Homogenization of diffuse delamination in composite laminates

Diffuse delamination induced by transverse cracking is usually the secondary damage mode when a composite laminate experiences tensile loading. The fist damage mechanism in such a laminate is transverse cracking which has been widely investigated with both analytical methods and " mechanism-based" constitutive laws. Delamination induced by matrix cracking is already studied extensively by analytical approaches, however, a proper homogenization way has not been proposed yet. In this paper, a modification to an available cohesive constitutive law is proposed which is capable of considering the effect of diffuse delamination without the necessity of consideration of an actual discontinuity between the layers. The proposed constitutive law is then compared against its equivalent models containing interlaminar discontinuity and it is shown that the obtained results from both models are in good. Then the proposed modification is used in Double Cantilever Beam (DCB) specimen and the obtained results are found coincident with the equivalent model with diffuse discontinuities at the interface. Finally, a damaged cross-ply laminate is modeled under the boundary conditions of tensile loading and also 3-point bending with and without the proposed cohesive modification. In tensile loading, the results of both cases are similar; however, it is shown that in bending, the unmodified cohesive law predicts the lateral stiffness larger than the proposed modification. The lateral stiffness of the equivalent model with discontinuities as crack indicates that the proposed modification is able to properly consider the lateral stiffness decrease

The Colonization Strategies of Nontypeable Haemophilus influenzae - Bacterial Colonization Factors and Vaccine Development

Nontypeable Haemophilus influenzae (NTHi) is a remarkably proficient colonizer of the human respiratory tract. The aim of this thesis has been to characterize currently known, and identify novel, bacterial factors involved in the key processes of colonization and pathogenesis, namely adherence to host tissue and evasion of the host innate immunity. We have shown that the NTHi virulence factor Protein E mediates innate immune evasion by interacting with the C-terminus of host complement down-regulatory protein vitronectin. This interaction delayed the formation of the membrane attack complex on the bacterial surface and increased the bacterial serum-resistance. Furthermore, we identified a novel surface-exposed virulence factor designated Haemophilus Protein F that also sequesters vitronectin on the surface of the bacteria, thus rendering the pathogen more serum-resistant. Moreover, we have revealed that Protein F mediates bacterial adherence to primary human bronchial cells and to the extracellular matrix protein laminin. Isogenic mutants devoid of Protein F were observed to adhere significantly less to vitronectin, epithelial cells and laminin. These interactions were characterized at the molecular level, and the N-terminus of Protein F was shown to contain the host-interacting region. Finally, we showed that immunization with Protein F conferred increased pulmonary clearance of NTHi in vivo. In summary, we have identified, and at the molecular level characterized, novel virulence factors that may be suitable for future vaccine development

Applications of Chemometrics‐Assisted Voltammetric Analysis

Electroanalytical techniques consist of the interplay between electricity and chemistry, namely the measurement of electrical quantities, such as charge, current or potential and their relationship to chemical parameters. Electrical measurements for analytical purposes have found a lot of applications including industry quality control, environmental monitoring and biomedical analysis. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments and to provide maximum chemical information by analysing chemical data. The use of chemometrics in electroanalytical chemistry is not as popular as in spectroscopy, although recently, applications of these methods for mathematical resolution of overlapping signals, calibration and model identification have been increasing. The electroanalytical methods will be improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. This chapter focuses on applications of first-, second- and third-order multivariate calibration coupled with voltammetric data for quantitative purposes and has been written from both electrochemical and chemometrical points of view with the aim of providing useful information for the electrochemists to promote the use of chemometrics in electroanalytical chemistry

DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Automatic Quality Estimation for ASR System Combination

Recognizer Output Voting Error Reduction (ROVER) has been widely used for system combination in automatic speech recognition (ASR). In order to select the most appropriate words to insert at each position in the output transcriptions, some ROVER extensions rely on critical information such as confidence scores and other ASR decoder features. This information, which is not always available, highly depends on the decoding process and sometimes tends to over estimate the real quality of the recognized words. In this paper we propose a novel variant of ROVER that takes advantage of ASR quality estimation (QE) for ranking the transcriptions at "segment level" instead of: i) relying on confidence scores, or ii) feeding ROVER with randomly ordered hypotheses. We first introduce an effective set of features to compensate for the absence of ASR decoder information. Then, we apply QE techniques to perform accurate hypothesis ranking at segment-level before starting the fusion process. The evaluation is carried out on two different tasks, in which we respectively combine hypotheses coming from independent ASR systems and multi-microphone recordings. In both tasks, it is assumed that the ASR decoder information is not available. The proposed approach significantly outperforms standard ROVER and it is competitive with two strong oracles that e xploit prior knowledge about the real quality of the hypotheses to be combined. Compared to standard ROVER, the abs olute WER improvements in the two evaluation scenarios range from 0.5% to 7.3%

On the application of reservoir computing networks for noisy image recognition

Reservoir Computing Networks (RCNs) are a special type of single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCNs. Evaluation in the domain of noise robust speech recognition proved that this method was effective. The aim of this work is to extend that study to the field of image processing, by showing that the proposed parameter tuning procedure is equally valid in the field of image processing and conforming that RCNs are apt at temporal modeling and are robust with respect to noise. In particular, we investigate the potential of RCNs in achieving competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise. (c) 2017 Elsevier B.V. All rights reserved