Decisive rule experimental studies to detect objects on the background of nhe earth surface using polarization differences of radar signals

The task of stationary and moving object detection against the background of the underlying surface (earth) by radar means becomes relevant. A decision rule is being developed to detect objects against the background of the earth surface using the polarization differences reflected from the earth and the objects of radar signals, represented as a measurement vecto

ПЕРСПЕКТИВЫ НЕЧЕТКО-МНОЖЕСТВЕННЫХ ОПИСАНИЙ ИННОВАЦИОННЫХ РИСКОВ

The authors have summarized the shortcomings of traditional methods of risk assessment based on probabilistic approaches, identifi ed the advantages of fuzzy multiple descriptions. Justifi ed the transition to non-Markov paradigm research of innovative risk. The implementation of innovative strategies takes place inRussiaunder development trends of globalization, nonlinearity and nonequilibrium external and internal environment that requires a new understanding of the management of high risk and innovative insurance in ensuring the sustainability of modern enterprises. Under these conditions, the problem also updated the competitiveness of enterprises, based on their innovative activity. However, a number of researchers have noted inconsistencies in practice innovative risk management mechanisms and insurance management innovation in enterprises, as well as the lack of development of management problems high innovation risk that the objective obstacles increase innovation activity of the domestic economy and its post-industrial development.Авторами обобщены недостатки традиционных методов оценки рисков на основе вероятностных подходов, выявлены преимущества метода нечетко-множественных описаний. Обоснован переход к немарковской парадигме исследований инновационного риска. Реализация инновационной стратегии России проходит в условиях развития тенденций глобализации, нелинейности и неравновесности внешней и внутренней среды, что требует нового понимания особенностей управления высоким инновационным риском и страхования в обеспечении устойчивости современных предприятий. В этих условиях также актуализируется проблема конкурентоспособности предприятий, основанной на их инновационной активности. Вместе с тем, ряд исследователей отмечает несогласованность на практике механизмов управления инновационным риском и его страхования с управлением инновационной деятельностью на предприятиях, а также недостаточную разработку проблем управления высоким инновационным риском, что объективно препятствует повышению инновационной активности отечественной экономики и ее постиндустриальному развитию

Earliness and morphotypes of common wheat cultivars of Western and Eastern Siberia

The global and local climate changes determine the producing of highly-adaptive common (bread) wheat commercial cultivars of a new generation whose optimal earliness matches the climatic features of the territory where the cultivars are farmed. Principal component analysis involving our own and published data has been applied to investigate 98 commercial common wheat cultivars from Western and Eastern Siberia comparing their morphotypes; cultivar zoning time; length of the vegetation period; 1000-grain weight, and inheritance of spring growth habit. It demonstrated that the dominant Vrn gene polymorphism determining the spring growth habit of the Siberian cultivars was minimally polymorphic. In 75 % of the tested cultivars, the spring growth habit was controlled by digenic, namely dominant Vrn-A1 and Vrn-B1 genes. In 25 % of them (24 cultivars), spring growth habit is controlled by a single gene. In 19 and 5 of these cultivars spring growth habit is controlled by only one dominant gene, Vrn-B1 or Vrn-A1, respectively. In cv. Tulun 15, a trigenic control was identified. A conclusion about the optimality of the digenic control for the climatic conditions of both Western and Eastern Siberia has been confirmed. However, since none of the tested cultivars had the dominant Vrn-D1 gene typical of the regions of China and Central Asia bordering Siberia, it can be considered as an additional argument in favor of the European origin of Siberian common wheat cultivars. The revealed high frequency of the Vrn-B1c allele in the Western Siberian cultivars and the Vrn-B1a allele in the Eastern Siberian cultivars suggests their selectivity. The analysis also confirmed the dominance of red glume (ferrugineum, milturum) and awned spike (ferrugineum, erythrospermum) varieties in the Eastern Siberian cultivars, and white glume and awnedless spike (lutescens and albidum) ones in the Western Siberian cultivars. Small grain size cultivars are more typical of Eastern than Western Siberia. The retrospective analysis based on the cultivars’ zoning time included in the “State Register for Selection Achievements Admitted for Usage” brought us to the conclusion that the earliness/lateness of modern Siberian commercial cultivars was not regionally but rather zonally-associated (taiga, subtaiga, forest-steppe and steppe zones)

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

Modelling of the effect of ELMs on fuel retention at the bulk W divertor of JET

Effect of ELMs on fuel retention at the bulk W target of JET ITER-Like Wall was studied with multi-scale calculations. Plasma input parameters were taken from ELMy H-mode plasma experiment. The energetic intra-ELM fuel particles get implanted and create near-surface defects up to depths of few tens of nm, which act as the main fuel trapping sites during ELMs. Clustering of implantation-induced vacancies were found to take place. The incoming flux of inter-ELM plasma particles increases the different filling levels of trapped fuel in defects. The temperature increase of the W target during the pulse increases the fuel detrapping rate. The inter-ELM fuel particle flux refills the partially emptied trapping sites and fills new sites. This leads to a competing effect on the retention and release rates of the implanted particles. At high temperatures the main retention appeared in larger vacancy clusters due to increased clustering rate

New H-mode regimes with small ELMs and high thermal confinement in the Joint European Torus

New H-mode regimes with high confinement, low core impurity accumulation, and small edge-localized mode perturbations have been obtained in magnetically confined plasmas at the Joint European Torus tokamak. Such regimes are achieved by means of optimized particle fueling conditions at high input power, current, and magnetic field, which lead to a self-organized state with a strong increase in rotation and ion temperature and a decrease in the edge density. An interplay between core and edge plasma regions leads to reduced turbulence levels and outward impurity convection. These results pave the way to an attractive alternative to the standard plasmas considered for fusion energy generation in a tokamak with a metallic wall environment such as the ones expected in ITER.& nbsp;Published under an exclusive license by AIP Publishing

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.Confining plasma and managing disruptions in tokamak devices is a challenge. Here the authors demonstrate a method predicting and possibly preventing disruptions and macroscopic instabilities in tokamak plasma using data from JET

Overview of JET results for optimising ITER operation

The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (α) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER

Shattered pellet injection experiments at JET in support of the ITER disruption mitigation system design

A series of experiments have been executed at JET to assess the efficacy of the newly installed shattered pellet injection (SPI) system in mitigating the effects of disruptions. Issues, important for the ITER disruption mitigation system, such as thermal load mitigation, avoidance of runaway electron (RE) formation, radiation asymmetries during thermal quench mitigation, electromagnetic load control and RE energy dissipation have been addressed over a large parameter range. The efficiency of the mitigation has been examined for the various SPI injection strategies. The paper summarises the results from these JET SPI experiments and discusses their implications for the ITER disruption mitigation scheme

Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision