Exact solution for the Bariev model with boundary fields

The Bariev model with open boundary conditions is introduced and analysed in detail in the framework of the Quantum Inverse Scattering Method. Two classes of independent boundary reflecting K-matrices leading to four different types of boundary fields are obtained by solving the reflection equations. The models are exactly solved by means of the algebraic nested Bethe ansatz method and the four sets or Bethe ansatz equations as well as their corresponding energy expressions are derived. (C) 2001 Elsevier Science B.V. All rights reserved.

Integrable open boundary conditions for the Bariev model of three coupled X Y spin chains

The integrable open-boundary conditions for the Bariev model of three coupled one-dimensional XY spin chains are studied in the framework of the boundary quantum inverse scattering method. Three kinds of diagonal boundary K-matrices leading to nine classes of possible choices of boundary fields are found and the corresponding integrable boundary terms are presented explicitly. The boundary Hamiltonian is solved by using the coordinate Bethe ansatz technique and the Bethe ansatz equations are derived. (C) 2001 Elsevier Science B.V. All rights reserved.

The expression of plasminogen activator system in a rat model of periodontal wound healing

Background: The plasminogen activator system has been proposed to play a role in proteolytic degradation of extracellular matrices in tissue remodeling, including wound healing. The aim of this study was to elucidate the presence of components of the plasminogen activator system during different stages of periodontal wound healing. Methods: Periodontal wounds were created around the molars of adult rats and healing was followed for 28 days. Immunohistochemical analyses of the healing tissues and an analysis of the periodontal wound healing fluid by ELISA were carried out for the detection of tissue-type plasminogen activator (t-PA), urokinase-type plasminogen activator (u-PA), and 2 plasminogen activator inhibitors (PAI-1 and PAI-2). Results: During the early stages (days 1 to 3) of periodontal wound healing, PAI-1 and PAI-2 were found to be closely associated with the deposition of a fibrin clot in the gingival sulcus. These components were strongly associated with the infiltrating inflammatory cells around the fibrin clot. During days 3 to 7, u-PA, PAI-1, and PAI-2 were associated with cells (particularly monocytes/macrophages, fibroblasts, and endothelial cells) in the newly formed granulation tissue. During days 7 to 14, a new attachment apparatus was formed during which PAI-1, PAI-2, and u-PA were localized in both periodontal ligament fibroblasts (PDL) and epithelial cells at sites where these cells were attaching to the root surface. In the periodontal wound healing fluid, the concentration for t-PA increased and peaked during the first week. PAI-2 had a similar expression to t-PA, but at a lower level over the entire wound-healing period. Conclusions: These findings indicate that the plasminogen activator system is involved in the entire process of periodontal wound healing, in particular with the formation of fibrin matrix on the root surface and its replacement by granulation tissue, as well as the subsequent formation of the attachment of soft tissue to the root surface during the later stages of wound repair.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

An identifiability analysis of a parent-metabolite pharmacokinetic model for ivabradine.

The paper considers the structural identifiability of a parent–metabolite pharmacokinetic model for ivabradine and one of its metabolites. The model, which is linear, is considered initially for intravenous administration of ivabradine, and then for a combined intravenous and oral administration. In both cases, the model is shown to be unidentifiable. Simplification of the model (for both forms of administration) to that proposed by Duffull et al. (1) results in a globally structurally identifiable model. The analysis could be applied to the modeling of any drug undergoing first-pass metabolism, with plasma concentrations available from drug and metabolite.

Fitting a mixture model to three-mode three-way data with missing information

When the data consist of certain attributes measured on the same set of items in different situations, they would be described as a three-mode three-way array. A mixture likelihood approach can be implemented to cluster the items (i.e., one of the modes) on the basis of both of the other modes simultaneously (i.e,, the attributes measured in different situations). In this paper, it is shown that this approach can be extended to handle three-mode three-way arrays where some of the data values are missing at random in the sense of Little and Rubin (1987). The methodology is illustrated by clustering the genotypes in a three-way soybean data set where various attributes were measured on genotypes grown in several environments.