Sensitivity analysis and uncertainty calculation for dispersion corrected density functional theory

The precision of binding energies and distances computed with dispersion-corrected density functional theory (DFT-D) is investigated by propagation of uncertainties, yielding relative uncertainties of several percent. Sensitivity analysis is used to calculate the geometry-dependent relative importance of each input parameter for the dispersion correction. While DFT-Ds are exact at asymptotically large distances, their damping functions are shown to play a significant role in binding geometries. This is demonstrated in detail for the interlayer binding of graphite. The techniques presented allow practitioners to quickly compute error bars and to get an a posteriori estimate about the transferability of their results. They can also aid the development of future dispersion corrections.     

铬铁矿中三氧化二铬测定结果不确定度的评定

讨论了影响铬铁矿中三氧化二铬测定结果不确定度的各种因素,并评定了地质样品中三氧化二铬测定结果的不确定度.结果表明,采用硫酸亚铁铵溶液滴定方法,当样品三氧化二铬的含量为43.78％时,其扩展不确定度U=0.32％.     

恒电流电解-BCO分光光度法测定纯铜中铜含量不确定度的评定

对采用恒电流电解-BC0分光光度法测定纯铜中铜含量的不确定度进行了评定.对测量重复性、电解重量法、光度法等影响测量结果的不确定度分量进行了分析和量化.当纯铜中铜的质量分数为99.93%时,扩展不确定度为0.01%,k=2.     

ICP-OES测定钒矿石中五氧化二钒的测量不确定度评定

介绍了电感耦合等离子体发射光谱法(ICP-OES)测定钒矿石中五氧化二钒测重不确定度的评定万法.建立了测量过程中各不确定度分量的数学模型,对不确定度来源进行了分析,并对不确定度分量进行量化.当钒矿石中五氧化二钒含量为0.419％时,其扩展不确定度为0.0070％(k=2).     

ICP-AES法测定烟花爆竹中16种化学成分的不确定度评定

采用电感耦合等离子体-原子发射光谱(ICP-AES)法测定烟花爆竹中16种化学成分并对测定结果的不确定度进行了评定和计算,提供了评定过程所需的各参数的采集和计算方法,定量表征了测定结果的可信程度.由计算得到的合成标准不确定度可以看出,不确定度主要来源于校准曲线的非线性,从而对此测定方法的可行性和准确性进行了科学的判断.     

差示扫描量热法对芝麻酚纯度标准物质的定值

建立了采用差示扫描量热法对芝麻酚纯度标准物质的定值及不确定度评价的数学模型、有效检测技术和分析方法.采用差示扫描量热法测量芝麻酚样品纯度的实验条件为升温速率3.0 K/min,称样量3.4～4.7 mg,炉内气体为静态空气.对通过均匀性检验和长期稳定性考察的芝麻酚纯度标准物质进行定值和不确定度评价,同时采用高效液相色谱...     

耐辐照玻璃成分分析标准物质的研制

采用高温熔融方法制备了玻璃成分分析标准物质.对标准物质的制备技术、均匀性检验、稳定性考察及定值不确定度进行了分析.研制的耐辐照成分分析标准物质具有良好的均匀性和稳定性,各组分不确定度均小于5％.     

Bayesian Variable Selection via Particle Stochastic Search

We focus on Bayesian variable selection in regression models. One challenge is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In this article, we propose a new computational approach based on sequential Monte Carlo (SMC), which we refer to as particle stochastic search (PSS). We illustrate PSS through applications to linear regression and probit models.     

Hysteresis effects under CIR interest rates

Most decision making research in real options focuses on revenue uncertainty assuming discount rates remain constant. However, for many decisions revenue or cost streams are relatively static and investment is driven by interest rate uncertainty, for example the decision to invest in durable machinery and equipment. Using interest rate models from Cox et al. (1985b), we generalize the work of Ingersoll and Ross (1992) in two ways. Firstly, we include real options on perpetuities (in addition to zero coupon cash flows). Secondly, we incorporate abandonment or disinvestment as well as investment options, and thus model interest rate hysteresis (parallel to revenue uncertainty in Dixit (1989a)). Under stochastic interest rates, economic hysteresis is found to be significant, even for small sunk costs.     

An efficient computational method for a stochastic dynamic lot-sizing problem under service-level constraints

We provide an efficient computational approach to solve the mixed integer programming (MIP) model developed by Tarim and Kingsman [8] for solving a stochastic lot-sizing problem with service level constraints under the static-dynamic uncertainty strategy. The effectiveness of the proposed method hinges on three novelties: (i) the proposed relaxation is computationally efficient and provides an optimal solution most of the time, (ii) if the relaxation produces an infeasible solution, then this solution yields a tight lower bound for the optimal cost, and (iii) it can be modified easily to obtain a feasible solution, which yields an upper bound. In case of infeasibility, the relaxation approach is implemented at each node of the search tree in a branch-and-bound procedure to efficiently search for an optimal solution. Extensive numerical tests show that our method dominates the MIP solution approach and can handle real-life size problems in trivial time.