Theoretical and experimental studies of the bell-jar-top inductively coupled plasma

The present paper describes a systematic study of argon plasmas in a bell-jar inductively coupled plasma (ICP) source over the range of pressure 5-20 mtorr and power input 0.2-0.5 kW, Experimental measurements as well as results of numerical simulations are presented. The models used in the study include the well-known global balance model (or the global model) as well as a detailed two-dimensional (2-D) fluid model of the system, The global model is able to provide reasonably accurate values for the global electron temperature and plasma density, The 2-D model provides spatial distributions of various plasma parameters that make it possible to compare with data measured in the experiments, The experimental measurements were obtained using a tuned Langmuir double-probe technique to reduce the RF interference and obtain the light versus current (I-V) characteristics of the probe. Time-averaged electron temperature and plasma density were measured for various combinations of pressure and applied RF power, The predictions of the 2-D model were found to be in good qualitative agreement with measured data, It was found that the electron temperature distribution T-e was more or less uniform in the chamber, It was also seen that the electron temperature depends primarily on pressure, but is almost independent of the power input, except in the very low-pressure regime. The plasma density goes up almost linearly with the power input.

Rapid diagnosis of experimental meningitis by bacterial heat production in cerebrospinal fluid

BACKGROUND: Calorimetry is a nonspecific technique which allows direct measurement of heat generated by biological processes in the living cell. We evaluated the potential of calorimetry for rapid detection of bacterial growth in cerebrospinal fluid (CSF) in a rat model of bacterial meningitis. METHODS: Infant rats were infected on postnatal day 11 by direct intracisternal injection with either Streptococcus pneumoniae, Neisseria meningitidis or Listeria monocytogenes. Control animals were injected with sterile saline or heat-inactivated S. pneumoniae. CSF was obtained at 18 hours after infection for quantitative cultures and heat flow measurement. For calorimetry, 10 microl and 1 microl CSF were inoculated in calorimetry ampoules containing 3 ml trypticase soy broth (TSB). RESULTS: The mean bacterial titer (+/- SD) in CSF was 1.5 +/- 0.6 x 108 for S. pneumoniae, 1.3 +/- 0.3 x 106 for N. meningitidis and 3.5 +/- 2.2 x 104 for L. monocytogenes. Calorimetric detection time was defined as the time until heat flow signal exceeded 10 microW. Heat signal was detected in 10-microl CSF samples from all infected animals with a mean (+/- SD) detection time of 1.5 +/- 0.2 hours for S. pneumoniae, 3.9 +/- 0.7 hours for N. meningitidis and 9.1 +/- 0.5 hours for L. monocytogenes. CSF samples from non-infected animals generated no increasing heat flow (<10 microW). The total heat was the highest in S. pneumoniae ranging from 6.7 to 7.5 Joules, followed by L. monocytogenes (5.6 to 6.1 Joules) and N. meningitidis (3.5 to 4.4 Joules). The lowest detectable bacterial titer by calorimetry was 2 cfu for S. pneumoniae, 4 cfu for N. meningitidis and 7 cfu for L. monocytogenes. CONCLUSION: By means of calorimetry, detection times of <4 hours for S. pneumoniae and N. meningitidis and <10 hours for Listeria monocytogenes using as little as 10 microl CSF were achieved. Calorimetry is a new diagnostic method allowing rapid and accurate diagnosis of bacterial meningitis from a small volume of CSF.

Syphilitic diseases : their pathology, diagnosis, and treatment, including experimental researches on inoculation as a differential agent in testing the character of these affections /

Mode of access: Internet.

Condition monitoring and diagnosis of injector faults in a diesel engine using in-cylinder pressure and acoustic emission techniques

Failing injectors are one of the most common faults in diesel engines. The severity of these faults could have serious effects on diesel engine operations such as engine misfire, knocking, insufficient power output or even cause a complete engine breakdown. It is thus essential to prevent such faults from occurring by monitoring the condition of these injectors. In this paper, the authors present the results of an experimental investigation on identifying the signal characteristics of a simulated incipient injector fault in a diesel engine using both in-cylinder pressure and acoustic emission (AE) techniques. A time waveform event driven synchronous averaging technique was used to minimize or eliminate the effect of engine speed variation and amplitude fluctuation. It was found that AE is an effective method to detect the simulated injector fault in both time (crank angle) and frequency (order) domains. It was also shown that the time domain in-cylinder pressure signal is a poor indicator for condition monitoring and diagnosis of the simulated injector fault due to the small effect of the simulated fault on the engine combustion process. Nevertheless, good correlations between the simulated injector fault and the lower order components of the enveloped in-cylinder pressure spectrum were found at various engine loading conditions.

Using optical coherence tomography for differential diagnosis of a pigmented choroidal lesion

Clinicians regularly face the confronting challenge of differentiating a choroidal naevus from a melanoma. Uveal naevi are a relatively common finding during routine eye examinations: a prevalence of 6.5 per cent has been reported.1 In contrast, malignant melanomata are uncommon, being found in six persons per million population, but they can have devastating implications and consequences.2 Differential diagnoses can be difficult to make with certainty; any additional information that can assist in this process is advantageous...

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Acetylcholine receptor antibody in the diagnosis and management of myasthenia gravis

The relationship of acetylcholine receptor (AchR) antibodies to disease activity in myasthenia gravis (MG) is controversial. Some authors claim a direct correlation with disease activity and treatment, in particular plasmapheresis therapy, whereas others have commented on the poor overall correlation of antibody levels with clinical state. Antibody levels were examined in a population of MG patients and correlated with disease activity and response to treatment. Antibodies to skeletal muscle AchR were found in most patients with generalised MG (24/25) and in about half of the patients with purely ocular MG (6/10) and in neither of 2 patients with congenital MG. There was scant correlation with disease activity or response to treatment. It is concluded that the assay is more useful for diagnosis than for management of MG.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Automatic diagnosis of task faults in flexible manufacturing systems

Hardy, N. W., Barnes, D. P., Lee, L. H. (1989). Automatic diagnosis of task faults in flexible manufacturing systems. Robotica, 7 (1):25-35