An intelligent assistant for design and material engineers in the submarine cable industry

This paper describes an industrial application of case-based reasoning in engineering. The application involves an integration of case-based reasoning (CBR) retrieval techniques with a relational database. The database is specially designed as a repository of experiential knowledge and with the CBR application in mind such as to include qualitative search indices. The application is for an intelligent assistant for design and material engineers in the submarine cable industry. The system consists of three components; a material classifier and a database of experiential knowledge and a CBR system is used to retrieve similar past cases based on component descriptions. Work has shown that an uncommon retrieval technique, hierarchical searching, well represents several search indices and that this techniques aids the implementation of advanced techniques such as context sensitive weights. The system is currently undergoing user testing at the Alcatel Submarine Cables site in Greenwich. Plans are for wider testing and deployment over several sites internationally.

Identifying the damage mechanisms in paper using the acoustic emission monitoring technique

This paper investigates the use of the acoustic emission (AE) monitoring technique for use in identifying the damage mechanisms present in paper associated with its production process. The microscopic structure of paper consists of a random mesh of paper fibres connected by hydrogen bonds. This implies the existence of two damage mechanisms, the failure of a fibre-fibre bond and the failure of a fibre. This paper describes a hybrid mathematical model which couples the mechanics of the mass-spring model to the acoustic wave propagation model for use in generating the acoustic signal emitted by complex structures of paper fibres under strain. The derivation of the mass-spring model can be found in [1,2], with details of the acoustic wave equation found in [3,4]. The numerical implementation of the vibro-acoustic model is discussed in detail with particular emphasis on the damping present in the numerical model. The hybrid model uses an implicit solver which intrinsically introduces artificial damping to the solution. The artificial damping is shown to affect the frequency response of the mass-spring model, therefore certain restrictions on the simulation time step must be enforced so that the model produces physically accurate results. The hybrid mathematical model is used to simulate small fibre networks to provide information on the acoustic response of each damage mechanism. The simulated AEs are then analysed using a continuous wavelet transform (CWT), described in [5], which provides a two dimensional time-frequency representation of the signal. The AEs from the two damage mechanisms show different characteristics in the CWT so that it is possible to define a fibre-fibre bond failure by the criteria listed below. The dominant frequency components of the AE must be at approximately 250 kHz or 750 kHz. The strongest frequency component may be at either approximately 250 kHz or 750 kHz. The duration of the frequency component at approximately 250 kHz is longer than that of the frequency component at approximately 750 kHz. Similarly, the criteria for identifying a fibre failure are given below. The dominant frequency component of the AE must be greater than 800 kHz. The duration of the dominant frequency component must be less than 5.00E-06 seconds. The dominant frequency component must be present at the front of the AE. Essentially, the failure of a fibre-fibre bond produces a low frequency wave and the failure of a fibre produces a high frequency pulse. Using this theoretical criteria, it is now possible to train an intelligent classifier such as the Self-Organising Map (SOM) [6] using the experimental data. First certain features must be extracted from the CWTs of the AEs for use in training the SOM. For this work, each CWT is divided into 200 windows of 5E-06s in duration covering a 100 kHz frequency range. The power ratio for each windows is then calculated and used as a feature. Having extracted the features from the AEs, the SOM can now be trained, but care is required so that the both damage mechanisms are adequately represented in the training set. This is an issue with paper as the failure of the fibre-fibre bonds is the prevalent damage mechanism. Once a suitable training set is found, the SOM can be trained and its performance analysed. For the SOM described in this work, there is a good chance that it will correctly classify the experimental AEs.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

ROLE OF MULTIPLE TOLL-LIKE RECEPTOR ACTIVATION IN REGULATING CYTOTOXIC T CELL PRIMING TO LYMPOCYTIC CHORIOMENINGITIS VIRUS INFECTIONS

Foreign pathogens are recognized by toll-like receptors (TLR), present on various immune cells such as professional antigen-presenting cells (pAPCs). On recognition of its ligand, these receptors activate pAPCs, which may in turn influence naïve CD8+ T cell activation and affect their abilities to clear viral infection. However, how TLR ligands (TLR-L) can regulate CD8+ T cell responses have not been fully elucidated. This thesis will focus on examining how the presence of components from foreign pathogens, e.g. viral or bacterial infection, can contribute to shaping host immunity during concurrent viral infections. Since nitric oxide (NO), an innate effector immune molecule, was recently suggested to regulate proteasome activity; we sought to examine if NO can influence MHC-I antigen presentation during viral infections. The data in this section of the thesis provides evidence that combined TLR engagement can alter the presentation of certain CD8+ epitopes due to NO-induced inhibition in proteasome activity. Taken together, the data demonstrate that TLR ligation can influence the adaptive immune response due to induction of specific innate effector molecules such as NO. Next, the influence of combined TLR engagement on CD8+ T cell immunodominance hierarchies during viral infections was examined. In this section, we established that dual TLR2 and TLR3 stimulation alters immunodominance hierarchies of LCMV epitopes as a result of reduced uptake of cell-associated antigens and reduced cross-presentation of NP396 consequently suppressing NP396-specific CD8+ T cell responses. These findings are significant as they highlight a new role for TLR ligands in regulating anti-viral CD8+ T cell responses through impairing cross-presentation of cell-associated antigens depending on the type of TLR present in the environment during infections. Finally, we addressed TLR ligand induced type I interferon production and the signalling pathways that regulate them in two different mouse macrophage populations – those derived from the spleen or bone marrow. In this study, we observed that concomitant TLR2 stimulation blocked the induction of type I IFN induced by TLR4 in bone marrow-derived macrophages, but not spleen-derived macrophages in SOCS3-dependent manner. Taken together, the data presented in this thesis have defined new facets of how anti-viral responses are regulated by TLR activation, especially if multiple receptors are engaged simultaneously.

A machine vision approach to the grading of crushed aggregate

The grading of crushed aggregate is carried out usually by sieving. We describe a new image-based approach to the automatic grading of such materials. The operational problem addressed is where the camera is located directly over a conveyor belt. Our approach characterizes the information content of each image, taking into account relative variation in the pixel data, and resolution scale. In feature space, we find very good class separation using a multidimensional linear classifier. The innovation in this work includes (i) introducing an effective image-based approach into this application area, and (ii) our supervised classification using wavelet entropy-based features.

Scientifi c discourses on chronic pain without organic cause Incorporating gender perspective for pain resignifyng-repoliticizing

Chronic pain, without any organic or physical cause (DC), which in psycho-medical terminology is known as fi bromyalgia, (FM), is diagnosed each year to a considerable number of women in capitalistic societies. Our main interest in the following paper is to go in depth in the elaboration of this symptom, its treatment and the psychosocial effects, both in the social order as well as in the lives of the people who suffer from it. Our main goal in the following paper is to look deeper in the elaboration (conceptualization) of this symptom, its treatment and psychological affects, both in the social order as well as in the lives of the people who suffer from it, we are using linked speeches in Spanish magazines publications. The result has been the emergence of three hegemonic discourse positions: One position “scientist”, one “therapeutic of the conformity” position and one “economic and legalistic” position. Each of these has a specifi c feature, but on the whole, is enhanced, producing effects such as the absence of social context to explain the disease; disregard of gender differences in the management and treatment; the instrumentalization of pain to legitimize their practices and the subjection of women to the “psycho-biomedical” paradigm. In that way, a new signifi cance and politicization of the concept of pain is proposed.

Proposed wavelet-neurofuzzy combined system for power qualityviolations detection and diagnosis

A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet multiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed information. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is extensively reduced. A modified organisation map of the neurofuzzy classifier has significantly improved the diagnosis efficiency. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis

The Combination of Multiple Classifiers Using an Evidential Reasoning Approach

In many domains when we have several competing classifiers available we want to synthesize them or some of them to get a more accurate classifier by a combination function. In this paper we propose a ‘class-indifferent’ method for combining classifier decisions represented by evidential structures called triplet and quartet, using Dempster's rule of combination. This method is unique in that it distinguishes important elements from the trivial ones in representing classifier decisions, makes use of more information than others in calculating the support for class labels and provides a practical way to apply the theoretically appealing Dempster–Shafer theory of evidence to the problem of ensemble learning. We present a formalism for modelling classifier decisions as triplet mass functions and we establish a range of formulae for combining these mass functions in order to arrive at a consensus decision. In addition we carry out a comparative study with the alternatives of simplet and dichotomous structure and also compare two combination methods, Dempster's rule and majority voting, over the UCI benchmark data, to demonstrate the advantage our approach offers. (A continuation of the work in this area that was published in IEEE Trans on KDE, and conferences)

"Bobok" y "Pedro Páramo", dos narraciones sobre muertos

El presente estudio realiza un análisis comparativo entre la novela del mexicano Juan Rulfo, Pedro Páramo (1955), y un cuento del escritor ruso Dostoievski que trata también el tema del Más allá, titulado Bobok (1873). Paralelamente, se trabaja con la posibilidad de que el relato ruso hubiese podido ser una más de las fuentes literarias de la novela mexicana, y se trata de determinar las posibles conexiones -directas o indirectas- entre las dos obras. Ambas son puestas en común por su género literario, y a partir de ahí se estudian los elementos constitutivos que tienen en común, sus afinidades y divergencias más llamativas.

Expression of the common heat-shock protein receptor CD91 is increased on monocytes of exposed yet HIV-1-seronegative subjects.

The significantly higher surface expression of the surface heat-shock protein receptor CD91 on monocytes of human immunodeficiency virus type-1 (HIV-1)-infected, long-term nonprogressors suggests that HIV-1 antigen uptake and cross-presentation mediated by CD91 may contribute to host anti-HIV-1 defenses and play a role in protection against HIV-1 infection. To investigate this further, we performed phenotypic analysis to compare CD91 surface expression on CD14+ monocytes derived from a cohort of HIV-1-exposed seronegative (ESN) subjects, their seropositive (SP) partners, and healthy HIV-1-unexposed seronegative (USN) subjects. The median fluorescent intensity (MFI) of CD91 on CD14+ monocytes was significantly higher in ESN compared with SP (P=0.028) or USN (P=0.007), as well as in SP compared with USN subjects (P=0.018). CD91 MFI was not normalized in SP subjects on highly active antiretroviral therapy (HAART) despite sustainable, undetectable plasma viraemia. Data in three SP subjects experiencing viral rebounds following interruption of HAART showed low CD91 MFI comparable with levels in USN subjects. There was a significant positive correlation between CD91 MFI and CD8+ T cell counts in HAART-naïve SP subjects (r=0.7, P=0.015). Increased surface expression of CD91 on CD14+ monocytes is associated with the apparent HIV-1 resistance that is observed in ESN subjects.

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78-0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1-6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI. © Springer-Verlag London Limited 2008.

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.

Multi-Study Assessment of Vaginal Cytokines and Microflora in the Safety Evaluation of Intravaginal Rings in Pig-Tailed Macaques

BACKGROUND: HIV microbicide trials have emphasized the need to evaluate the safety of topical microbicides and delivery platforms in an animal model prior to conducting clinical efficacy trials. An ideal delivery device should provide sustainable and sufficient concentrations of effective products to prevent HIV transmission while not increasing transmission risk by either local mucosal inflammation and/or disruption of the normal vaginal microflora.

METHODS: Safety analyses of macaque-sized elastomeric silicone and polyurethane intravaginal rings (IVRs) loaded with candidate antiretroviral (ARV) drugs were tested in four studies ranging in duration from 49 to 73 days with retention of the IVR being 28 days in each study. Macaques were assigned to 3 groups; blank IVR, ARV-loaded IVR, and naïve. In sequential studies, the same macaques were used but rotated into different groups. Mucosal and systemic levels of cytokines were measured from vaginal fluids and plasma, respectively, using multiplex technology. Changes in vaginal microflora were also monitored. Statistical analysis (Mann-Whitney test) was used to compare data between two groups of unpaired samples (with and without IVR, and IVR with and without ARV) for the groups collectively, and also for individual macaques.

RESULTS: There were few statistically significant differences in mucosal and systemic cytokine levels measured longitudinally when the ring was present or absent, with or without ARVs. Of the 8 proinflammatory cytokines assayed a significant increase (p = 0.015) was only observed for IL8 in plasma with the blank and ARV loaded IVR (median of 9.2 vs. 5.7 pg/ml in the absence of IVR). There were no significant differences in the prevalence of H2O2-producing lactobacilli or viridans streptococci, or other microorganisms indicative of healthy vaginal microflora. However, there was an increase in the number of anaerobic gram negative rods in the presence of the IVR (p= < 0.0001).

CONCLUSIONS: IVRs with or without ARVs neither significantly induce the majority of potentially harmful proinflammatory cytokines locally or systemically, nor alter the lactobacillus or G. vaginalis levels. The increase in anaerobic gram negative rods alone suggests minimal disruption of normal vaginal microflora. The use of IVRs as a long-term sustained delivery device for ARVs is promising and preclinical studies to demonstrate the prevention of transmission in the HIV/SHIV nonhuman primate model should continue.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.