Measuring equipment and test bench to control evolution of acoustic-deformation and heat fields induced in solids under failure by fluids

The authors have developed a procedure and a test bench for studying evolution of various nature physical fields in modeling geomedium fracture by fluids. The test bench performs synchronous recording of macro- and micro-deformation, heat and acoustic emission induced in physical models of geomedium under loading to discontinuity. The experimental procedure has been trialed. The analysis of the synchronized test data allows a conclusion on the existence of time–space relationship between different nature physical fields induced during failure of solids.     

Determining hearing threshold from brain stem evoked potentials.Optimizing a neural network to improve classification performance

Feed-forward neural networks in conjunction with back-propagation are an effective tool to automate the classification of biomedical signals. Most of the neural network research to date has been done with a view to accelerate learning speed. In the medical context, however, generalisation may be more important than learning speed. With the brain stem auditory evoked potential classification task described in this study, the authors found that parameter values that gave fastest learning could result in poor generalisation. In order to achieve maximum generalisation, it was necessary to fine tune the neural net for gain, momentum, batch size, and hidden layer size. Although this maximization could be time consuming, especially with larger training sets, the authors' results suggest that fine tuning parameters can have important clinical consequences, which justifies the time involved. In the authors' case, fine tuning parameters for high generalisation had the additional effect of reducing false negative classifications, with only a small sacrifice in learning speed     

The effect of Pseudomonas aeruginosa infection on clinical parameters in steady-state bronchiectasis

STUDY OBJECTIVE: To investigate the effect of Pseudomonas aeruginosa infection on clinical parameters in Chinese patients with noncystic fibrosis and steady-state bronchiectasis. DESIGN: Prospective, cross-sectional clinicomicrobiological study with informed consent. SETTING: Consecutive outpatient recruitment from a specialist bronchiectasis respiratory clinic. PATIENTS: Outpatients (n = 100; 62 women; 55.1+/-16.7 years old; FEV1/FVC 1.4+/-0.7/2.1+/-0.9 L), who had stable respiratory symptoms for more than 3 weeks. MEASUREMENTS AND RESULTS: Respiratory pathogens isolated from the sputum were: Pseudomonas aeruginosa (33), Haemophilus influenzae (10), Moraxella catarrhalis (2), other Gram-negative bacilli (5), Streptococcus pneumoniae (6), Staphylococcus aureus (5), mycobacteria (3), and yeast (1). Clinical parameters in patients with positive isolation of P aeruginosa were compared with those without the organism in the sputum culture (non-P aeruginosa). In the P aeruginosa group, the FEV1/FVC ratio and sputum volume were lower (p < 0.005) and higher (p < 0.0001), respectively, than those of the non-P aeruginosa group. The FEV1/FVC ratio (< 60%) and sputum volume (grading > 5) were independently associated with a positive sputum isolation of P aeruginosa with odds ratios of 3.1 (confidence interval [CI] 1.2 to 8.4; p < 0.01) and 4.7 (CI 1.6 to 13.3; p < 0.001), respectively. CONCLUSIONS: P aeruginosa is the predominant respiratory pathogen isolated in the sputum of Chinese patients with steady-state bronchiectasis, and its isolation is associated with high sputum output (> or = 75th quartile) and moderately severe airflow obstruction (FEV1/FVC < 60%).     

Structural stability of single-layered LaNi4.25Al0.75 film and its electrochemical hydrogen-storage properties

AB5-based hydrogen storage thin fdms （LaNi4.25Al0.75）, deposited on Cu substrate by dc magnetron sputtering were investigated in this study. X-ray diffraction （XRD） revealed that the microstructure of the layer was in crystal form. SEM and AFM analyses proved that the film appeared to be rather rough with numerous randomly sized pores of approximately 15-40 in nm diameter. Structural stability of the film was examined by the combined analyses of DSC, XRD, and SEM, which indicated that this film maintained its structural stability below 500 K or so, and a network structure was observed on the film after being heated at 700 K for 30 min. Electrochemical hydrogen-storage properties of the films were investigated by simulated battery tests. It was found that single-layered LaNi4.25A10.75 film exhibited electrochemical hydrogen-storage properties similar to typical AB5 alloys in bulk, and the maximum discharge capacity of the film was about 220 mAh/g. After 20 charge/discharge cycles, small needle-shaped aluminium oxide was formed on some fractions of the film surface.     

Scale effect in high-rate (spall) failure

Experimental results on the spall strength of copper in which the scale of the system was changed by a factor of ten show that the scale effect for high-rate one-dimensional strain depends on energy. The spall energy per unit surface area for failure increases with time. Arzamas-16. Translated from Fizika Goreniya i Vzryva, Vol. 29, No. 6, pp. 88–93, November–December, 1993.     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

Service bid comparisons by fuzzy ranking in open railway market timetabling

In an open railway access market, the Infrastructure Provider (IP), upon the receipts of service bids from the Train Service Providers (TSPs), assigns track access rights according to its own business objectives and the merits of the bids; and produces the train service timetable through negotiations. In practice, IP chooses to negotiate with the TSPs one by one in such a sequence that IP optimizes its objectives. The TSP bids are usually very complicated, containing a large number of parameters in different natures. It is a difficult task even for an expert to give a priority sequence for negotiations from the contents of the bids. This study proposes the application of fuzzy ranking method to compare and prioritize the TSP bids in order to produce a negotiation sequence. The results of this study allow investigations on the behaviors of the stakeholders in bid preparation and negotiation, as well as evaluation of service quality in the open railway market.     

A mixture of generalized latent variable models for mixed mode and heterogeneous data

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented.