POLLING SYSTEMS WITH PARAMETER REGENERATION, THE GENERAL CASE

We consider a polling model with multiple stations, each with Poisson arrivals and a queue of infinite capacity. The service regime is exhaustive and there is Jacksonian feedback of served customers. What is new here is that when the server comes to a station it chooses the service rate and the feedback parameters at random; these remain valid during the whole stay of the server at that station. We give criteria for recurrence, transience and existence of the sth moment of the return time to the empty state for this model. This paper generalizes the model, when only two stations accept arriving jobs, which was considered in [Ann. Appl. Probab. 17 (2007) 1447-1473]. Our results are stated in terms of Lyapunov exponents for random matrices. From the recurrence criteria it can be seen that the polling model with parameter regeneration can exhibit the unusual phenomenon of null recurrence over a thick region of parameter space.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

A new monitor for routine thermal and epithermal neutron fluence rate monitoring in k(0) INAA

The Zr-Au set for monitoring the thermal and epithermal neutron fluence rate and the epithermal spectrum parameter a is not always practicable for routine application of INAA in well-thermalized facilities. An alternative set consisting of Cr, Au and Mo provides values for the thermal neutron fluence rate, f and alpha that are not significantly different from those found via the Zr-Au method and the Cd-covered Zr-method. The IRMM standard SMELS-II was analyzed using the (Au-Cr-Mo) monitor and a good agreement was obtained. (C) 2008 Elsevier Ltd. All rights reserved.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Efficient method for obtaining Lep(ob)/Lep(ob)-derived animal models using adipose tissue transplantations

Background: Leptin-deficient mice (Lep(ob)/Lep(ob), also known as ob/ob) are of great importance for studies of obesity, diabetes and other correlated pathologies. Thus, generation of animals carrying the Lep(ob) gene mutation as well as additional genomic modifications has been used to associate genes with metabolic diseases. However, the infertility of Lep(ob)/Lep(ob) mice impairs this kind of breeding experiment. Objective: To propose a new method for production of Lep(ob)/Lep(ob) animals and Lep(ob)/Lep(ob)-derived animal models by restoring the fertility of Lep(ob)/Lep(ob) mice in a stable way through white adipose tissue transplantations. Methods: For this purpose, 1 g of peri-gonadal adipose tissue from lean donors was used in subcutaneous transplantations of Lep(ob)/Lep(ob) animals and a crossing strategy was established to generate Lep(ob)/Lep(ob)-derived mice. Results: The presented method reduced by four times the number of animals used to generate double transgenic models (from about 20 to 5 animals per double mutant produced) and minimized the number of genotyping steps (from 3 to 1 genotyping step, reducing the number of Lep gene genotyping assays from 83 to 6). Conclusion: The application of the adipose transplantation technique drastically improves both the production of Lep(ob)/Lep(ob) animals and the generation of Lep(ob)/Lep(ob)-derived animal models. International Journal of Obesity (2009) 33, 938-944; doi: 10.1038/ijo.2009.95; published online 16 June 2009

Exercise training associated with diet improves heart rate recovery and cardiac autonomic nervous system activity in obese children

The purpose of this study was to test the hypotheses that in obese children: 1) hypocaloric diet (D) improves both heart rate recovery at 1 min (Delta HRR1) cfter an exercise test, and cardiac autonomic nervous system activity (CANSA) in obese children; 2) Diet and exercise training (DET) combined leads to greater improvement in both Delta HRR1 after an exercise test and in CANSA, than D alone. Moreover, we examined the relationships among Delta HRR1, CANSA, cardiorespiratory fitness and anthropometric variables (AV) in obese children submitted to D and to DET. 33 obese children (10 +/- 0.2 years; body mass index (BMI) >95(th) percentile) were divided into 2 groups: D (n = 15; BMI = 31 +/- 1 kg/m(2)) and DET (n = 18; 29 +/- 1 kg/m(2)). All children performed a maximal cardiopulmonary exercise test on a treadmill. The Delta HRR1 was defined as the difference between heart rate at peak and at 1-min post-exercise. CANSA was assessed using power spectral analysis of heart rate variability at rest. The sympathovagal balance (low frequency and high frequency ratio, LF/HF) was measured. After interventions, all obese children showed reduced body weight (P < 0.05). The D group did not improve in terms of peak VO(2), Delta HRR1 or LF/HF ratio (P > 0.05). In contrast, the DET group showed increased peak VO(2) (P = 0.01) and improved Delta HRR1 (Delta HRR1 = 37.3 +/- 2.6; P = 0.01) and LF/HF ratio (P = 0.001). The DET group demonstrated significant relationships among Delta HRR1, peak VO(2) and CANSA (P < 0.05). In conclusion, DET, in contrast to D, promoted improved Delta HRR1 and CANSA in obese children, suggesting a positive influence of increased levels of cardiorespiratory fitness by exercise training on cardiac autonomic activity.

Influence of high- and low-carbohydrate diet following glycogen-depleting exercise on heart rate variability and plasma catecholamines

The purpose of this study was to investigate the effects of a short-term low-or high-carbohydrate (CHO) diet consumed after exercise on sympathetic nervous system activity. Twelve healthy males underwent a progressive incremental test; a control measurement of plasma catecholamines and heart rate variability (HRV); an exercise protocol to reduce endogenous CHO stores; a low-or high-CHO diet (counterbalanced order) consumed for 2 days, beginning immediately after the exercise protocol; and a second resting plasma catecholamine and HRV measurement. The exercise and diet protocols and the second round of measurements were performed again after a 1-week washout period. The mean (+/- SD) values of the standard deviation of R-R intervals were similar between conditions (control, 899.0 +/- 146.1 ms; low-CHO diet, 876.8 +/- 115.8 ms; and high-CHO diet, 878.7 +/- 127.7 ms). The absolute high-and low-frequency (HF and LF, respectively) densities of the HRV power spectrum were also not different between conditions. However, normalized HF and LF (i.e., relative to the total power spectrum) were lower and higher, respectively, in the low-CHO diet than in the control diet (mean +/- SD, 17 +/- 9 normalized units (NU) and 83 +/- 9 NU vs. 27 +/- 11 NU and 73 +/- 17 NU, respectively; p < 0.05). The LF/HF ratio was higher with the low-CHO diet than with the control diet (mean +/- SD, 7.2 +/- 6.2 and 4.2 +/- 3.2, respectively; p < 0.05). The mean values of plasma catecholamines were not different between diets. These results suggest that the autonomic control of the heart rate was modified after a short-term low-CHO diet, but plasma catecholamine levels were not altered.

Peak torque and rate of torque development in elderly with and without fall history

Background Falls are one of the greatest concerns among the elderly A number of studies have described peak torque as one of the best fall-related predictor. No studies have comprehensively focused on the rate of torque development of the lower limb muscles among elderly fallers. Then, the aim of this study was to determine the relationship between muscle peak torque and rate of torque development of the lower limb joints in elderly with and without fall history It was also aimed to determine whether these parameters of muscle performance (i e, peak torque and rate of torque development) are related to the number of falls. Methods: Thirty-one women volunteered to participate in the study and were assigned in one of the groups according to the number of falls over the 12 months that preceded the present Then, participants with no fall history (Cl; n = 13; 67.6[7.5] years-old), one fall (GII; n = 8, 66 0[4 91 years-old) and two or more falls (GIII, n = 10; 67.8[8.8] years-old) performed a number of lower limb maximal isometric voluntary contractions from which peak torque and rate of torque development were quantified Findings. Primary outcomes indicated no peak torque differences between experimental groups in any lower limb joint. The rate of torque development of the knee flexor muscles observed in the non-fallers (Cl) was greater than that observed in the fallers (P < 0.05) and had a significant relationship with the number of falls (P < 0 05) Interpretation. The greater knee flexor muscles` rate of torque development found in the non-fallers in comparison to the fallers indicated that the ability of the elderly to rapidly reorganise the arrangement of the lower limb may play a significant role in allowing the elderly to recover balance after a trip. Thus, training stimulus aimed to improve the rate of torque development may be more beneficial to prevent falls among the elderly than other training stimulus, which are not specifically designed to improve the ability to rapidly produce large amounts of torque (C) 2010 Published by Elsevier Ltd

Resting Heart Rate is Associated with Blood Pressure in Male Children and Adolescents

Objectives To analyze the association between resting heart rate and blood pressure in male children and adolescents and to identify if this association is mediated by important confounders. Study design Cross-sectional study carried out with 356 male children and adolescents from 8 to 18 years old. Resting heart rate was measured by a portable heart rate monitor according to recommendations and stratified into quartiles. Blood pressure was measured with an electronic device previously validated for pediatric populations. Body fatness was estimated by a dual-energy x-ray absorptiometry. Results Obese subjects had values of resting heart rate 7.8% higher than nonobese (P = .001). Hypertensive children and adolescents also had elevated values of resting heart rate (P = .001). When the sample was stratified in nonobese and obese, the higher quartile of resting heart rate was associated with hypertension in both groups of children and adolescents. Conclusions This study confirms the existence of a relationship between elevated resting heart rate and increased blood pressure in a pediatric population, independent of adiposity, ethnicity and age. (J Pediatr 2011; 158:634-7).

Niobium hydrogenation process Effect of temperature and cooling rate from the hydrogenation temperature

High-purity niobium powder can be produced via the hydrogenation and dehydrogenation processes The present work aimed at the effect of temperature and cooling rate conditions on the niobium hydrogenation process using hydrogen gas The hydrogen contents of the materials were evaluated by weight change and chemical analysis X ray diffraction (XRD) was performed to identify and determine the lattice parameters of the formed hydride phases No hydrogenation took place under isothermal conditions only during cooling of the materials Significant hydrogenation occurred in the 500 C and 700 C experiments leading to the formation of a beta NbH(x) single phase material (C) 2010 Elsevier Ltd All rights reserved

Screening of lipases for the synthesis of xylitol monoesters by chemoenzymatic esterification and the potential of microwave and ultrasound irradiations to enhance the reaction rate

Lipases from different sources, Pseudomonas fluorescens (AK lipase), Burkholderia cepacia (PS lipase), Penicillium camembertii (lipase G) and Porcine pancreas lipase (PPL), previously immobilized on epoxy SiO(2)-PVA, were screened for the synthesis of xylitol monoesters by esterification of the protected xylitol using oleic acid as acyl donor group. Among all immobilized derivatives, the highest esterification yield was achieved by P. camembertii lipase, showing to be attractive alternative to bulk chemical routes to satisfy increasing commercial demands. Further experiments were performed to determine the influence of fatty acids chain size on the reaction yield and the feasibility of using non-conventional heating systems (microwave and ultrasound irradiations) to enhance the reaction rate. (C) 2010 Elsevier B.V. All rights reserved.

Aerodynamic study of three-dimensional larynx models using finite element methods

The airflow velocities and pressures are calculated from a three-dimensional model of the human larynx by using the finite element method. The laryngeal airflow is assumed to be incompressible, isothermal, steady, and created by fixed pressure drops. The influence of different laryngeal profiles (convergent, parallel, and divergent), glottal area, and dimensions of false vocal folds in the airflow are investigated. The results indicate that vertical and horizontal phase differences in the laryngeal tissue movements are influenced by the nonlinear pressure distribution across the glottal channel, and the glottal entrance shape influences the air pressure distribution inside the glottis. Additionally, the false vocal folds increase the glottal duct pressure drop by creating a new constricted channel in the larynx, and alter the airflow vortexes formed after the true vocal folds. (C) 2007 Elsevier Ltd. All rights reserved.

Mechanistic approach to predict real machining time for milling free-form geometries applying high feed rate

An accurate estimate of machining time is very important for predicting delivery time, manufacturing costs, and also to help production process planning. Most commercial CAM software systems estimate the machining time in milling operations simply by dividing the entire tool path length by the programmed feed rate. This time estimate differs drastically from the real process time because the feed rate is not always constant due to machine and computer numerical controlled (CNC) limitations. This study presents a practical mechanistic method for milling time estimation when machining free-form geometries. The method considers a variable called machine response time (MRT) which characterizes the real CNC machine`s capacity to move in high feed rates in free-form geometries. MRT is a global performance feature which can be obtained for any type of CNC machine configuration by carrying out a simple test. For validating the methodology, a workpiece was used to generate NC programs for five different types of CNC machines. A practical industrial case study was also carried out to validate the method. The results indicated that MRT, and consequently, the real machining time, depends on the CNC machine`s potential: furthermore, the greater MRT, the larger the difference between predicted milling time and real milling time. The proposed method achieved an error range from 0.3% to 12% of the real machining time, whereas the CAM estimation achieved from 211% to 1244% error. The MRT-based process is also suggested as an instrument for helping in machine tool benchmarking.