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This paper continues the publication cycle on developing the ion plasma electrically powered spacecraft propulsion (EPSP) of the spacecraft. For monitoring and control of the EPSP operation, a feedback system based on a signal proportional to the EPSP plasma radiation intensity has been proposed to be used. It was assumed that the radiation intensity in the ultraviolet, visible and infrared ranges being proportional to the instantaneous thrust value of the EPSP. Accordingly, the introduction of a signal from the radiation registration detector into the feedback loop should allow to create an onboard closed system for monitoring and control of the EPSP operation. A photodetector based on a dynamic pin-diode integrator was considered for use in this system.

A number of characteristics of ionic and ion-plasma accelerators laboratory samples designed for electrically powered spacecraft propulsion were investigated in the experimental research. In a large-sized vacuum chamber (2.4 m^3, 10^-3 Pa) in the MCA named after A.F. Mozhaysky provided the necessary physical and technological processes, methods and means of measurement, parameters and operation modes of ion accelerators with contact ionization were provided in a large-sized vacuum chamber (2.4 m3, 10-3 Pa) in A.F.Mozhaysky`s Military-Space Academy. The theoretical analysis, including by computer simulation, of samples design features, as well as physical processes and operating parameters was carried out. It is established that the implemented and tested measurement methods, technologies, and ion-physical laboratory samples characteristics correspond to the tasks of developing promising electrically powered spacecraft propulsion.

Crop losses are a major threat to the wellbeing of rural families, to the economy and governments, and to food security worldwide. The goal of our research is to develop a multi-functional platform to help the farming community to tilt against plant diseases. In our previous works, we reported about the creation of a special database of healthy and diseased plants’ leaves consisting of five sets of grapes images and proposed a special classification model based on a deep siamese network followed by k-nearest neighbors (KNN) classifier. Then we extended our database to five sets of images for grape, corn, and wheat – 611 images in total. Since after this extension the classification accuracy decreased to 86 %, we propose in this paper a novel architecture with a deep siamese network as feature extractor and a single-layer perceptron as a classifier that results in a significant gain of accuracy, up to 96 %.

Three deep tracking methods are presented for the BM@N experiment GEM track detector, which differ in their concepts. The first is a two-stage method with data preprocessing by a directional search in the k-d tree to find all possible candidates for tracks, and then use a deep recurrent neural network to classify them by true and ghost tracks. The second end-to-end method used a deep recurrent neural network to extrapolate the initial tracks, similar to the Kalman filter, which learns necessary parameters from the data. The third method implements our new attempt to adapt the neural graph network approach developed in the HEP.TrkX project at CERN to GEM-specific data. The results of applying these three methods to simulated events are presented.

One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes. Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip in GEM detectors due to the appearance in them a lot of fake hits caused by extra spurious crossings of fired strips. Since the number of those fakes is several orders of magnitude greaterthan for true hits, one faces with the quite serious difficulty to unravelpossible track-candidates via true hits ignoring fakes. On the basis of our previous two-stage approach based on hits preprocessing using directed K-d tree search followed by a deep neural classifier we introduce here two new tracking algorithms. Both algorithms combine those two stages in one while using different types of deep neural nets. We show that both proposed deep networks do not require any special preprocessing stage, are more accurate, faster and can be easier parallelized. Preliminary results of our new approaches for simulated events are presented.

One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes. Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip and GEM detectors due to appearance in them a lot of fake hits caused by extra spurious crossings of fired strips. Since the number of those fakes is several orders of magnitude greater than for true hits, one faces with the quite serious difficulty to unravel possible track-candidates via true hits ignoring fakes. We introduce a renewed method that is a significant improvement of our previous two-stage approach based on hit preprocessing using directed K-d tree search followed a deep neural classifier. We combine these two stages in one by applying recurrent neural network that simultaneously determines whether a set of points belongs to a true track or not and predicts where to look for the next point of track on the next coordinate plane of the detector. We show that proposed deep network is more accurate, faster and does not require any special preprocessing stage. Preliminary results of our approach for simulated events of the BM@N GEM detector are presented.

Cloud computing has become a routine tool for scientists in many fields. The JINR cloud infrastructure provides JINR users with computational resources to perform various scientific calculations. In order to speed up achievements of scientific results the JINR cloud service for parallel applications has been developed. It consists of several components and implements a flexible and modular architecture which allows to utilize both more applications and various types of resources as computational backends. An example of using the Cloud&HybriLIT resources in scientific computing is the study of superconducting processes in the stacked long Josephson junctions (LJJ). The LJJ systems have undergone intensive research because of the perspective of practical applications in nano-electronics and quantum computing. In this contribution we generalize the experience in application of the Cloud&HybriLIT resources for high performance computing of physical characteristics in the LJJ system.

The article discloses the conceptual apparatus of innovation for the knowledge of its nature in order to improve the quality of governance. An analysis of the conceptual apparatus in the retrospective aspect allowed to assess the process of innovation at different stages of economic development. The comparative characteristic of interpretations of "innovation" and "innovation" in order to understand their nature and the definition of scientific approaches to the control, as a separate step of the innovation process, as well as a set of elements to obtain economic benefits.