Dynamic viscosity of binary and ternary mixtures containing poly(ethylene glycol), potassium phosphate, and water

Dynamic viscosity of binary mixtures of poly(ethylene glycol) molar mass 1500 da + water, potassium phosphate + water, and ternary mixtures of poly(ethylene glycol) molar mass 1500 da + potassium phosphate + water were determined at 303.15 K Binary and ternary mixture viscosities showed a direct logarithm-type relation with the increase of poly(ethylene glycol) and potassium phosphate contents. The models used for viscosity correlation gave a good fit to the experimental data.

Density, heat capacity and thermal conductivity of liquid egg products

Density, heat capacity and thermal conductivity of liquid egg products, such as egg white, egg yolk, whole egg and various white and yolk blends, were determined as affected by temperature and water content ranging from 273 to 311 K and 51.8 to 88.2% (mass), respectively. Polynomial models fitted the experimental data very well, showing a linear relationship both for temperature and water content. (c) 2005 Published by Elsevier Ltd.

Effects of Transcranial Magnetic Stimulation on the Cognitive Event-Related Potential P300: A Literature Review

The objective of this study was to perform a systematic review regarding the effects of transcranial magnetic stimulation (TMS) on the cognitive event-related potential P300. A search was performed of the PubMed database, using the keywords "transcranial magnetic stimulation" and "P300." Eight articles were selected and, after analysis of references, one additional article was added to the list. We found the comparison among studies to be difficult, as the information regarding the effects of TMS on P300 is both scarce and heterogeneous with respect to the parameters used in TMS stimulation and the elicitation of P300. However, 7 of 9 studies found positive results. New studies need to be carried out in order to understand the contribution of these variables and others to the alteration in the latency and amplitude of the P300 wave.

Infantile epileptic encephalopathy with hypsarrhythmia (infantile spasms/west syndrome) and immunity

West syndrome is a severe epilepsy, occurring in infancy, that comprises epileptic seizures known as spasms, in clusters, and a unique EEG pattern, hypsarrhythmia, with psychomotor regression. Maturation of the brain is a crucial component. The onset is within the first year of life, before 12 months of age. Patients are classified as cryptogenic (10 to 20%), when there are no known or diagnosed previous cerebral insults, and symptomatic (80 to 90%), when associated with pre-existing cerebral damages. The time interval from a brain insult to infantile spasms onset ranged from 6 weeks to 11 months. West syndrome has a time-limited natural evolutive course, usually disappearing by 3 or 4 years of age. In 62% of patients, there are transitions to another age-related epileptic encephalopathies, the Lennox-Gastaut Syndrome and severe epilepsy with multiple independent foci. Spontaneous remission and remission after viral infections may occur. Therapy with ACTH and corticosteroids are the most effective. Reports about intravenous immunoglobulins action deserve attention. There is also immune dysfunction, characterized mainly by anergy, impaired cell-mediated immunity, presence of immature thymocytes in peripheral blood, functional impairment of T lymphocytes induced by plasma inhibitory factors, and altered levels of immunoglobulins. Changes in B lymphocytes frequencies and increased levels of activated B cells have been reported. Sensitized lymphocytes to brain extract were also described. Infectious diseases are frequent and may, sometimes, cause fatal outcomes. Increase of pro-inflamatory cytokines in serum and cerebrospinal fluid of epileptic patients were reported. Association with specific HLA antigens was described by several authors (HLA-DR7, HLA-A7, HLA-DRw52, and HLA-DR5). Auto-antibodies to brain antigens, of several natures (N-methyl-d-aspartate glutamate receptor, gangliosides, brain tissue extract, synaptic membrane, and others), were described in epileptic patients and in epileptic syndromes. Experimental epilepsy studies with anti-brain antibodies demonstrated that epileptiform discharges can be obtained, producing hyperexcitability leading to epilepsy. We speculate that in genetically prone individuals, previous cerebral lesions may sensitize immune system and trigger an autoimmune disease. Antibody to brain antigens may be responsible for impairment of T cell function, due to plasma inhibitory effect and also cause epilepsy in immature brains. © 2008 Bentham Science Publishers Ltd.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

Studying the satisfaction of patients on the outcome of an aesthetic dermatological filler treatment

Background: Many factors contribute to extend productive life in the modern world. Competition makes people worry about physical appearance, mosftly in respect to facial and skin aging. This has motivated new developments in cosmetic dermatology and the need of evaluating patient satisfaction with the new proposed treatments. Poll questionnaire has been used for such evaluation, and the analysis of the electroencephalogram (EEG) mapping obtained while the patient answers the satisfaction questionnaire may render the results less subjective. Objectives: The purpose of this paper is to study the satisfaction of a group of 33 women (mean age, 44.years) treated with hyaluronic acid filling of nasolabial folding or lips, combining the EEG brain mapping and questionnaire techniques. Methods: At the third month of evaluation, two networked personal computers were used for the EEG recording and for presenting the patient with a questionnaire about her well-being feeling; self-evaluation of her face; her satisfaction with the results of the aesthetic treatment; how the family, friends, and people at work evaluated the result of the treatment; and her decision to repeat the treatment and to recommend it to friends and family. Results: Poll results showed that patients were feeling well and were satisfied with the results of the aesthetic treatment. Furthermore, the regression EEG mappings showed patients to be satisfied with their appearance and with the treatment involving similar brain areas. Conclusion: Patients decided to undergo the treatment because they were already considering it (54%) or because they were dissatisfied with their lips or nasolabial folding (52%). The fact that the treatment was free of charge solidified the decision. Patients consider themselves as good-looking and they wanted to preserve such a condition. © 2008 Wiley Periodicals, Inc.

Infrared face recogniton by optimum-path forest

This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

A snoring classifier based on heart rate variability analysis

The effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring. © 2011 CCAL.

Tri-state Space Vector Modulation for Three-phase integrated inverters

The aim of this work is to present a modified Space Vector Modulation (SVM) suitable for Tri-state Three-phase inverters. A standard SVM algorithm and the Tri-state PWM (Pulse Width Modulation) are presented and their concept are mixed into the novel SVM. The proposed SVM is applied to a three-phase tri-state integrated Boost inverter, intended to Photovoltaic Energy Applications. The main features for this novel SVM are validated through simulations and also by experimental tests. The obtained results prove the feasibility of the proposal. © 2011 IEEE.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.