In situ damage detection in frame structures through coupled response measurements

Due to the existence of global modes and local modes of the neighbouring members, damage detection on a structure is more challenging than damage on isolated beams. Detection of an artificial circumferential crack on a joint in a frame-like welded structure is studied in this paper using coupled response measurements. Similarity to real engineering structures is maintained in the fabrication of the test frame. Both the chords and the branch members have hollow sections and the branch members have smaller sizes. The crack is created by a hacksaw on a joint where a branch meets the chord. The methodology is first demonstrated on a single hollow section beam. The test results are then presented for the damaged and undamaged frame. The existence of the damage is clearly observable from the experimental results. It is suggested that this approach offers the-potential to detect damage in welded structures such as cranes, mining equipment, steel-frame bridges, naval and offshore structures. (C) 2003 Elsevier Ltd. All rights reserved.

Quantitative assessment of aortic sclerosis using ultrasonic backscatter

Background. The development of therapeutic interventions to prevent progressive valve damage is more likely to limit the progression of structural damage to the aortic valve with normal function (aortic sclerosis [ASC]) than clinically apparent aortic stenosis. Currently, the ability to appreciate the progression of ASC is compromised by the subjective and qualitative evaluation of sclerosis severity. Methods: We sought to reveal whether the intensity of ultrasonic backscatter could be used to quantify sclerosis severity in 26 patients with ASC and 23 healthy young adults. images of the aortic valve were obtained in the parasternal long-axis view and saved in raw data format. Six square-shaped 11 X 11 pixel regions of interest were placed on the anterior and posterior leaflets, and calibrated backscatter values were obtained by subtracting the regions of interest in the blood pool from the averaged backscatter values obtained from the leaflets. Results. Mean ultrasonic backscatter values for sclerotic valves exceeded the results in normal valve tissue (16.3 +/- 4.4 dB vs 9.8 +/- 3.3 dB, P < .0001). Backscatter values were greater (22.0 +/- 3.5 dB) in a group of 6 patients with aortic stenosis. Within the sclerosis group, the magnitude of backscatter was directly correlated (P < .05) with a subjective sclerosis score, and with transvalvular pressure gradient. mean reproducibility was 2.4 +/- 1.8 dB (SD) between observers, and 2.3 +/- 1.7 dB (SD) between examinations. Conclusion: Measurement of backscatter from the valve leaflets of patients with ASC may be a feasible means of following the progression and treatment response of aortic sclerosis.

Predictors of osteoarthritis(OA) improvement in patients treated with Hylan G-F for knee OA.

Aim of study: Different criteria for treatment response were explored to identify predictors of OA improvement. Analyses were based on data from a previously reported 1-year randomized controlled trial of appropriate care with or without hylan G-F 20 in patients with knee OA. Methods: Five definitions of ‘‘patient responder’’ from baseline to month 12 were examined: at least 20% reduction in WOMAC pain score; at least 20% reduction in WOMAC pain score and at least 20% reduction in either the WOMAC stiffness or function score; OARSI responder criteria (Propositions A and B) for intra-articular drugs; and OMERACT-OARSI responder criteria (Proposition D). As an a posteriori analysis, multivariable logistic regression models for each definition of patient responder were developed using a forward selection method. The following variables were defined prior to modeling and considered in the model along with two-way interactions: age (O65 years), BMI, gender, X-ray grade (0, I, II vs III, IV), co-morbidity (1 or 2 conditions vs 3 or more), duration of OA in study knee (years), previous surgery of study knee, hylan G-F 20 injection technique, WOMAC pain, stiffness and function, and treatment group. Results: Hylan G-F 20 was a predictor of improvement for all patient responder definitions P ! 0.001; odds of improvement were 2.7 or higher for patients in the hylan G-F 20 group compared to appropriate care without hylan G-F 20. For three of the five patient responder definitions, X-ray grade was a predictor of improvement (P ! 0.10; lower X-ray grade increased the odds of improvement). For four of the five patient responder definitions, duration of OA was a predictor of improvement (P ! 0.10; shorter duration of OA increased the odds of improvement). Conclusion: Analyses showed that appropriate care with hylan G-F 20 is the dominant predictor of patient improvement. While high grade structural damage does not preclude a response, patients who are targeted early in the disease process when less structural damage has occurred, may have a greater chance of improvement.

Structural basis and prediction of substrate specificity in protein serine/threonine kinases

The large number of protein kinases makes it impractical to determine their specificities and substrates experimentally. Using the available crystal structures, molecular modeling, and sequence analyses of kinases and substrates, we developed a set of rules governing the binding of a heptapeptide substrate motif (surrounding the phosphorylation site) to the kinase and implemented these rules in a web-interfaced program for automated prediction of optimal substrate peptides, taking only the amino acid sequence of a protein kinase as input. We show the utility of the method by analyzing yeast cell cycle control and DNA damage checkpoint pathways. Our method is the only available predictive method generally applicable for identifying possible substrate proteins for protein serine/threonine kinases and helps in silico construction of signaling pathways. The accuracy of prediction is comparable to the accuracy of data from systematic large-scale experimental approaches.

Damage by eutectic particle cracking in aluminum casting alloys A356/357

The strain dependence of particle cracking in aluminum alloys A356/357 in the T6 temper has been studied in a range of microstructures produced by varying solidification rate and Mg content, and by chemical (Sr) modification of the eutectic silicon. The damage accumulates linearly with the applied strain for all microstructures, but the rate depends on the secondary dendrite arm spacing and modification state. Large and elongated eutectic silicon particles in the unmodified alloys and large pi-phase (Al9FeMg3Si5) particles in alloy A357 show the greatest tendency to cracking. In alloy A356, cracking of eutectic silicon particles dominates the accumulation of damage while cracking of Fe-rich particles is relatively unimportant. However, in alloy A357, especially with Sr modification, cracking of the large pi-phase intermetallics accounts for the majority of damage at low and intermediate strains but becomes comparable with silicon particle cracking at large strains. Fracture occurs when the volume fraction of cracked particles (eutectic silicon and Fe-rich intermetallics combined) approximates 45 pct of the total particle volume fraction or when the number fraction of cracked particles is about 20 pct. The results are discussed in terms of Weibull statistics and existing models for dispersion hardening.

Structural, electrical, and optical analysis of ion implanted semi-insulating InP

Semi-insulating InP was implanted with MeV P, As, Ga, and In ions, and the resulting evolution of structural properties with increased annealing temperature was analyzed using double crystal x-ray diffractometry and cross sectional transmission electron microscopy. The types of damage identified are correlated with scanning spreading resistance and scanning capacitance measurements, as well as with previously measured Hall effect and time resolved photoluminescence results. We have identified multiple layers of conductivity in the samples which occur due to the nonuniform damage profile of a single implant. Our structural studies have shown that the amount and type of damage caused by implantation does not scale with implant ion atomic mass. (C) 2004 American Institute of Physics.

Lattice damage produced in GaN by swift heavy ions

Wurtzite GaN epilayers bombarded at 300 K with 200 MeV Au-197(16+) ions are studied by a combination of transmission electron microscopy (TEM) and Rutherford backscattering/channeling spectrometry (RBS/C). Results reveal the formation of near-continuous tracks propagating throughout the entire similar to1.5-mum-thick GaN film. These tracks, similar to100 Angstrom in diameter, exhibit a large degree of structural disordering but do not appear to be amorphous. Throughout the bombarded epilayer, high-resolution TEM reveals planar defects which are parallel to the basal plane of the GaN film. The gross level of lattice disorder, as measured by RBS/C, gradually increases with increasing ion fluence up to similar to10(13) cm(-2). For larger fluences, delamination of the nitride film from the sapphire substrate occurs. Based on these results, physical mechanisms of the formation of lattice disorder in GaN in such a high electronic stopping power regime are discussed. (C) 2004 American Institute of Physics.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

The pathophysiology of 'brain shrinkage' in alcoholics - Structural and molecular changes and clinical implications

This article represents a symposium of the 2004 ISBRA Congress held in Mannheim. The presentations were: Review of the neuropathological and neurochemical changes seen in alcohol-related ' brain shrinkage ' by Clive Harper; In Vivo Detection of Macrostructural and Microstructural Markers of Brain Integrity in Human Alcoholism and a Rodent Model of Alcoholism by Adolf Pfefferbaum, Elfar Adalsteinsson and Edith Sullivan; Gene and Protein Changes in the Brains of Alcoholics with ' Brain Shrinkage ' by Joanne Lewohl and Peter Dodd; Cross sectional and longitudinal MR spectroscopy studies of chronic adult alcoholics by Michael Taylor; Brain Atrophy Associated with Impairment on a Simulated Gambling Task in Long-Term Abstinent Alcoholics by George Fein and Bennett Landman.

Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

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.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.