The Computation of Fields and Signals due to Ferromagnetic Anomalies

In this paper we develop a model for the computation of electromagnetic fields in anomalous regions of ferromagnetic cylinders. The role of electric and magnetic current densities as sources for these fields is explicitly presented. The starting point for the development is the computation of a three-dimensional Green’s function for the cylinder, from which the appropriate integral relations between the field and its sources can be derived. The rigorous calculation of the anomalous current source within the anomalous region requires the solution of an integral equation that has the Green’s function as its kernel. We do not carry out this calculation, but approximate the anomalous current by the applied field due to the exciting coil (which, for our examples is an infinite solenoid that is coaxial to the cylinder). Once the field within the anomalies is determined, the field external to the wall of the tube may be computed, and this provides the signal that is sensed by a coil, or other means

A Computer Model of Eddy-Current Probe-Crack Interaction

A general three-dimensional eddy-current probe model, developed by Sabbagh Associates and reported in [1], [2] and [3], has been adapted for the calculation of probe-flaw interactions. The theoretical model, [4] and [5], uses integral equations with dyadic Green’s function kernels, and is applicable to both probe and flaw calculations at arbitrary skin depths and frequencies. Discrete approximations of the integral equations are solved using a highly efficient algorithm based on recent developments in numerical techniques and their application to the solution of large problems in electromagnetic field-theory

Numerical Electromagnetic Modeling for Three-Dimensional Inspection of Ferrous Metals

The problem that we are trying to solve is: Given an excitation source, which is known to us, and a scattered field, which we can measure (albeit somewhat inaccurately, because of noise and the like), determine the spatial distribution of the electromagnetic parameters, µ, and σ, where µ is the magnetic permeability and σ the electrical conductivity. This allows us to determine the structure of a body in free space, or the structure of an internal flaw (or anomalous region) within a given body whose properties, such as size, shape and electrical parameters, are known to us. Throughout this paper we will consider only isotropic bodies, which means that the conductivity and magnetic permeability are scalar functions of positions

Consequences of Aberrant Insulin Regulation in the Brain: Can Treating Diabetes be Effective for Alzheimer’s Disease

There is an urgent need for new ways to treat Alzheimer’s disease (AD), the most common cause of dementia in the elderly. Current therapies are modestly effective at treating the symptoms, and do not significantly alter the course of the disease. Over the years, a range of epidemiological and experimental studies have demonstrated interactions between diabetes mellitus and AD. As both diseases are leading causes of morbidity and mortality in the elderly and are frequent co-morbid conditions, it has raised the possibility that treating diabetes might be effective in slowing AD. This is currently being attempted with drugs such as the insulin sensitizer rosiglitazone. These two diseases share many clinical and biochemical features, such as elevated oxidative stress, vascular dysfunction, amyloidogenesis and impaired glucose metabolism suggesting common pathogenic mechanisms. The main thrust of this review will be to explore the evidence from a pathological point of view to determine whether diabetes can cause or exacerbate AD. This was supported by a number of animal models of AD that have been shown to have enhanced pathology when diabetic conditions were induced. The one drawback in linking diabetes and insulin to AD has been the postmortem studies of diabetic brains demonstrating that AD pathology was not increased; in fact decreased pathology has often been reported. In addition, diabetes induces its own distinct features of neuropathology different from AD. There are common pathological features to be considered including vascular abnormalities, a major feature arising from diabetes; there is increasing evidence that vascular abnormalities can contribute to AD. The most important common mechanism between insulin-resistant (type II) diabetes and AD could be impaired insulin signaling; a form of toxic amyloid can damage neuronal insulin receptors and affect insulin signaling and cell survival. It has even been suggested that AD could be considered as “type 3 diabetes” since insulin can be produced in brain. Another common feature of diabetes and AD are increased advanced glycation endproduct-modified proteins are found in diabetes and in the AD brain; the receptor for advanced glycation endproducts plays a prominent role in both diseases. In addition, a major role for insulin degrading enzyme in the degradation of Aβ peptide has been identified. Although clinical trials of certain types of diabetic medications for treatment of AD have been conducted, further understanding the common pathological processes of diabetes and AD are needed to determine whether these diseases share common therapeutic targets

A User Level Markov model for service based CRRM algorithm

In order to support the conceptual development of Radio Access Technology (RAT) selection algorithms, the theory of Markov model has been used. Performance metrics can be derived from the steady state probabilities of a Markov model. This paper extends a User Level Markov model for a three co-located RATs system from existing two co-located RATs Markov models. The service based RAT selection algorithm has been studied using the proposed Markov model. Numerical results obtained from the proposed Markov model are presented. ©2010 IEEE

Integrated Biomarker Profiling of Smokers with Periodontitis

Background In the context of precision medicine, understanding patient‐specific variation is an important step in developing targeted and patient‐tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups. Aim The goal of this study was to discern biological response profiles of periodontitis and non‐periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers). Material & Methods The investigation uses a novel ensemble classification approach (SVA‐SVM) to differentiate disease groups and patient‐specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups. Results Sensitivity of SVA‐SVM is shown to be considerably higher than several traditional independent classifier systems. Patient‐specific networks generated from SVA‐SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High‐confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups. Conclusions Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient‐specific risk profiles and tailored interventions

Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis

This study investigates the use of saliva, as an emerging diagnostic fluid in conjunction with classification techniques to discern biological heterogeneity in clinically labelled gingivitis and periodontitis subjects (80 subjects; 40/group) A battery of classification techniques were investigated as traditional single classifier systems as well as within a novel selective voting ensemble classification approach (SVA) framework. Unlike traditional single classifiers, SVA is shown to reveal patient-specific variations within disease groups, which may be important for identifying proclivity to disease progression or disease stability. Salivary expression profiles of IL-1ß, IL-6, MMP-8, and MIP-1α from 80 patients were analyzed using four classification algorithms (LDA: Linear Discriminant Analysis [LDA], Quadratic Discriminant Analysis [QDA], Naïve Bayes Classifier [NBC] and Support Vector Machines [SVM]) as traditional single classifiers and within the SVA framework (SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM). Our findings demonstrate that performance measures (sensitivity, specificity and accuracy) of traditional classification as single classifier were comparable to that of the SVA counterparts using clinical labels of the samples as ground truth. However, unlike traditional single classifier approaches, the normalized ensemble vote-counts from SVA revealed varying proclivity of the subjects for each of the disease groups. More importantly, the SVA identified a subset of gingivitis and periodontitis samples that demonstrated a biological proclivity commensurate with the other clinical group. This subset was confirmed across SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM. Heatmap visualization of their ensemble sets revealed lack of consensus between these subsets and the rest of the samples within the respective disease groups indicating the unique nature of the patients in these subsets. While the source of variation is not known, the results presented clearly elucidate the need for novel approaches that accommodate inherent heterogeneity and personalized variations within disease groups in diagnostic characterization. The proposed approach falls within the scope of P4 medicine (predictive, preventive, personalized, and participatory) with the ability to identify unique patient profiles that may predict specific disease trajectories and targeted disease management

Low disordered, stable, and shallow germanium quantum wells: a playground for spin and hybrid quantum technology

Buried-channel semiconductor heterostructures are an archetype material platform to fabricate gated semiconductor quantum devices. Sharp confinement potential is obtained by positioning the channel near the surface, however nearby surface states degrade the electrical properties of the starting material. In this paper we demonstrate a two-dimensional hole gas of high mobility ($5\times 10^{5}$ cm$^2$/Vs) in a very shallow strained germanium channel, which is located only 22 nm below the surface. This high mobility leads to mean free paths $\approx6 \mu m$, setting new benchmarks for holes in shallow FET devices. Carriers are confined in an undoped Ge/SiGe heterostructure with reduced background contamination, sharp interfaces, and high uniformity. The top-gate of a dopant-less field effect transistor controls the carrier density in the channel. The high mobility, along with a percolation density of $1.2\times 10^{11}\text{ cm}^{-2}$, light effective mass (0.09 m$_e$), and high g-factor (up to $7$) highlight the potential of undoped Ge/SiGe as a low-disorder material platform for hybrid quantum technologies

Cross-Talk Between Clinical and Host-Response Parameters of Periodontitis in Smokers

Background and Objective Periodontal diseases are a major public health concern leading to tooth loss and have also been shown to be associated with several chronic systemic diseases. Smoking is a major risk factor for the development of numerous systemic diseases, as well as periodontitis. While it is clear that smokers have a significantly enhanced risk for developing periodontitis leading to tooth loss, the population varies regarding susceptibility to disease associated with smoking. This investigation focused on identifying differences in four broad sets of variables, consisting of: (i) host‐response molecules; (ii) periodontal clinical parameters; (iii) antibody responses to periodontal pathogens and oral commensal bacteria; and (iv) other variables of interest, in a population of smokers with (n = 171) and without (n = 117) periodontitis. Material and Methods Bayesian network structured learning (BNSL) techniques were used to investigate potential associations and cross‐talk between the four broad sets of variables. Results BNSL revealed two broad communities with markedly different topology between the populations of smokers, with and without periodontitis. Confidence of the edges in the resulting network also showed marked variations within and between the periodontitis and nonperiodontitis groups. Conclusion The results presented validated known associations and discovered new ones with minimal precedence that may warrant further investigation and novel hypothesis generation. Cross‐talk between the clinical variables and antibody profiles of bacteria were especially pronounced in the case of periodontitis and were mediated by the antibody response profile to Porphyromonas gingivalis