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A magnetic solid-phase extraction method (MSPE) was developed to pre-concentrate and cleanup clenbuterol (CLE) from pork muscle. Novel sulfonated polystyrene magnetic nanobeads (spMNBs) were synthesized via a one-pot emulsion copolymerization method by using divinylbenzene, styrene, and sodium styrene sulfonate in the presence of oleic acid-modified and 10-undecylenic acid-modified magnetic ferrofluid. The resulting spMNBs exhibited high adsorption efficiency for CLE and for 10 other common beta-adrenergic agonists, namely, brombuterol, ractopamine, tulobuterol, bambuterol, cimbuterol, mabuterol, clorprenaline, penbutolol, salbutamol, and cimaterol. The adsorption behavior of the spMNBs for CLE was described by the Langmuir equation with a maximum adsorption capacity of 0.41 mg/g. Under the optimized parameters, the extraction of CLE from 0.5 g of pork muscle required 25 mg of the spMNBs at a shortened adsorption time (0.5 min). The proposed MSPE was coupled with colloidal gold nanoparticle-based immunochromatographic assay (MSPE-AuNPIA) for the quantitative detection of CLE residue in pork muscle. The limit of detection and limit of quantification for the pork muscle were 0.10 and 0.24 ng/g, respectively. The intra-day and inter-day assay recoveries at three CLE spiked concentrations ranged from 92.5% to 98.1%, with relative standard deviations ranging from 3.2% to 13.0%. The results of MSPE-AuNPIA were confirmed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The CLE values obtained with MSPE-AuNPIA agreed with those obtained with LC-MS/MS. [ABSTRACT FROM AUTHOR]

The Thick-Restart Lanczos (TRLan) method is an effective method for solving large-scale Hermitian eigenvalue problems. The performance of the method strongly depends on the dimension of the projection subspace used at each restart. In this article, we propose an objective function to quantify the effectiveness of the selection of subspace dimension, and then introduce an adaptive scheme to dynamically select the dimension to optimize the performance. We have developed an open-source software package a-TRLan to include this adaptive scheme in the TRLan method. When applied to calculate the electronic structure of quantum dots, a-TRLan runs up to 2.3x faster than a state-of-the-art preconditioned conjugate gradient eigensolver. [ABSTRACT FROM AUTHOR]

Proposes an alternative orthogonalization procedure that computes the orthonormal basis from the right singular vectors of a matrix. Matrix-matrix multiplication operations; Synchronization point in parallel implementations.

We describe a Krylov subspace technique, based on incomplete orthogonalization of the Krylov vectors, which can be considered as a truncated version of OMRES. Unlike GMRES(m), the restarted version of GMRES, the new method does not require restarting. Like OMRES, it does not break down. Numerical experiments show that DQGMRES(k) often performs as well as the restarted GMRES using a subspace of dimension m = 2k. in addition, the algorithm is flexible to variable preconditioning, i.e., it can accommodate variations in the preconditioner at every step. In particular, this feature allows the use of any iterative solver as a right-preconditioner for DOGMRES(k). This inner-outer iterative combination often results in a robust approach for solving indefinite non-Hermitian linear systems. [ABSTRACT FROM AUTHOR]

The Davidson method is a popular preconditioned variant of the Arnoldi method for solving large eigenvalue problems. For theoretical as well as practical reasons the two methods are often used with restarting. Frequently, information is saved through approximated eigenvectors to compensate for the convergence impairment caused by restarting. We call this scheme of retaining more eigenvectors than needed "thick restarting" and prove that thick restarted, nonpreconditioned Davidson is equivalent to the implicitly restarted Arnoldi. We also establish a relation between thick restarted Davidson and a Davidson method applied on a deflated system. The theory is used to address the question of which and how many eigenvectors to retain and motivates the development of a dynamic thick restarting scheme for the symmetric case, which can be used in both Davidson and implicit restarted Arnoldi. Several experiments demonstrate the efficiency and robustness of the scheme. [ABSTRACT FROM AUTHOR]