutions are first derived using the Adomian decomposition method.It also occurs on the sea floor as opposed to at the functionality.A set of dynamical partial differential equations(PDEs)in this article exemplify an unconfined aquifer flow implication.Thismethodology can accurately simulate climatological intrinsic waves,so the ripples are spread across a large demographic zone.The Aboodh transform merged with the mechanism of Adomian decomposition is implemented to obtain the fuzzified FBSQe in R,R^(n) and(2nth)-order involving generalized Hukuhara differentiability.According to the system parameter,we classify the qualitative features of the Aboodh transform in the fuzzified Caputo and Atangana-Baleanu-Caputo fractional derivative formulations,which are addressed in detail.The illustrations depict a comparison analysis between the both fractional operators under gH-differentiability,as well as the appropriate attributes for the fractional-order and unpredictability factorsσ∈[0,1].A statistical experiment is conducted between the findings of both fractional derivatives to prevent changing the hypothesis after the results are known.Based on the suggested analyses,hydrodynamic technicians,as irrigation or aquifer quality experts,may be capable of obtaining an appropriate storage intensity amount,including an unpredictability threshold.

基于自动重要抽样方法的减方差技巧体系构建与验证

屏蔽计算问题根据求解目标不同一般可分为源-探测器问题、区域问题和全局问题。MCShield研究团队针对3类问题中存在的深穿透问题提出了相应的减方差技巧,本文以此为基础构建了基于自动重要抽样(AIS)方法的减方差技巧体系,并开展了验证工作。针对源-探测器问题,采用NUREG/CR-6115 PWR压力容器计算基准题对小探测器自动重要抽样(SDAIS)方法进行验证。结果表明,SDAIS方法的计算效率约为AIS方法的7倍。此外还提出并验证了基于AIS伴随蒙特卡罗的耦合减方差(AIS-CADIS)方法,将AIS方法引入到蒙特卡罗伴随计算中,取得了良好的效果。针对全局问题,提出网格化-AIS方法并使用简化反应堆屏蔽计算算例进行验证,结果表明,网格化-AIS方法的计算效率是AIS方法的12倍左右,是直接蒙特卡罗方法的290倍左右。

区域创新发展能力评价与空间差异测度

区域创新发展能力是区域经济在发展过程中获取竞争优势的重要影响因素,文章从区域创新发展规模、区域创新发展条件、区域创新发展活力、区域创新发展环境四个子系统筛选出20个可观测指标构建区域创新发展能力评价指标体系,应用面板数据熵权法对我国31个省份2014—2019年创新发展能力进行综合评价,利用泰尔指数和莫兰指数对创新发展能力空间差异特征进行分析,并为区域创新发展能力实现协同发展提供参考。

血清微量元素与营养素对骨坏死因果效应的孟德尔随机化分析

背景:多项临床研究观察表明血清微量元素和营养素与骨坏死之间存在密切关系,但血清微量元素和营养素与骨坏死之间是否存在遗传因果效应尚不清楚。目的:通过孟德尔随机化方法探讨血清微量元素与营养素对骨坏死的因果效应。方法:暴露因素血清微量元素和维生素的单核苷酸多态性来自已发表的UK Biobank数据库以及全基因组关联研究公开数据库。结局事件骨坏死全基因组关联研究数据来自FinnGen Biobank数据库。采用孟德尔随机化方法探讨7种微量元素和3种营养素与骨坏死之间的因果关系。利用逆方差加权、MR-Egger和加权中位数等方法进行因果推断,计算F值统计量以确保工具变量的稳健性,采用Cochran’s Q统计法和留一法进行异质性检验,采用MR-Egger回归法、MR-PRESSO进行水平多效性检验。使用PhenoScanner数据库剔除具有水平多效性的单核苷酸多态性,以确保结果的可靠性。结果与结论:经过孟德尔随机化分析发现,血清硒、磷酸盐以及维生素C、维生素E与骨坏死之间存在明显的因果关系。血清硒、维生素C、维生素E对骨坏死有保护作用,磷酸盐摄入过多会增加骨坏死的风险。研究过程中没有发现异质性和水平多效性,孟德尔随机化统计效力(Power值>80%)显示上述4项结果可靠,研究结果对于开发有针对性的预防及治疗骨坏死的措施具有重要临床意义。