Experimental Characterization of Ultrasonic Phenomena by a Neural-Like Learning System

This paper describes a novel approach for analyzing ultrasonic signals to permit an experimental determination of the relations between elastic wave phenomena and the properties of a source of sound in a material. It is demonstrated that an adaptive learning system comprising an associative memory can be used to map source and waveform data and vice versa with the auto- and cross-correlation portions of the associative memory. Experiments are described which utilize such an adaptive system, running on a laboratory minicomputer, to process the data from a transient ultrasonic pulse in a plate specimen. In the learning procedure, the system learns from experimental pattern vectors, which are formed from the ultrasonic waveforms and, in this paper, encoded information about the source. The source characteristics are recovered by the recall procedure from detected ultrasonic signals and vice versa. Furthermore, from the discrepancy between the presented and the learned signals, the changes in the wave phenomenon, corresponding, for example, to changes in the boundary conditions of a specimen, can be determined

Novel Approaches for the Ultrasonic NDE of Thick and other Composites

This paper summarizes several recent developments which are facilitating new approaches for both active and passive quantitative ultrasonic measurements in composite materials. These include the development of point sources and point receivers, a theory for analyzing the propagation of transient elastic waves through a bounded, dispersive and attenuative medium, and the development and implementation of appropriate signal processing algorithms. An alternative to these deterministic approaches is a processing scheme based on a simulated intelligent system which processes the signals like a neural network. Examples of applications of these ideas to the NDE of composite materials are shown

Generation of folk song melodies using Bayes transforms

The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models

Dynamic clustering of time series with Echo State Networks

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Treatment of Aneurysmal Bone Cysts with Bioactive Glass in Children

Background and Aims: Aneurysmal bone cysts represent about 1% of primary bone tumors. The standard treatment is curettage, followed by local adjuvant treatments and bone grafting. The problem is the high recurrence rate. The purpose of this study was to evaluate retrospectively the use of bioactive glass as a filling material in the treatment of aneurysmatic bone cysts in children. Material and Methods: A total of 18 consecutive children (mean 11.3years at surgery; 10 males; 11 lower, 6 upper limb, 1 pelvis; 15 with primary surgery) with histologically proven primary aneurysmal bone cysts operated with curettage and bioactive glass filling between 2008 and 2013 were evaluated after a mean follow-up of 2.0years (range, 0.7-5.1years). Results: Two (11%) patients showed evidence of aneurysmal bone cyst recurrence and both have been re-operated for recurrence. Bone remodeling was noted in all patients with remaining growth and no growth plate disturbances were recorded. Two patients needed allogeneic blood transfusion. No intraoperative or postoperative complications were recorded. Conclusion: We conclude that bioactive glass is a suitable filling material for children with primary aneurysmal bone cyst. Bioactive glass did not affect bone growth and no side effects were reported.Peer reviewe

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models

<p>Abstract</p> <p>Background</p> <p>Standard approaches to address the performance of predictive models that used common statistical measurements for the entire data set provide an overview of the average performance of the models across the entire predictive space, but give little insight into applicability of the model across the prediction space. Guha and Van Drie recently proposed the use of structure-activity landscape index (SALI) curves via the SALI curve integral (SCI) as a means to map the predictive power of computational models within the predictive space. This approach evaluates model performance by assessing the accuracy of pairwise predictions, comparing compound pairs in a manner similar to that done by medicinal chemists.</p> <p>Results</p> <p>The SALI approach was used to evaluate the performance of continuous prediction models for MDR1-MDCK <it>in vitro </it>efflux potential. Efflux models were built with ADMET Predictor neural net, support vector machine, kernel partial least squares, and multiple linear regression engines, as well as SIMCA-P+ partial least squares, and random forest from Pipeline Pilot as implemented by AstraZeneca, using molecular descriptors from <it>SimulationsPlus </it>and AstraZeneca.</p> <p>Conclusion</p> <p>The results indicate that the choice of training sets used to build the prediction models is of great importance in the resulting model quality and that the SCI values calculated for these models were very similar to their Kendall τ values, leading to our suggestion of an approach to use this SALI/SCI paradigm to evaluate predictive model performance that will allow more informed decisions regarding model utility. The use of SALI graphs and curves provides an additional level of quality assessment for predictive models.</p

Dark matter in early-type galaxies: dynamical modelling of IC1459, IC3370, NGC3379 and NGC4105

We analyse long-slit spectra of four early-type galaxies which extend from ~1 to ~3 effective radii: IC1459, IC3370, NGC3379 and NGC4105. We have extracted the full line-of-sight velocity distribution (in the case of NGC3379 we also used data from the literature) which we model using the two-integral approach. Using two-integral modelling we find no strong evidence for dark haloes, but the fits suggest that three-integral modelling is necessary. We also find that the inferred constant mass-to-light ratio in all four cases is typical for early-type galaxies. Finally, we also discuss the constraints on the mass-to-light ratio which can be obtained using X-ray haloes in the case of IC1459, NGC3379 and NGC4105 and compare the estimated values with the predictions from the dynamical modelling.Comment: 42 pages, 18 figures, accepted for publication in MNRA