Stability of localized operators

Let ?p, 1?p?∞, be the space of all p-summable sequences and Ca be the convolution operator associated with a summable sequence a. It is known that the ?p-stability of the convolution operator Ca for different 1?p?∞ are equivalent to each other, i.e., if Ca has ?p-stability for some 1?p?∞ then Ca has ?q-stability for all 1?q?∞. In the study of spline approximation, wavelet analysis, time-frequency analysis, and sampling, there are many localized operators of non-convolution type whose stability is one of the basic assumptions. In this paper, we consider the stability of those localized operators including infinite matrices in the Sjöstrand class, synthesis operators with generating functions enveloped by shifts of a function in the Wiener amalgam space, and integral operators with kernels having certain regularity and decay at infinity. We show that the ?p-stability (or Lp-stability) of those three classes of localized operators are equivalent to each other, and we also prove that the left inverse of those localized operators are well localized.     

沿海软粘土取土质量的对比分析

本文在简要介绍固定活塞式薄壁取土器取土技术基础上，详尽分析了在连云港、赤湾港和广深路所进行的软粘土取土技术对比试验结果。试验结果表明，土样扰动对于室内所测定的力学性指标影响极大，但对物理性指标影响不大。研究结果还有力地证明，只要操作正确，采用薄壁取土器能取得高质量土样，而采用敞口式厚壁取土器取出的土样扰动较大。     

Calibration of a stochastic health evolution model using NHIS data

This paper presents and calibrates an individual’s stochastic health evolution model. In this health evolution model, the uncertainty of health incidents is described by a stochastic process with a finite number of possible outcomes. We construct a comprehensive health status index (HSI) to describe an individual’s health status, as well as a health risk factor system (RFS) to classify individuals into different risk groups. Based on the maximum likelihood estimation (MLE) method and the method of nonlinear least squares fitting, model calibration is formulated in terms of two mixed-integer nonlinear optimization problems. Using the National Health Interview Survey (NHIS) data, the model is calibrated for specific risk groups. Longitudinal data from the Health and Retirement Study (HRS) is used to validate the calibrated model, which displays good validation properties. The end goal of this paper is to provide a model and methodology, whose output can serve as a crucial component of decision support for strategic planning of health related financing and risk management.     

Confidence-based optimisation for the newsvendor problem under binomial,Poisson and exponential demand

We introduce a novel strategy to address the issue of demand estimation in single-item single-period stochastic inventory optimisation problems. Our strategy analytically combines confidence interval analysis and inventory optimisation. We assume that the decision maker is given a set of past demand samples and we employ confidence interval analysis in order to identify a range of candidate order quantities that, with prescribed confidence probability, includes the real optimal order quantity for the underlying stochastic demand process with unknown stationary parameter(s). In addition, for each candidate order quantity that is identified, our approach produces an upper and a lower bound for the associated cost. We apply this approach to three demand distributions in the exponential family: binomial, Poisson, and exponential. For two of these distributions we also discuss the extension to the case of unobserved lost sales. Numerical examples are presented in which we show how our approach complements existing frequentist—e.g. based on maximum likelihood estimators—or Bayesian strategies.     

分层性物质的组合取样误差与份样数目之间的关系及其Monte Carlo模拟

本文从理论上研究了分层性物质的组合取样误差与份样数目之间的定量关系，表明份样数目与组合取样相对标准偏差的平方之积为一常数，称为组合取样常数，它与Ｉｎｇａｍ ｅｌｌｓ 和Ｓｗ ｉｔｚｅｒ 提出的取样常数是相似的，表示６８％ 置信度下组合取样的相对标准偏差达到１％ 时的份样数目。应用Ｍｏｎｔｅ Ｃａｒｌｏ 模拟技术对该理论进行了验证，结果令人满意。     

Recent advances in clinical microdialysis

Clinical microdialysis (MD) is a minimally invasive sampling technique that offers selective in-vivo measurement of free, active drug or biomolecule concentrations in human tissues and organs. From a regulatory perspective, MD can thus be seen as a suitable scientific tool that meets regulatory requirements for the study of tissue distribution or bioequivalence during drug development. From a clinical perspective, the use of MD in different applications has shown the potential to rationalize drug-dosing regimens and to influence clinical decision-making, although validation and correlation of MD-derived results with clinical response are required to promote routine clinical use of the technique. From an analytical perspective, highly sensitive analytical systems have increasingly become available for MD-sample analysis, and these have further improved the quality and the power of MD-derived information. Given the constant development in recent years, MD data might become an important part of new drug submissions and clinical treatment algorithms, and might positively influence patient benefit in the future.     

Vector sampling expansions in shift invariant subspaces

Multi-input multi-output (MIMO) sampling scheme which is motivated by applications in multi-channel deconvolution and multi-source separation has been investigated in many aspects. Common for most of results on MIMO systems is that the input signals are supposed to be band-limited. In this paper, we study vector sampling expansions on general finitely generated shift-invariant subspaces. Necessary and sufficient conditions for a vector sampling theorem to hold are given. We also give several examples to illustrate the main result.     

The Development of Appropriate Upscaling Procedures

Permeability upscaling should be carried out with careful attention to the nature of rock heterogeneities. While there are many large-scale features which must be taken into account, there are also important heterogeneities at the small-scale. Many sedimentary structures contain laminae at the mm–cm scale, and beds at the m-scale, which give rise to strong contrasts in permeability. We use a 2D model derived from a photo-panel of an aeolian outcrop, along with permeability measurements from a North Sea oil field, to demonstrate the effects of small-scale heterogeneity. This model is similar in size to a typical cell of a reservoir geological model. We take imaginary probe and core plug measurements from the model, average them, and compare these with the effective permeability for the model computed from a finite difference flow calculation. Although this procedure is standard practice, we show that it can lead to biased estimates of the permeabilities used in flow simulation. As an alternative we suggest using models of representative beds, and performing flow simulation to calculate effective permeabilities for both single-phase and two-phase flow.     

Measurement techniques for mercury species in ambient air

This review critically evaluates the measurement methodologies most commonly employed for the analysis of the various forms of mercury (Hg) in air. Emphasis is given to the three most common forms of mercury in air [i.e. gaseous elemental mercury (GEM, Hg⁰), reactive gaseous mercury (RGM), and particle-bound mercury (Hg_p)]. Moreover, we also briefly describe methods dealing with gas-phase analysis of organic mercury species (e.g., mostly methyl mercury), as they are also reported to be present in air on rare occasions. To begin with, we describe the approaches to sampling airborne mercury species and associated sample-treatment strategies. We evaluate both conventional and emerging alternative detection techniques for different mercury forms with respect to their applicability in airborne mercury analysis. We also discuss the artifacts and the biases associated with analysis of different mercury species. Finally, the review summarizes current methodological developments for the determination of mercury in air and highlights future prospects for improvements.     

Sampling and analytical framework for routine environmental monitoring of organic pollutants

Dubious data would lead to incorrect interpretations and consequently faulty conclusions. Environmental monitoring results therefore have to be unambiguous to avoid misunderstanding the problems under investigation. Representative sampling and appropriate laboratory procedures are keys to acquiring quality data in order to draw unbiased conclusions.Although a large number of studies on organic pollutants have been published, few efforts have been directed towards instituting a systematic framework from sampling design to instrumental analysis. Generally, the main components in such a framework should include sampling design, sample preparation, sample extraction, extract purification and fractionation, and quantification (including qualitative and quantitative analyses).This review outlines the sampling and analytical framework appropriate for routine monitoring of organic pollutants, particularly persistent organic pollutants widely occurring in the environment. We emphasize statistically-based sampling schemes and quality-assurance and quality-control measures desirable for environmental monitoring programs.By way of demonstrating their importance, we especially review procedures for collecting unconventional environmental samples (e.g., human blood, breast milk, human hair, fish and bird tissues, and ice and snow) and analytical methods for typical emerging organic chemicals.