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

A Data Set and a Convolutional Model for Iconography Classification in Paintings

Iconography in art is the discipline that studies the visual content of artworks to determine their motifs and themes andto characterize the way these are represented. It is a subject of active research for a variety of purposes, including the interpretation of meaning, the investigation of the origin and diffusion in time and space of representations, and the study of influences across artists and art works. With the proliferation of digital archives of art images, the possibility arises of applying Computer Vision techniques to the analysis of art images at an unprecedented scale, which may support iconography research and education. In this paper we introduce a novel paintings data set for iconography classification and present the quantitativeand qualitative results of applying a Convolutional Neural Network (CNN) classifier to the recognition of the iconography of artworks. The proposed classifier achieves good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73% Average Precision) in the task of identifying saints in Christian religious paintings, a task made difficult by the presence of classes with very similar visual features. Qualitative analysis of the results shows that the CNN focuses on the traditional iconic motifs that characterize the representation of each saint and exploits such hints to attain correct identification. The ultimate goal of our work is to enable the automatic extraction, decomposition, and comparison of iconography elements to support iconographic studies and automatic art work annotation.Comment: Published at ACM Journal on Computing and Cultural Heritage (JOCCH) https://doi.org/10.1145/345888

Experimental Validation of a Numerical Model for the Prediction of the Vulcanization Degree of a Fiber Reinforced Elastomeric Isolator (frei)

Seismic isolation of a structure can be achieved by interposing a rubber device between the foundation and superstructure, which increases the period of the superstructure, resulting in a structure relatively transparent to seismic excitation. Alternating layers of rubber pads and steel laminas or Fiber Reinforced Polymer (FRP) dry textiles suitable treated, typically constitute an elastomeric seismic isolator. The rubber should be processed through several stages to be ready for structural application. One of the most critical is vulcanization. During this stage, rubber is heated with sulfur or peroxides, accelerators, and activators at around 130-160°C. This process triggers the formation of cross-links between long rubber molecules, creating the so called polymer network. The chains are prevented from sliding along each other thanks to cross-links, and the rubber becomes elastic. This study proposes a combined numerical and experimental approach to predict the vulcanization degree for a Fiber-Reinforced Elastomeric Isolator (FREI). According to the numerical results, two typologies of vulcanization have been considered: one suboptimal at 145°C for 5400 seconds and one optimal at 145°C for 7200 seconds. Results, in terms of Shore A hardness measurements, have shown a non-homogeneous distribution within the isolator suboptimal vulcanized. Instead, as expected, the hardness distribution is homogeneous for the optimal one

A Numerical Model for the Prediction of Vulcanization Degree of a Fiber Reinforced Elastomeric Isolator (FREI) Taking Into Account the Induction Time

The rubber material is widely used either for household or industrial needs. Since the prehistoric era, rubber has been involved in human life by exploiting the latex from specific trees. For elastomeric isolators, rubber pads have a central role. Damping performance is a prerequisite for isolation-bearing materials. Besides, the materials must have an excellent overall performance, such as high strength to resist damage. From a chemical point of view, it is paramount that the rubber used for assembling the devices is vulcanized correctly. It is crucial to determine the optimal vulcanization times and temperatures to properly create the polymer network and make the rubber capable of exhibiting good mechanical properties at large strains applied. All rubber mechanical properties are strongly affected by vulcanization. This study proposes a numerical model to predict the degree of vulcanization of a Fiber-Reinforced Elastomeric Isolator (FREI) made of a Natural Rubber (NR) – Ethylene Propylene Diene Monomer (EPDM) blend. The aim is to determine the optimal vulcanization time and temperature, taking the induction time into account, to obtain a homogeneous curing level distribution within the isolator

Keplerian integrals, elimination theory and identification of very short arcs in a large database of optical observations

Modern asteroid surveys produce an increasingly large number of observations, which are grouped into very short arcs (VSAs) each containing a few observations of the same object in one single night. To decide whether two VSAs collected in different nights correspond to the same observed object we can attempt to compute an orbit with the observations of both arcs: this is called the linkage problem. Since the number of linkages to be attempted is very large, we need efficient methods of orbit determination. Using the first integrals of Kepler’s motion we can write algebraic equations for the linkage problem, which can be put in polynomial form. In Gronchi et al. (Celest Mech Dyn Astron 123(2):105–122, 2015) these equations are reduced to a polynomial equation of degree 9: the unknown is the topocentric distance of the observed body at the mean epoch of one VSA. Here we derive the same equations in a more concise way, and show that the degree 9 is optimal in a sense that will be specified in Sect. 3.3. We also introduce a procedure to join three VSAs: from the conservation of angular momentum we obtain a polynomial equation of degree 8 in the topocentric distance at the mean epoch of the second VSA. For both identification methods, with two and three VSAs, we discuss how to discard solutions. Finally, we present some numerical tests showing that the new methods give satisfactory results and can be used also when the time separation between the VSAs is large. The low polynomial degree of the new methods makes them well suited to deal with the very large number of asteroid observations collected by the modern surveys

Low cost frictional seismic base-isolation of residential new masonry buildings in developing countries: A small masonry house case study

Introduction: An advanced Finite Element model is presented to examine the performance of a low-cost friction based-isolation system in reducing the seismic vulnerability of low-class rural housings. This study, which is mainly numerical, adopts as benchmark an experimental investigation on a single story masonry system eventually isolated at the base and tested on a shaking table in India. Methods: Four friction isolation interfaces, namely, marble-marble, marble-high-density polyethylene, marble-rubber sheet, and marblegeosynthetic were involved. Those interfaces differ for the friction coefficient, which was experimentally obtained through the aforementioned research. The FE model adopted here is based on a macroscopic approach for masonry, which is assumed as an isotropic material exhibiting damage and softening. The Concrete damage plasticity (CDP) model, that is available in standard package of ABAQUS finite element software, is used to determine the non-linear behavior of the house under non-linear dynamic excitation. Results and Conclusion: The results of FE analyses show that the utilization of friction isolation systems could much decrease the acceleration response at roof level, with a very good agreement with the experimental data. It is also found that systems with marble-marble and marblegeosynthetic interfaces reduce the roof acceleration up to 50% comparing to the system without isolation. Another interesting result is that there was little damage appearing in systems with frictional isolation during numerical simulations. Meanwhile, a severe state of damage was clearly visible for the system without isolation

Exploring the biodiversity of Bifidobacterium asteroides among honey bee microbiomes

Bifidobacterium asteroides is considered the ancestor of the genus Bifidobacterium, which has evolved in close touch with the hindgut of social insects. However, recent studies revealed high intraspecies biodiversity within this taxon, uncovering the putative existence of multiple bifidobacterial species, thus, suggesting its reclassification. Here, a genomic investigation of 98 B. asteroides-related genomes retrieved from public repositories and reconstructed from metagenomes of the hindgut of Apis mellifera and Apis cerana was performed to shed light on the genetic variability of this taxon. Phylogenetic and genomic analyses revealed the existence of eight clusters, of which five have been recently characterized with a representative type strain of the genus and three were represented by putative novel bifidobacterial species inhabiting the honeybee gut. Then, the dissection of 366 shotgun metagenomes of honeybee guts revealed a pattern of seven B. asteroides-related taxa within A. mellifera that co-exist with the host, while A. cerana microbiome was characterized by the predominance of one of the novel species erroneously classified as B. asteroides. A further glycobiome analysis unveiled a conserved repertoire of glycosyl hydrolases (GHs) reflecting degradative abilities towards a broad range of simple carbohydrates together with genes encoding specific GHs of each B. asteroides-related taxa

Genetic insights into the dark matter of the mammalian gut microbiota through targeted genome reconstruction

Whole metagenomic shotgun (WMS) sequencing has dramatically enhanced our ability to study microbial genomics. The possibility to unveil the genetic makeup of bacteria that cannot be easily isolated has significantly expanded our microbiological horizon. Here, we report an approach aimed at uncovering novel bacterial species by the use of targeted WMS sequencing. Employing in silico data retrieved from metabolic modelling to formulate a chemically defined medium (CDM), we were able to isolate and subsequently sequence the genomes of six putative novel species of bacteria from the gut of non-human primates.We thank GenProbio srl for the financial support of the Laboratory of Probiogenomics. Part of this research is conducted using the High Performance Computing (HPC) facility of the University of Parma. D.v.S. is a member of The APC Microbiome Institute funded by Science Foundation Ireland (SFI), through the Irish Government's National Development Plan (Grant numbers SFI/12/RC/2273a and SFI/12/RC/2273b). This work was financially supported by a PostDoc fellowship (Bando Ricerca Finalizzata) to G.A. F.T. is funded by Italian Ministry of Health through the Bando Ricerca Finalizzata (Grant Number GR-2018-12365988)

Módulo I: subsistema soporte de diseño de producto

El objetivo de nuestro Grupo de Investigación y Desarrollo es analizar, diseñar y desarrollar un prototipo generador de Sistemas Soporte de Decisiones (SSD) con capacidad de adaptación a diferentes estructuras organizativas de la empresa, con flexibilidad para incorporar cambios de los productos y de los procesos de producción y ofrecer soporte a todos los puntos de decisión de la organización. En este trabajo presentamos una descripción del subsistema de soporte de la actividad de diseño de producto. Este subsistema es uno de los doce módulos que integran el prototipo, y uno de los cinco que actualmente están siendo desarrollados por nuestro grupo. En particular presentamos la estrategia diseñada para el manejo de la información asociada al producto y una descripción del análisis y diseño orientado a objetos del subsistema.Eje: Ingeniería de software. Bases de datosRed de Universidades con Carreras en Informática (RedUNCI

Módulo I: subsistema soporte de diseño de producto

El objetivo de nuestro Grupo de Investigación y Desarrollo es analizar, diseñar y desarrollar un prototipo generador de Sistemas Soporte de Decisiones (SSD) con capacidad de adaptación a diferentes estructuras organizativas de la empresa, con flexibilidad para incorporar cambios de los productos y de los procesos de producción y ofrecer soporte a todos los puntos de decisión de la organización. En este trabajo presentamos una descripción del subsistema de soporte de la actividad de diseño de producto. Este subsistema es uno de los doce módulos que integran el prototipo, y uno de los cinco que actualmente están siendo desarrollados por nuestro grupo. En particular presentamos la estrategia diseñada para el manejo de la información asociada al producto y una descripción del análisis y diseño orientado a objetos del subsistema.Eje: Ingeniería de software. Bases de datosRed de Universidades con Carreras en Informática (RedUNCI