Uncertainty evaluation of the spectral UV irradiance evaluated by using the UVSPEC radiative transfer model

The radiative transfer models allow calculating the spectral UV irradiance from some set of measured input quantities linked with the surface reflectivity, the solar zenith angle, the ozone column and the characteristics of clouds and aerosols. The spectral irradiance yielded by a model is influenced by errors in the measurement of the input quantities. In this paper, the influences of these errors are characterized and compared with other systematic effects through an uncertainty analysis. We evaluated the uncertainty of the spectral UV irradiance rendered by the UVSPEC model, under cloudless sky conditions. In order to express the uncertainty of the output quantities (the global, direct and diffuse irradiances) in terms of the standard uncertainties of the input quantities, we used a Monte Carlo-based uncertainty propagation technique. We found that the uncertainty of the irradiance in the UV-B part of the spectrum was strongly influenced by the uncertainty attributed to the ozone column datum. Moreover, the uncertainities associated with the aerosol parameters accounted for most of the UV-A global irradiance uncertainty; the latter increased from about 4% under low aerosol conditions, up to about 14% in case of polluted air. We conclude that the UV irradiance evaluation through radiative transfer models requires paying special attention to the assessment of the aerosols properties.     

A sharper uncertainty principle

The work strengthens the result established by L. Cohen on uncertainty principle involving phase derivative. We propose stronger uncertainty principles not only in the classical setting for Fourier transform, but also for self-adjoint operators. We also deduce the conditions that give rise to the equal relation of the uncertainty principle. Examples are provided to show that the new uncertainty principle is truly sharper than the existing ones in literature.     

Microbiological proficiency testing: a personal perspective

The author considers the fundamental differences between analyses in microbiology and those in chemistry and physics, deducing special issues for microbiological proficiency testing. He concludes that the variability and uncertainty implicit in microbiological analysis requires a broader range of proficiency scheme providers providing a broader range of services than in chemistry and physics. Received: 10 November 2000 Accepted: 3 December 2000     

Proactive heuristic procedures for robust project scheduling: An experimental analysis

Solution-robust project scheduling is a growing research field aiming at constructing proactive schedules to cope with multiple disruptions during project execution. When stochastic activity durations are considered, including time buffers between activities is a proven method to improve the stability of a baseline schedule.     

A multi-resolution,non-parametric,Bayesian framework for identification of spatially-varying model parameters

This paper proposes a hierarchical, multi-resolution framework for the identification of model parameters and their spatially variability from noisy measurements of the response or output. Such parameters are frequently encountered in PDE-based models and correspond to quantities such as density or pressure fields, elasto-plastic moduli and internal variables in solid mechanics, conductivity fields in heat diffusion problems, permeability fields in fluid flow through porous media etc. The proposed model has all the advantages of traditional Bayesian formulations such as the ability to produce measures of confidence for the inferences made and providing not only predictive estimates but also quantitative measures of the predictive uncertainty. In contrast to existing approaches it utilizes a parsimonious, non-parametric formulation that favors sparse representations and whose complexity can be determined from the data. The proposed framework in non-intrusive and makes use of a sequence of forward solvers operating at various resolutions. As a result, inexpensive, coarse solvers are used to identify the most salient features of the unknown field(s) which are subsequently enriched by invoking solvers operating at finer resolutions. This leads to significant computational savings particularly in problems involving computationally demanding forward models but also improvements in accuracy. It is based on a novel, adaptive scheme based on Sequential Monte Carlo sampling which is embarrassingly parallelizable and circumvents issues with slow mixing encountered in Markov Chain Monte Carlo schemes. The capabilities of the proposed methodology are illustrated in problems from nonlinear solid mechanics with special attention to cases where the data is contaminated with random noise and the scale of variability of the unknown field is smaller than the scale of the grid where observations are collected.     

Uncertainty calculations in the certification of reference materials

To serve as a measurement standard, a (certified) reference material must be stable. For this purpose, the material should undergo stability testing after it has been prepared. This paper looks at the statistical aspects of stability testing. Essentially, these studies can be described with analysis of variance statistics, including variant regression analysis. The latter is used in practice for both trend analysis and for the development of expressions for extrapolations. Extrapolation of stability data is briefly touched upon, as far as the combined standard uncertainty of the reference material is concerned. There are different options to validate the extrapolations made from initial stability studies, and some of them might influence the uncertainty of the reference material and/or the shelf-life. The latter is the more commonly observed consequence of what is called ’stability monitoring’. Received: 6 October 2000 Accepted: 4 December 2000     

人力资源成本预算与控制的理论分析

本的分析是人力资源成本管理体系研究的一部分，着重于探讨在人力资源成本管理体系中，人力资源成本的预算与控制与该体系中其他几部分之间的关系，研究人力资源成本管理实践中成本预算与控制的理论基础，并提出一些新的思路。     

Approaches to uncertainty evaluation based on proficiency testing schemes in chemical measurements

Since the advent of the Guide to the Expression of Uncertainty in Measurement (GUM) founding the principles of uncertainty evaluation, numerous projects have been carried out to develop alternative practical methods when it is impossible to model technical or economical aspects of the measurement process. These methods can use all the experimental data available to the laboratories, such as repeatability, reproducibility, quality-control charts, etc. The studies presented in this paper compare the results obtained by the modelling method from GUM with the uncertainties found by applying alternative methods. They show two examples, one in the field of environmental monitoring, the other in the biomedical field, based on the exploitation of PT schemes results. Presented at BERM-11, October 2007, Tsukuba, Japan.     

Application of possibility theory to investment decisions

Carlson and Fuller (2001, Fuzzy Sets and Systems, 122, 315–326) introduced the concept of possibilistic mean, variance and covariance of fuzzy numbers. In this paper, we extend some of these results to a nonlinear type of fuzzy numbers called adaptive fuzzy numbers (see Bodjanova (2005, Information Science, 172, 73–89) for detail). We then discuss the application of these results to decision making problems in which the parameters may involve uncertainty and vagueness. As an application, we develop expression for fuzzy net present value (FNPV) of future cash flows involving adaptive fuzzy numbers by using their possibilistic moments. An illustrative numerical example is given to illustrate the results.     

Reduced Vertex Set Result for Interval Semidefinite Optimization Problems

In this paper we propose a reduced vertex result for the robust solution of uncertain semidefinite optimization problems subject to interval uncertainty. If the number of decision variables is m and the size of the coefficient matrices in the linear matrix inequality constraints is n×n, a direct vertex approach would require satisfaction of 2 ^{n(m+1)(n+1)/2} vertex constraints: a huge number, even for small values of n and m. The conditions derived here are instead based on the introduction of m slack variables and a subset of vertex coefficient matrices of cardinality 2 ⁿ⁻¹, thus reducing the problem to a practically manageable size, at least for small n. A similar size reduction is also obtained for a class of problems with affinely dependent interval uncertainty. This work is supported by MIUR under the FIRB project “Learning, Randomization and Guaranteed Predictive Inference for Complex Uncertain Systems,” and by CNR RSTL funds.