A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.

A demonstration of artificial dissipation effects in some finite volume solution methods for the Navier-Stokes equations

The artificial dissipation effects in some solutions obtained with a Navier-Stokes flow solver are demonstrated. The solvers were used to calculate the flow of an artificially dissipative fluid, which is a fluid having dissipative properties which arise entirely from the solution method itself. This was done by setting the viscosity and heat conduction coefficients in the Navier-Stokes solvers to zero everywhere inside the flow, while at the same time applying the usual no-slip and thermal conducting boundary conditions at solid boundaries. An artificially dissipative flow solution is found where the dissipation depends entirely on the solver itself. If the difference between the solutions obtained with the viscosity and thermal conductivity set to zero and their correct values is small, it is clear that the artificial dissipation is dominating and the solutions are unreliable.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Rheological behaviour of the mushy zone and its effect on the formation of casting defects during solidification

A new conceptual framework has been developed which explains the formation of shear-related casting defects such as porosity, segregation and tears. The theory relates defect formation to the mechanical behaviour of the partially solidified microstructure when shear stresses are developed during the filling of a casting and by the subsequent feeding processes during solidification. Two transition points, the dendrite coherency point and the maximum packing solid fraction, divide the mushy zone into three regions of different mechanical and feeding behaviours. The response of the mush to shear is related to the presence of these zones during solidification of a casting. The resulting defects are rationalized by considering the governing local shear stress and shear rate, local strength and time available for fluid flow. The design of the casting, the casting process used and the alloy composition all influence the relative importance of shearing on defect formation. (C) 1998 Acta Metallurgica Inc. Published by Elsevier Science Ltd. All rights reserved.

Indicators used to measure the innovation process: defects and possible remedies

Some diverse indicators used to measure the innovation process are considered, They include those with art aggregate, and often national, focus, and rely on data from scientific publications, patents and R&D expenditures, etc. Others have a firm-level perspective, relying primarily on surveys or case studies. Also included are indicators derived from specialized databases, or consensual agreements reached through foresight exercises. There is an obvious need for greater integration of the various approaches to capture move effectively the richness of available data and better reflect the reality of innovation. The focus for such integration could be in the area of technology strategy, which integrates the diverse scientific, technological, and innovation activities of firms within their operating environments; improved capacity to measure it has implications for policy-makers, managers and researchers.

Operational regimes for a simplified one-step artificial chaperone refolding method

The artificial chaperone method for protein refolding developed by Rozema et al. (Rozema, D.; Gellman, S. H. J. Am. Chem. Soc. 1995, 117 (8), 2373-2374) involves the sequential dilution of denatured protein into a buffer containing detergent (cetyltrimethylammonium bromide, CTAB) and then into a refolding buffer containing cyclodextrin WD). In this paper a simplified one-step artificial chaperone method is reported, whereby CTAB is added directly to the denatured solution, which is then diluted directly into a refolding buffer containing P-cyclodextrin (P-CD). This new method can be applied at high protein concentrations, resulting in smaller processing volumes and a more concentrated protein solution following refolding. The increase in achievable protein concentration results from the enhanced solubility of CTAB at elevated temperatures in concentrated denaturant. The refolding yields obtained for the new method were significantly higher than for control experiments lacking additives and were comparable to the yields obtained with the classical two-step approach. A study of the effect of beta-CD and CTAB concentrations on refolding yield suggested two operational regimes: slow stripping ( beta-CDXTABsimilar to1), most suited for higher protein concentrations, and fast stripping (beta-CD/CTABsimilar to2.7), best suited for lower protein concentrations. An increased chaotrope concentration resulted in higher refolding yields and an enlarged operational regime.

A bacterial artificial chromosome library of Lotus japonicus constructed in an Agrobacterium tumefaciens-transformable vector

We constructed a BAC library of the model legume Lotus japonicus with a 6-to 7-fold genome coverage. We used vector PCLD04541, which allows direct plant transformation by BACs. The average insert size is 94 kb. Clones were stable in Escherichia coli and Agrobacterium tumefaciens.