New information processing methods for control in magnetically confinement nuclear fusion

Thermonuclear plasmas are complex and highly non-linear physical objects and therefore, in the most advanced present day devices for the study of magnetic confinement fusion, thousands of signals have to be acquired for each experiment, in order to progress with the understanding indispensable for the final reactor. On the other hand, the resulting massive databases, more than 40 Tbytes in the case of the JET joint Undertaking, pose significant problems. In this paper, solutions to reduce the shear amount of data by different compression techniques and adaptive sampling frequency architectures are presented. As an example of methods capable of providing significant help in the data analysis and real time control, a Classification and Regression Tree software is applied to the problem of regime identification, to discriminate in an automatic way whether the plasma is in the L or H confinement mode

Removal of Hg from Real Polluted Sediments Using Enhanced-EK Decontamination: Verification of Experimental Methods and Batch-Test Preliminary Results

The aim of the research is to apply a biosurfactant-enhanced-EK technology to marine sediment contaminated by high level of Hg. In this work, data from batch-tests using different novel biosurfactant agents were reported. In addition, a dedicated EK bench-scale apparatus was designed and carried out. Technical test was also performed to evaluate the optimal operating features of the EK bench-scale apparatus, assessing the influence of applied voltage and treatment time on the current intensity and electroosmotic flow. Batch experiments were conducted using two sugar esters as biosurfactants and EDTA salt at different concentrations. Results showed that the maximum extraction efficiency was observed for the biosurfactant Olimpicon GC (15%), for which the Hg extraction was shown to be 3.6-fold higher than for 0.2 M EDTA. From technical tests, the observed reduction of current intensity and electroosmotic flow with time highlights the necessity of using conditioning agents during the treatment. Data demonstrates also the good working features of the experimental apparatus. Preliminary results show that EK treatment jointly with biosurfactants such as sugar esters could be a better choice for the remediation of Hg-polluted sediments. The results obtained are of scientific and practical interest and can be used for further researches

Development of a performance threshold approach for identifying the management options for stabilisation/solidification of lead polluted soils

Two soils spiked with lead at different rates were stabilised/solidified using Portland cement and fy ash at different soil:binder ratios, and tested for their setting time, unconfined compressive strength, leachability and durability. A performance threshold approach was used in order to identify optimal management options for the products of the S/S treatment. Results show that soil texture, percentage of binders and lead concentration play an important part in the treatment, significantly influencing the performance of the resulting products in terms of curing, compressive strength and durability. Pb soil concentrations higher than 15000 mg kg-1 were found to heavily reduce the applicability of the treatment requiring the maximum amount of binder in order to satisfy the performance criteria. Te performance of sandy soils was shown to be limited by setting time and UCS features due to the retardation of the hydration reactions and also by its leaching behaviour, whereas for silt-clayey soils the critical parameter is the mechanical resistance

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

An advanced disruption predictor for JET tested in a simulated real-time environment

Disruptions are sudden and unavoidable losses of confinement that may put at risk the integrity of a tokamak. However, the physical phenomena leading to disruptions are very complex and non-linear and therefore no satisfactory model has been devised so far either for their avoidance or their prediction. For this reason, machine learning techniques have been extensively pursued in the last years. In this paper a real-time predictor specifically developed for JET and based on support vector machines is presented. The main aim of the present investigation is to obtain high recognition rates in a real-time simulated environment. To this end the predictor has been tested on the time slices of entire discharges exactly as in real world operation. Since the year 2000, the experiments at JET have been organized in campaigns named sequentially beginning with campaign C1. In this paper results from campaign C1 (year 2000) and up to C19 (year 2007) are reported. The predictor has been trained with data from JET???s campaigns up to C7 with particular attention to reducing the number of missed alarms, which are less than 1%, for a test set of discharges from the same campaigns used for the training. The false alarms plus premature alarms are of the order of 6.4%, for a total success rate of more than 92%. The robustness of the predictor has been proven by testing it with a wide subset of shots of more recent campaigns (from C8 to C19) without any retraining. The success rate over the period between C8 and C14 is on average 88% and never falls below 82%, confirming the good generalization capabilities of the developed technique. After C14, significant modifications were implemented on JET and its diagnostics and consequently the success rates of the predictor between C15 and C19 decays to an average of 79%. Finally, the performance of the developed detection system has been compared with the predictions of the JE

Unbiased and non-supervised learning methods for disruption prediction at JET

The importance of predicting the occurrence of disruptions is going to increase significantly in the next generation of tokamak devices. The expected energy content of ITER plasmas, for example, is such that disruptions could have a significant detrimental impact on various parts of the device, ranging from erosion of plasma facing components to structural damage. Early detection of disruptions is therefore needed with evermore increasing urgency. In this paper, the results of a series of methods to predict disruptions at JET are reported. The main objective of the investigation consists of trying to determine how early before a disruption it is possible to perform acceptable predictions on the basis of the raw data, keeping to a minimum the number of 'ad hoc' hypotheses. Therefore, the chosen learning techniques have the common characteristic of requiring a minimum number of assumptions. Classification and Regression Trees (CART) is a supervised but, on the other hand, a completely unbiased and nonlinear method, since it simply constructs the best classification tree by working directly on the input data. A series of unsupervised techniques, mainly K-means and hierarchical, have also been tested, to investigate to what extent they can autonomously distinguish between disruptive and non-disruptive groups of discharges. All these independent methods indicate that, in general, prediction with a success rate above 80% can be achieved not earlier than 180???ms before the disruption. The agreement between various completely independent methods increases the confidence in the results, which are also confirmed by a visual inspection of the data performed with pseudo Grand Tour algorithms

Clinical outcomes of patients with complicated post-operative course after gastrectomy for cancer: a GIRCG study using the GASTRODATA registry

Gastrectomy for gastric cancer is still performed in Western countries with high morbidity and mortality. Post-operative complications are frequent, and effective diagnosis and treatment of complications is crucial to lower the mortality rates. In 2015, a project was launched by the EGCA with the aim of building an agreement on list and definitions of post-operative complications specific for gastrectomy. In 2018, the platform www.gastrodata.org was launched for collecting cases by utilizing this new complication list. In the present paper, the Italian Research Group for Gastric Cancer endorsed a collection of complicated cases in the period 2015–2019, with the aim of investigating the clinical pictures, diagnostic modalities, and treatment approaches, as well as outcome measures of patients experiencing almost one post-operative complication. Fifteen centers across Italy provided 386 cases with a total of 538 complications (mean 1.4 complication/patient). The most frequent complications were non-surgical infections (gastrointestinal, pulmonary, and urinary) and anastomotic leaks, accounting for 29.2% and 17.3% of complicated patients, with a median Clavien–Dindo score of II and IIIB, respectively. Overall mortality of this series was 12.4%, while mortality of patients with anastomotic leak was 25.4%. The clinical presentation with systemic septic signs, the timing of diagnosis, and the hospital volume were the most relevant factors influencing outcome

Mapping the prion protein distribution in marsupials: insights from comparing opossum with mouse CNS

The cellular form of the prion protein (PrP(C)) is a sialoglycoprotein widely expressed in the central nervous system (CNS) of mammalian species during neurodevelopment and in adulthood. The location of the protein in the CNS may play a role in the susceptibility of a species to fatal prion diseases, which are also known as the transmissible spongiform encephalopathies (TSEs). To date, little is known about PrP(C) distribution in marsupial mammals, for which no naturally occurring prion diseases have been reported. To extend our understanding of varying PrP(C) expression profiles in different mammals we carried out a detailed expression analysis of PrP(C) distribution along the neurodevelopment of the metatherian South American short-tailed opossum (Monodelphis domestica). We detected lower levels of PrP(C) in white matter fiber bundles of opossum CNS compared to mouse CNS. This result is consistent with a possible role for PrP(C) in the distinct neurodevelopment and neurocircuitry found in marsupials compared to other mammalian species

Relationship of edge localized mode burst times with divertor flux loop signal phase in JET

A phase relationship is identified between sequential edge localized modes (ELMs) occurrence times in a set of H-mode tokamak plasmas to the voltage measured in full flux azimuthal loops in the divertor region. We focus on plasmas in the Joint European Torus where a steady H-mode is sustained over several seconds, during which ELMs are observed in the Be II emission at the divertor. The ELMs analysed arise from intrinsic ELMing, in that there is no deliberate intent to control the ELMing process by external means. We use ELM timings derived from the Be II signal to perform direct time domain analysis of the full flux loop VLD2 and VLD3 signals, which provide a high cadence global measurement proportional to the voltage induced by changes in poloidal magnetic flux. Specifically, we examine how the time interval between pairs of successive ELMs is linked to the time-evolving phase of the full flux loop signals. Each ELM produces a clear early pulse in the full flux loop signals, whose peak time is used to condition our analysis. The arrival time of the following ELM, relative to this pulse, is found to fall into one of two categories: (i) prompt ELMs, which are directly paced by the initial response seen in the flux loop signals; and (ii) all other ELMs, which occur after the initial response of the full flux loop signals has decayed in amplitude. The times at which ELMs in category (ii) occur, relative to the first ELM of the pair, are clustered at times when the instantaneous phase of the full flux loop signal is close to its value at the time of the first ELM