Determination of Lead in Human Hair by High Resolution Continuum Source Graphite Furnace Atomic Absorption Spectrometry with Microwave Digestion and Solid Sampling

The content of lead in human hair was measured by high resolution continuum source graphite furnace atomic absorption spectrometry (HR-CS GFAAS) combined with microwave-assisted digestion (MAD) and direct solid sampling (DSS). Hair strands were washed, dried, and then cut into three parts (root, middle portion, and tip). For MAD-GFAAS assays, approximately 0.25 g of hair was completely digested using a mixture of concentrated nitric acid and hydrogen peroxide in a closed system. In the DSS-GFAAS assays, 0.1–0.2 mg of dried hair was directly introduced into a graphite furnace using a solid autosampler. The temperature programs were optimized and the effects of various added modifiers were compared. The results indicated that NH₄H₂PO₄ was the optimal modifier for analysis of Pb using GFAAS. Use of the optimal modifier and temperature program gave similar limits of detection for MAD-GFAAS and DSS-GFAAS of 1.16 ng/g and 0.82 ng/g, respectively. Both methods also produced satisfactory recoveries ranging from 98.69% to 103.14%. There was no significant difference observed between the Pb contents of hair strands determined by the MAD-GFAAS and DSS-GFAAS assays, which both indicated that the Pb levels increased along the hair strands. Comparison of the two methods revealed that DSS-GFAAS had several advantageous characteristics over MAD-GFAAS, including the need for much less sample material and having a less time-consuming procedure, lower sample blank absorbance, lower memory effect, and no risk of environmental pollution by digesting chemicals. The direct solid sampling technique can be employed as a good alternative to conventional wet digestion in AAS assays.     

The Kumar-Becker-Lin scheme revisited

The Kumar-Becker-Lin scheme introduces a slowly vanishing cost bias in the parameter estimation part of self-tuning control in order to improve its performance. This paper establishes the a.s. optimality of a variant of this scheme for Markov chains on a countable state space when the action space is compact metric and the parameter space is a compact subset ofR ^m .     

Recovery of Band-Limited Functions on Locally Compact Abelian Groups from Irregular Samples

Using the techniques of approximation and factorization of convolution operators we study the problem of irregular sampling of band-limited functions on a locally compact Abelian group G. The results of this paper relate to earlier work by Feichtinger and Gröchenig in a similar way as Kluvánek's work published in 1969 relates to the classical Shannon Sampling Theorem. Generally speaking we claim that reconstruction is possible as long as there is sufficient high sampling density. Moreover, the iterative reconstruction algorithms apply simultaneously to families of Banach spaces.     

On the Solution of Discontinuous IVPs by Adaptive Runge–Kutta Codes

In this paper an automatic technique for handling discontinuous IVPs when they are solved by means of adaptive Runge–Kutta codes is proposed. This technique detects, accurately locates and passes the discontinuities in the solution of IVPs by using the information generated by the code along the numerical integration together with a continuous interpolant of the discrete solution. A remarkable feature is that it does not require additional information on the location of the discontinuities. Some numerical experiments are presented to illustrate the reliability and efficiency of the proposed algorithms.     

Localized Frames Are Finite Unions of Riesz Sequences

It is shown that every localized frame is a finite union of Riesz sequences. This is a partial solution to a question of Feichtinger.     

Technically optimal inspection policy with arithmetical adaption

A process is considered whose quality deteriorates accordingto a constant failure intensity . As in practice it can be difficultto estimate the true value of the purpose of this paper isto present a strategy which can be applied without knowing .In order to maximize the number of conforming items per timeunit perfect inspections and renewals are performed. The lengthof the inspection interval is described by an arithmetical sequenceand changes by the time depending on perceived assignable causes.Optimal adaptive control plans provide nearly the same performanceas in the case when is known.     

Extremes of deterministic sub‐sampled moving averages with heavy‐tailed innovations

Let {X_k}_k?1 be a strictly stationary time series. For a strictly increasing sampling function g:?→? define Y_k=X_g(k) as the deterministic sub‐sampled time series. In this paper, the extreme value theory of {Y_k} is studied when X_k has representation as a moving average driven by heavy‐tailed innovations. Under mild conditions, convergence results for a sequence of point processes based on {Y_k} are proved and extremal properties of the deterministic sub‐sampled time series are derived. In particular, we obtain the limiting distribution of the maximum and the corresponding extremal index. Copyright © 2003 John Wiley & Sons, Ltd.     

Queueing Dynamics and Maximal Throughput Scheduling in Switched Processing Systems

We study a processing system comprised of parallel queues, whose individual service rates are specified by a global service mode (configuration). The issue is how to switch the system between various possible service modes, so as to maximize its throughput and maintain stability under the most workload-intensive input traffic traces (arrival processes). Stability preserves the job inflow–outflow balance at each queue on the traffic traces. Two key families of service policies are shown to maximize throughput, under the mild condition that traffic traces have long-term average workload rates. In the first family of cone policies, the service mode is chosen based on the system backlog state belonging to a corresponding cone. Two distinct policy classes of that nature are investigated, MaxProduct and FastEmpty. In the second family of batch policies (BatchAdapt), jobs are collectively scheduled over adaptively chosen horizons, according to an asymptotically optimal, robust schedule. The issues of nonpreemptive job processing and non-negligible switching times between service modes are addressed. The analysis is extended to cover feed-forward networks of such processing systems/nodes. The approach taken unifies and generalizes prior studies, by developing a general trace-based modeling framework (sample-path approach) for addressing the queueing stability problem. It treats the queueing structure as a deterministic dynamical system and analyzes directly its evolution trajectories. It does not require any probabilistic superstructure, which is typically used in previous approaches. Probability can be superposed later to address finer performance questions (e.g., delay). The throughput maximization problem is seen to be primarily of structural nature. The developed methodology appears to have broader applicability to other queueing systems.     

Edgeworth Expansions for Studentized Versions of Symmetric Finite Population Statistics

We show the validity of the one-term Edgeworth expansion for Studentized asymptotically linear statistics based on samples drawn without replacement from finite populations. Replacing the moments defining the expansion by their estimators, we obtain an empirical Edgeworth expansion. We show the validity of the empirical Edgeworth expansion in probability.     

Speeding up the FMMR perfect sampling algorithm: A case study revisited

In a previous paper by the second author, two Markov chain Monte Carlo perfect sampling algorithms—one called coupling from the past (CFTP) and the other (FMMR) based on rejection sampling—are compared using as a case study the move‐to‐front (MTF) self‐organizing list chain. Here we revisit that case study and, in particular, exploit the dependence of FMMR on the user‐chosen initial state. We give a stochastic monotonicity result for the running time of FMMR applied to MTF and thus identify the initial state that gives the stochastically smallest running time; by contrast, the initial state used in the previous study gives the stochastically largest running time. By changing from worst choice to best choice of initial state we achieve remarkable speedup of FMMR for MTF; for example, we reduce the running time (as measured in Markov chain steps) from exponential in the length n of the list nearly down to n when the items in the list are requested according to a geometric distribution. For this same example, the running time for CFTP grows exponentially in n. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 2003