Neural plasma

This paper presents a novel type of artificial neural network, called neural plasma, which is tailored for classification tasks involving few observations with a large number of variables. Neural plasma learns to adapt its classification confidence by generating artificial training data as a function of its confidence in previous decisions. In contrast to multilayer perceptrons and similar techniques, which are inspired by topological and operational aspects of biological neural networks, neural plasma is motivated by aspects of high-level behavior and reasoning in the presence of uncertainty. The basic principles of the proposed model apply to other supervised learning algorithms that provide explicit classification confidence values. The empirical evaluation of this new technique is based on benchmarking experiments involving data sets from biotechnology that are characterized by the small-n-large-p problem. The presented study exposes a comprehensive methodology and is seen as a first step in exploring different aspects of this methodology.IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural NetsRed de Universidades con Carreras en Informática (RedUNCI

Catalytic-Dielectric Barrier Discharge Plasma Reactor For Methane and Carbon Dioxide Conversion

A catalytic - DBD plasma reactor was designed and developed for co-generation of synthesis gas and C2+ hydrocarbons from methane. A hybrid Artificial Neural Network - Genetic Algorithm (ANN-GA) was developed to model, simulate and optimize the reactor. Effects of CH4/CO2 feed ratio, total feed flow rate, discharge voltage and reactor wall temperature on the performance of catalytic DBD plasma reactor was explored. The Pareto optimal solutions and corresponding optimal operating parameters ranges based on multi-objectives can be suggested for catalytic DBD plasma reactor owing to two cases, i.e. simultaneous maximization of CH4 conversion and C2+ selectivity, and H2 selectivity and H2/CO ratio. It can be concluded that the hybrid catalytic DBD plasma reactor is potential for co-generation of synthesis gas and higher hydrocarbons from methane and carbon dioxide and showed better than the conventional fixed bed reactor with respect to CH4 conversion, C2+ yield and H2 selectivity for CO2 OCM process

Developement of real time diagnostics and feedback algorithms for JET in view of the next step

Real time control of many plasma parameters will be an essential aspect in the development of reliable high performance operation of Next Step Tokamaks. The main prerequisites for any feedback scheme are the precise real-time determination of the quantities to be controlled, requiring top quality and highly reliable diagnostics, and the availability of robust control algorithms. A new set of real time diagnostics was recently implemented on JET to prove the feasibility of determining, with high accuracy and time resolution, the most important plasma quantities. With regard to feedback algorithms, new model–based controllers were developed to allow a more robust control of several plasma parameters. Both diagnostics and algorithms were successfully used in several experiments, ranging from H-mode plasmas to configuration with ITBs. Since elaboration of computationally heavy measurements is often required, significant attention was devoted to non-algorithmic methods like Digital or Cellular Neural/Nonlinear Networks. The real time hardware and software adopted architectures are also described with particular attention to their relevance to ITER.Comment: 12th International Congress on Plasma Physics, 25-29 October 2004, Nice (France

Effect of Intraduodenal Bile and Na-Taurodeoxycholate on Exocrine Pancreatic Secretion and on Plasma Levels of Secretin, Pancreatic Polypeptide, and Gastrin in Man

The effect of intraduodenally administered cattle bile (CB) and Na-taurodeoxycholate (TDC) on basal pancreatic secretion and plasma levels of secretin, pancreatic polypeptide (PP), and gastrin were investigated on two separate days in 10 fasting volunteers. Doses of 2-6 g CB and 20&600 mg TDC were given intraduodenally at 65-min intervals. Volume, bicarbonate, lipase, trypsin, amylase, and bilirubin were measured in 10-min fractions of duodenal juice, and GI peptides determined by radioimmunoassay. CB and TDC enhanced significantly and dose-dependently volume, bicarbonate and enzyme secretion, and plasma secretin and PP levels. In contrast, plasma gastrin showed only a marginal increase. We conclude that the hydrokinetic effect of intraduodenal CB and TDC is at least partially mediated by secretin. Gastrin could be ruled out as a mediator of the ecbolic effect, whereas other GI peptides, primarily CCK, and/or neural mechanisms must be considered possible mediators. Both pathways may also play a role in the PP release

Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors

While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for them to be economical. A significant bottleneck towards applying more advanced control algorithms is the need for better plasma simulation, where both physics-based and data-driven approaches currently fall short. The former is bottle-necked by both computational cost and the difficulty of modelling plasmas, and the latter is bottle-necked by the relative paucity of data. To address this issue, this work applies the neural ordinary differential equations (ODE) framework to the problem of predicting a subset of plasma dynamics, namely the coupled plasma current and internal inductance dynamics. As the neural ODE framework allows for the natural inclusion of physics-based inductive biases, we train both physics-based and neural network models on data from the Alcator C-Mod fusion reactor and find that a model that combines physics-based equations with a neural ODE performs better than both existing physics-motivated ODEs and a pure neural ODE model

Maternal plasma docosahexaenoic acid (DHA) concentrations increase at the critical time of neural tube closure

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