Western Siberia is exposed to extreme wind events caused by severe convective storms. However, our knowledge on such storms in Siberia is still fragmentary compared to other parts of the world primarily due to the lack of weather radar data. These storms cause substantial damage, which signifies the need for comprehensive assessment of their characteristics and predictability even on the basis of existing data. In this paper, we present a case study analysis of a severe weather outbreak that occurred on 25–26 May 2020 in Western Siberia, during a record six-month heatwave that lasted in Siberia from January to June. The outbreak resulted in six fatalities and substantial economic losses. Using various satellite data and damage reports we found that two consecutive mesoscale convective systems (MCSs) developed within the outbreak having an exceptionally long total track about 2000 km and causing large-scale forest damage with a total area of 64.5 km2. Such an exceptionally long path was supported by a strong mid-tropospheric jet, which settled extremely high values of wind shear that fostered the development of the outbreak. To analyze the accuracy of the forecast of the MCS and three associated windstorms on 26 May, we performed a set of simulations with the COSMO and ICON numerical weather prediction models launched with convection-permitting resolution (2.2 km) with different forecast lead times. Both models successfully predicted the most severe windstorm with the <50 km spatial displacement and the <1.5 h time shift, but COSMO generally simulated more strong wind. However, two other windstorms of that day were predicted with lower accuracy. The simulation results revealed a limited sensitivity of the model to variations in initial soil temperature and moisture. Together with the better performance of the forecast with >24 h lead time, this emphasized the predominant role of large-scale dynamics and the minor role of local factors in the outbreak formation and development. In particular, the intrusion of the upper tropospheric high potential vorticity streamer along the blocking periphery induced strong deep convection and determined the severe character of the outbreak. Specifically, the studied outbreak had an exceptional longevity compared to other long-lived windstorms observed in Northern Eurasia at the blocking periphery.

The paper describes the configuration of the COSMO-Ru6Sib system with a horizontal grid spacing of 6.6 km adapted to regional conditions. The COSMO-Ru2Sib configuration with a horizontal grid spacing of 2.2 km is also considered. The results of modifying this configuration using weather radar data assimilation as well as urban canopy parameterization are presented and discussed. The results of numerical experiments for the COSMO-Ru6Sib with the COSMO and ICON-LAM models as compute kernels are also presented.

One of the most common congenital metabolic disorders is familial hypercholesterolemia. Familial hyper-cholesterolemia is a condition caused by a type of genetic defect leading to a decreased rate of removal of low-density lipoproteins from the bloodstream and a pronounced increase in the blood level of total cholesterol. This disease leads to the early development of cardiovascular diseases of atherosclerotic etiology. Familial hypercholesterolemia is a monogenic disease that is predominantly autosomal dominant. Rare pathogenic variants in the LDLR gene are present in 75–85 % of cases with an identified molecular genetic cause of the disease, and variants in other genes (APOB, PCSK9, LDLRAP1, ABCG5, ABCG8, and others) occur at a frequency of < 5 % in this group of patients. A negative result of genetic screening for pathogenic variants in genes of the low-density lipoprotein receptor and its ligands does not rule out a diagnosis of familial hypercholesterolemia. In 20–40 % of cases, molecular genetic testing fails to detect changes in the above genes. The aim of this work was to search for new genes associated with the familial hypercholesterolemia phenotype by modern high-tech methods of sequencing and machine learning. On the basis of a group of patients with familial hypercholesterolemia (enrolled according to the Dutch Lipid Clinic Network Criteria and including cases confirmed by molecular genetic analysis), decision trees were constructed, which made it possible to identify cases in the study population that require additional molecular genetic analysis. Five probands were identified as having the severest familial hypercholesterolemia without pathogenic variants in the studied genes and were analyzed by whole-genome sequencing on the HiSeq 1500 platform (Illumina). The whole-genome sequencing revealed rare variants in three out of five analyzed patients: a heterozygous variant (rs760657350) located in a splicing acceptor site in the PLD1 gene (c.2430-1G>A), a previously undescribed single-nucleotide deletion in the SIDT1 gene [c.2426del (p.Leu809CysfsTer2)], new missense variant c.10313C>G (p.Pro3438Arg) in the LRP1B gene, and single-nucleotide deletion variant rs753876598 [c.165del (p.Ser56AlafsTer11)] in the CETP gene. All these variants were found for the first time in patients with a clinical diagnosis of familial hypercholesterolemia. Variants were identified that may influence the formation of the familial hypercholesterolemia phenotype.

Моногенные нарушения – патологии, которые вызваны изменениями только одного гена. Одним из наиболее распространенных (1:250) моногенных нарушений липидного обмена является семейная гиперхолестеринемия (СГХС) [1]. СГХС приводит к раннему развитию сердечно-сосудистых заболеваний (ССЗ) атеросклеротического генеза [2–4]. Редкие патогенные варианты в гене LDLR определяются в 80–85 % случаев, когда выявлена молекулярно-генетическая причина развития СГХС, варианты в других генах определяются с частотой менее 5 % (APOB, PCSK9, LDLRAP1, ABCG5, ABCG8 и др.) [5, 6]. У лиц с СГХС риск развития ССЗ в 2,5–10 раз выше по сравнению с контрольной группой [7, 8], но в случае диагностики и лечения СГХС в раннем возрасте риск значительно снижается (≈ 80 %) [7]. Активное выявление пациентов с СГХС и применение каскадного скрининга могут помочь обеспечить лечение до начала клинических проявлений ССЗ [9].

This paper presents results of numerical estimates of vertical temperature profiles in the COSMO and ICON NWP models. Data from two vertical profilers, an MTP-5 microwave temperature profiler and a classic AVK-type radiosonde, are used for the estimates. Mean errors, correlation coefficients, and root-mean-square errors are calculated for different seasons. The simulation results are analysed depending on configurations of the horizontal resolution of the models and between the models. The estimates are plotted, and comparisons are made between the models using each of the measurement types: between different measurements for each model. The response to spatial variability in the measurements and model grid cells is assessed. Conclusions are formulated by comparing the quality of reproduction of the atmospheric boundary layer between the models and the model configurations. The findings provide useful information for the interpretation of modeling results by forecasters.

Low cost 3D LiDAR complements cameras in perception algorithms for various robotic applications. Safe and efficient human robot collaboration requires easy and accurate extrinsic calibration of sensors with an industrial robot. This work presents an efficient and accurate method for extrinsic calibration between LiDAR, camera and an industrial robot. Open-box target mounted on robot enables parameter estimation by constraining sensor data to multiple planes, which constitute the target planner surface. The method enables extraction of eight correspondences between the sensors and the robot in each data sample. The method enables speedup in sensor setup and drastically reduces efforts required for data collection through automation. The results have been evaluated for a simulated and real environment.

The article presents annotated dataset designed for training, validation and testing of instance semantic objects’ segmentation algorithms using convolutional neural networks. A feature of the presented dataset is the fact that it consists of real objects images and the images of objects which were synthetically obtained. The data was collected and generated in the different conditions of lighting, camera angle relative to scene objects, position and orientation of objects in the scene, with taking into account overlaps. Annotation or real objects images in the scene are carried out using the developed tool of automatic data annotation. The synthetic data annotation were obtained during the process of generating objects’ images based on the POV-Ray ray tracing technology. Both methods make it possible to create your own qualitatively marked datasets as soon as possible. The paper presents the training and testing results of instance semantic segmentation algorithm, which was implemented on the basis of Mask R-CNN, are presented with using created dataset by model. The dataset is publicly available and can be used for your own research.

Creating quality-annotated dataset is one of the main tasks in the field of deep learning technologies for pattern recognition. However, in the real world, collecting a sufficient number of detailed images of an object is difficult and time-consuming. The article considers an approach to creating synthetic datasets based on the ray tracing method. This paper also presents the results of success tests of real object image segmentation by convolutional neural networks, trained entirely on synthetic data and data of different nature.

In recent years, one of the most important scientific task in robotics is research aimed at developing methods that ensure a safe interaction between a robot and a human. An important aspect of solving this problem is the need to find a solution that allows obtaining maximum recognition accuracy. At the same time, the latest achievements in deep learning open up new possibilities in solving the task of object recognition. In this regard, the purpose of this work is to investigate the algorithms for recognizing human hands on the image, developed on the basis of deep convolutional neural networks.

This paper presents preliminary results of the effectiveness analysis of an air quality forecasting system for the city of Novosibirsk with replenishment of the missing information on emission sources by solving an inverse problem with urban monitoring network data. In solving the inverse problem, a priori information about the location and mode of the sources is used. To simulate concentration distributions, the WRF-Chem model is used, and a simplified model of chemical transport is applied to solving the inverse problem. These models are offline coupled in a hybrid forecast system in order to improve the initial information about the spatial distribution of emission intensity and air quality forecast, respectively. The results of numerical experiments and their analysis are presented. The influence of an urban parameterization on the results of the forecast is shown.

The results of WRF-CHEM model simulation of dispersal of anthropogenic emissions from the Norilsk industrial zone are verified against data of aircraft sensing performed in August 2004. It is shown that the WRF-CHEM v3.5.1 model configuration selected adequately reproduces the meteorological parameters obtained during the 2004 measurement campaign. The model-derived distributions of the concentrations of sulfur anhydride and ozone and mass concentration of aerosol qualitatively reproduce those retrieved from data of aircraft sensing. Quantitative estimates showed that the standard errors for sulfur dioxide, PM 2.5 mass concentration, and ozone, calculated for three flights, had been 23 ppb, 2.6 μg/m 3 , and 9.8 ppb, respectively. These discrepancies may be due to incorrect specification of the initial and boundary conditions, inaccurate specification of anthropogenic emissions, and limitations in the aerosol and chemical descriptions.

The verification of the results of numerical simulation of the distribution of anthropogenic emissions of the Norilsk industrial zone using the WRF-CHEM model using airborne sounding data carried out in 13 August 2004 was carried out. The results of numerical modelling of the distribution of the concentration of sulphur dioxide, ozone and mass concentration of aerosol reproduce qualitatively the distributions obtained during airborne sounding. Quantitative estimates showed that the root-mean-square error for sulphur dioxide, the mass concentration of aerosol PM2.5 and ozone, calculated for all three flights, was 36 ppb, 3.4 μg/m3, 7.7 ppb, respectively.

The implementation of robots to manipulate with soft or fragile objects requires usage of high sensible tactile sensors. For many applications, beside the force magnitude, the direction is also important. This paper extends already available ideas and implementations of 3D-tactile sensors. Our sensor can detect a wide range of forces, the direction of forces and shifting forces along the sensor surface for several contact points simultaneously. The amount of capabilities in a single sensor is unique. The underlying concept is a pressure-to-light system. A camera provides images of a structure, which generates geometric shapes on the images according to external acting forces. The shapes are well convenient for image processing and give the ability to use them as reference for the forces. After describing our approach very detailed we show experiments for evaluation, e.g. applying it to grab objects carefully. Finally the future work is discussed, where we plan to bring the sensor to anthropomorphic robot hands.

The paper describes the laboratory bench, structure and the contains of the laboratory course, intended for study of principal components of the robotic platform NI Robotics Starter Kit 2.0 with the differential drive of wheels, the basic principles, and also design tools and system of navigation and motion control of the mobile robot.

This paper will present a new proposed system which combines vision and force control in order to transfer model-free objects between human hand and robot hand. The proposed system will improve robot's facilities to interact with human, especially during handing-over tasks. The contributions of this work are: 1. The robot system will be able to detect all kinds of objects which are carried out by human hand without any a priori information about the model of the objects. 2. The robot system will track the object visually and grasp it with the help of force control. 3. In the proposed system, vision and force sensors are integrated in order to guarantee the safety issues, to guarantee the fulfillment of the grasping task and to react to the motion of human hand during the interaction phase. The proposed system is supported by experimental results which illustrate the capability of the proposed algorithms in different cases with different objects.

This paper will propose an assistance robot system which is able to transfer model-free objects from/to human hand with the help of visual servoing and force control. The proposed robot system is fully automated, i.e. the handing-over task is performed exclusively by the robot and the human will be considered as the weakest party, e.g. elderly, disabled, blind, etc. The proposed system is supported with different real time vision algorithms to detect, to recognize and to track: 1. Any object located on flat surface or conveyor. 2. Any object carried by human hand. 3. The loadfree human hand. Furthermore, the proposed robot system has integrated vision and force feedback in order to: 1. Perform the handing-over task successfully starting from the free space motion until the full physical human-robot integration. 2. Guarantee the safety of the human and react to the motion of the human hand during the handing-over task. The proposed system has shown a great efficiency during the experiments.

In this paper the stabilization of the well known inverted pendulum problem by visual control is presented. The pendulum is mounted on the flange of an articulated robot arm. It is observed by camera and its angle of inclination is computed by image processing on a PC. For stabilization a state space controller realized with the standard robot controller is used. In summary, this system can be seen as an example of a robot with visual control. The inverted pendulum is used because it is an adequate controlled system to demonstrate advanced control algorithms. Its implementation with a robot results in some more restrictions in comparison with the pendulum mounted on a linear drive, e.g. in lower acceleration, smaller traversing range and the relatively high cycle time of the commercial robot controller. Furthermore, the integration of the camera in the closed loop control instead of an angular transmitter makes the successful realization more difficult.