Disruption prediction at JET [Joint European Torus]

The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach several Mega Newtons. The problem is even more severe in the nuclear fusion power station where the forces are in the order of one hundred Mega Newtons. The events that occur during a disruption are still not well understood even if some mechanisms that can lead to a disruption have been identified and can be used to predict them. Unfortunately it is always a combination of these events that generates a disruption and therefore it is not possible to use simple algorithms to predict it. This thesis analyses the possibility of using neural network algorithms to predict plasma disruptions in real time. This involves the determination of plasma parameters every few milliseconds. A plasma boundary reconstruction algorithm, XLOC, has been developed in collaboration with Dr. D. Ollrien and Dr. J. Ellis capable of determining the plasma wall/distance every 2 milliseconds. The XLOC output has been used to develop a multilayer perceptron network to determine plasma parameters as ?i and q? with which a machine operational space has been experimentally defined. If the limits of this operational space are breached the disruption probability increases considerably. Another approach for prediction disruptions is to use neural network classification methods to define the JET operational space. Two methods have been studied. The first method uses a multilayer perceptron network with softmax activation function for the output layer. This method can be used for classifying the input patterns in various classes. In this case the plasma input patterns have been divided between disrupting and safe patterns, giving the possibility of assigning a disruption probability to every plasma input pattern. The second method determines the novelty of an input pattern by calculating the probability density distribution of successful plasma patterns that have been run at JET. The density distribution is represented as a mixture distribution, and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. Together with these methods, a network has been designed to predict the vertical forces, that a disruption can cause, in order to avoid that too dangerous plasma configurations are run. This network can be run before the pulse using the pre-programmed plasma configuration or on line becoming a tool that allows to stop dangerous plasma configuration. All these methods have been implemented in real time on a dual Pentium Pro based machine. The Disruption Prediction and Prevention System has shown that internal plasma parameters can be determined on-line with a good accuracy. Also the disruption detection algorithms showed promising results considering the fact that JET is an experimental machine where always new plasma configurations are tested trying to improve its performances.

In vivo and in vitro aspects of imidazoline-2(1/2)sites within the central nervous system

The in vivo and in vitro characteristics of the I2 binding site were probed using the technique of drug discrimination and receptor autoradiography. Data presented in this thesis indicates the I2 ligand 2-BFI generates a cue in drug discrimination. Further studies indicated agmatine, a proposed endogenous imidazoline ligand, and a number of imidazoline and imidazole analogues of 2-BFI substitute significantly for 2-BFI. In addition to specific I2 ligands the administration of NRl's (noradrenaline reuptake inhibitors), the sympathomimetic d-amphetamine, the α1-adrenoceptor agonist methoxamine, but not the β1 agonist dobutamine or the β2 agonist salbutamol, gave rise to significant levels of substitution for the 2-BFI cue. The administration of the α1-adrenoceptor antagonist WB4101, prior to 2- BFI itself significantly reduced levels of 2-BFI appropriate responding. Administration of the reversible MAO-A inhibitors moclobemide and Ro41-1049, but not the reversible MAO-B inhibitors lazabemide and Ro16-6491, gave rise to potent dose dependent levels of substitution for the 2-BFI cue. Further studies indicated the administration of a number of β-carbolines and the structurally related indole alkaloid ibogaine also gave rise to dose dependent significant levels of substitution. Due to the relationship of indole alkaloids to serotonin the 5-HT releaser fenfluramine and a number of SSRI's (selective serotonin reuptake inhibitor) were also administered and these compounds gave rise to significant partial (20-80% responses to the 2-BFI lever) levels of substitution. The autoradiographical studies reported here indicate [3H]2-BFI labels I2 sites within the rat arcuate nucleus, area postrema, pineal gland, interpeduncular nucleus and subfornical organ. Subsequent experiments confirmed that the drug discrimination dosing schedule significantly increases levels of [3H]2-BFI 12 binding within two of these nuclei. However, levels of [3H]2-BFI specific binding were significantly reduced within four of these nuclei after chronic treatment with the irreversible MAO inhibitors deprenyl and tranylcypromine but not pargyline, which only reduced levels significantly in two. Further autoradiographical studies indicated that the distribution of [3H]2-BFI within the C57/B mouse compares favourably to that within the rat. Comparison of these levels of binding to those from transgenic mice who over-express MAO-B indicates two possibly distinct populations of [3H]2-BFI 12 sites exist in mouse brain. The data presented here indicates the 2-BFI cue is associated with the selective activation of α1-adrenoceptors and possibly 5-HT receptors. 2-BFI trained rats recognise reversible MAO-A but not MAO-B inhibitors. However, data within this thesis indicates the autoradiographical distribution of I2 sites bears a closer resemblance to that of MAO-B not MAO-A and further studies using transgenic mice that over-express MAO-B suggests a non-MAO-B I2 site exists in mouse brain.

Low temperature 1,1,1-trichloroethane dehydrochlorination over Pt catalysts:from model surfaces to the real world

Fast X-ray photoelectron spectroscopy reveals efficient C–Cl activation of 1,1,1-trichloroethane occurs over platinum surfaces at 150 K, and in the presence of hydrogen, sustained ambient temperature dehydrochlorination to HCl and ethane is possible over supported Pt/Al2O3 catalysts.

Double-Wavelet Neuron Based on Analytical Activation Functions

In this paper a new double-wavelet neuron architecture obtained by modification of standard wavelet neuron, and its learning algorithm are proposed. The offered architecture allows to improve the approximation properties of wavelet neuron. Double-wavelet neuron and its learning algorithm are examined for forecasting non-stationary chaotic time series.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Asymptotic and structural properties of special cases of the Wright function arising in probability theory

This analysis paper presents previously unknown properties of some special cases of the Wright function whose consideration is necessitated by our work on probability theory and the theory of stochastic processes. Specifically, we establish new asymptotic properties of the particular Wright function 1Ψ1(ρ, k; ρ, 0; x) = X∞ n=0 Γ(k + ρn) Γ(ρn) x n n! (|x| < ∞) when the parameter ρ ∈ (−1, 0)∪(0, ∞) and the argument x is real. In the probability theory applications, which are focused on studies of the Poisson-Tweedie mixtures, the parameter k is a non-negative integer. Several representations involving well-known special functions are given for certain particular values of ρ. The asymptotics of 1Ψ1(ρ, k; ρ, 0; x) are obtained under numerous assumptions on the behavior of the arguments k and x when the parameter ρ is both positive and negative. We also provide some integral representations and structural properties involving the ‘reduced’ Wright function 0Ψ1(−−; ρ, 0; x) with ρ ∈ (−1, 0) ∪ (0, ∞), which might be useful for the derivation of new properties of members of the power-variance family of distributions. Some of these imply a reflection principle that connects the functions 0Ψ1(−−;±ρ, 0; ·) and certain Bessel functions. Several asymptotic relationships for both particular cases of this function are also given. A few of these follow under additional constraints from probability theory results which, although previously available, were unknown to analysts.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

A 12-month randomized controlled trial of balance training in elderly women with ostebporosis: Improvement of quality of life

Objective: Physical and psychological incapacity, including fear of falling is related to decreased satisfaction with life in osteoporosis (OP). The impact of a balance exercise program on improving the quality of life is not well established. We have, therefore, investigated the effect of 12-month Balance Training Program in quality of life, functional balance and falls in elderly OP women. Methods: Sixty consecutive women with senile OP were randomized into a Balance Training Group (BT) of 30 patients and no intervention control group (CG) of 30 patients. The BT program included techniques to improve balance over a period of 12 months (1 h exercise session/week and home-based exercises). The quality of life was evaluated before and at the end of the trial using the Osteoporosis Assessment Questionnaire (OPAQ), functional balance was evaluated by Berg Balance Scale (BBS). Falls in the preceding year were noted and compared to the period of study. Results: The comparison of OPAQ variations (INITIAL-FINAL) revealed a significant improvement in quality of life in all parameters for BT compared to CG: well-being (1.61 +/- 1.44 vs. -1.46 +/- 1.32, p < 0001), physical function (1.30 +/- 1.33 vs. -0.36 +/- 0.82, p < 0.001), psychological status (1.58 +/- 1.36 vs. -1.02 +/- 0.83, p < 0.001), symptoms (2.76 +/- 1.96 vs. -0.63 +/- 0.87, p < 0.001), social interaction (1.01 +/- 1.51 vs. 0.35 +/- 1.08, p < 0.001). Of note, this overall benefit was paralleled by an improvement of BBS (-5.5 +/- 5.67 vs. +0.5 +/- 4.88 p < 0.001) and a reduction of falls in 50% in BT group vs. 26.6% for the CG (RR: 1.88, p < 0.025). Conclusion: The long-term Balance Training Program of OP women provides a striking overall health quality of life improvement in parallel with improving functional balance and reduced falls. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Comparison of renal transplantation outcomes in children with and without bladder dysfunction. A customized approach equals the difference

Purpose: We examined the development of urological abnormalities in a group of pediatric renal transplant recipients. Materials and Methods: We reviewed 211 patients younger than 19 years who underwent 226 renal transplants. Three groups of patients were studied-136 children with end stage renal disease due to a nonurological cause (group 1), 56 children with a urological disorder but with an adequate bladder (group 2a) and 19 children with lower urinary tract dysfunction and/or inadequate bladder drainage (group 2b). A total of 15 children in group 2b underwent bladder augmentation (ureterocystoplasty in 6, enterocystoplasty in 9), 2 underwent continent urinary diversion, 1 underwent autoaugmentation and 1 underwent a Mitrofanoff procedure at the bladder for easier drainage. Kidney transplantation was performed in the classic manner by extraperitoneal access, and whenever possible the ureter was reimplanted using an antireflux procedure. Results: At a mean followup of 75 months 13 children had died, 59 grafts were lost and 15 children had received a second transplant. Two patients in group 2a required a complementary urological procedure to preserve renal function (1 enterocystoplasty, 1 vesicostomy). A total of 12 major surgical complications occurred in 226 kidney transplants (5.3%), with a similar incidence in all groups. The overall graft survival at 5 years was 75%, 74% and 84%, respectively, in groups 1, 2a and 2b. Conclusions: With individualized treatment children with severely inferior lower urinary tract function may undergo renal transplantation with a safe and adequate outcome.

Active and passive behaviors of soft tissues: Pelvic floor muscles

: A new active-contraction visco-elastic numerical model of the pelvic floor (skeletal) muscle is presented. Our model includes all elements that represent the muscle constitutive behavior, contraction and relaxation. In contrast with the previous models, the activation function can be null. The complete equations are shown and exactly linearized. Small verification and validation tests are performed and the pelvis is modeled using the data from the intra-abdominal pressure tests

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

The Role of B-Type Natriuretic Peptide in the Diagnosis of Congestive Heart Failure in Patients Presenting to an Emergency Department with Dyspnea

OBJECTIVE: To determine the utility of B-type natriuretic peptide (BNP) in the diagnosis of congestive heart failure (CHF) in patients presenting with dyspnea to an emergency department (ED). METHODS: Seventy patients presenting with dyspnea to an ED from April to July 2001 were included in the study. Mean age was 72±16 years and 33 (47%) were male. BNP was measured in all patients at the moment of admission to the ED. Emergency-care physicians, blinded to BNP values, were required to assign a probable initial diagnosis. A cardiologist retrospectively reviewed the data (blinded to BNP measurements) and assigned a definite diagnosis, which was considered the gold standard for assessing the diagnostic performance of BNP. RESULTS: The mean BNP concentration was higher in patients with CHF (n=36) than in those with other diagnoses (990±550 vs 80±67 pg/mL, p<0.0001). Patients with systolic dysfunction had higher BNP levels than those with preserved systolic function (1,180±641 vs 753±437 pg/mL, p=0.03). At a blood concentration of 200 pg/mL, BNP showed a sensitivity of 100%, specificity of 97.1%, positive predictive value of 97.3%, and negative predictive value of 100%. The application of BNP could have potentially corrected all 16 cases in which the diagnosis was missed by the emergency department physician. CONCLUSION: BNP measurement is a useful tool in the diagnosis of CHF in patients presenting to the ED with dyspnea.

Biomedical nanoparticles modulate specific CD4(+) T cell stimulation by inhibition of antigen processing in dendritic cells

Understanding how nanoparticles may affect immune responses is an essential prerequisite to developing novel clinical applications. To investigate nanoparticle-dependent outcomes on immune responses, dendritic cells (DCs) were treated with model biomedical poly(vinylalcohol)-coated super-paramagnetic iron oxide nanoparticles (PVA-SPIONs). PVA-SPIONs uptake by human monocyte-derived DCs (MDDCs) was analyzed by flow cytometry (FACS) and advanced imaging techniques. Viability, activation, function, and stimulatory capacity of MDDCs were assessed by FACS and an in vitro CD4(+) T cell assay. PVA-SPION uptake was dose-dependent, decreased by lipopolysaccharide (LPS)-induced MDDC maturation at higher particle concentrations, and was inhibited by cytochalasin D pre-treatment. PVA-SPIONs did not alter surface marker expression (CD80, CD83, CD86, myeloid/plasmacytoid DC markers) or antigen-uptake, but decreased the capacity of MDDCs to process antigen, stimulate CD4(+) T cells, and induce cytokines. The decreased antigen processing and CD4(+) T cell stimulation capability of MDDCs following PVA-SPION treatment suggests that MDDCs may revert to a more functionally immature state following particle exposure.