bluuebunny/crossref_metadata_2025 huggingface dataset:
Up-Conversion Nanomaterial Doped With Au Nanoparticles With Photothermal Conversion for Multi-Modality Imaging
Authors: Zhang Wei , Lu Yanli , Zang Yang , Xiong Qingyun , Xiong Jinping
Published: December 2021

In this study, a new method for synthesizing Au-NaYF4:Yb3+/Er3+-DSPE-PEG2K nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Au nanoparticles were doped into upconversion nanomaterials and modified with DSPE-PEG2k up-conversion nanomaterials. This material is known as Ag-UCNPs-DSPE-PEG2k, it improves both the luminous intensity because of the doped Au nanoparticles and has low cytotoxicity because of the DSPE-PEG2k modified. Exciting UCNPs with a wavelength of 980nm near-infrared light will emit light with a wavelength of 520nm to further excite gold nanoparticles to convert light energy into heat. Successful synthesized gold nanoparticles was confirmed using transmission electron microscopy (TEM). The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Au nanoparticles. Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Au nanoparticles. The cytotoxicity of Au-UCNPs-DSPE-PEG2K was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) method.
Sentiment Analysis of Short Russian Texts Using BERT and Word2Vec Embeddings
Authors: Popova Ekaterina , Spitsyn Vladimir
Published: December 2021

This article is devoted to modern approaches for sentiment analysis of short Russian texts from social networks using deep neural networks. Sentiment analysis is the process of detecting, extracting, and classifying opinions, sentiments, and attitudes concerning different topics expressed in texts. The importance of this topic is linked to the growth and popularity of social networks, online recommendation services, news portals, and blogs, all of which contain a significant number of people's opinions on a variety of topics. In this paper, we propose machine-learning techniques with BERT and Word2Vec embeddings for tweets sentiment analysis. Two approaches were explored: (a) a method, of word embeddings extraction and using the DNN classifier; (b) refinement of the pre-trained BERT model. As a result, the fine- tuning BERT outperformed the functional method to solving the problem.
Visualization of Research Trending Topic Prediction: Intelligent Method for Data Analysis
Authors: Charnine Michael , Tishchenko Alexey , Kochiev Leon
Published: December 2021

This paper presents the results of a method for the visualization of the long-term prediction of research trending topics. Meaningful topics were identified among the words included in the titles of scientific articles. The title is the most important element of a scientific article and the main indication of the article’s subject and topic. We treated the titles’ words, which occur several times in articles cited in the analyzed collection, as the research trending topics. The longevity of the citation trend growth was the target for the machine learning algorithms. The CatBoost machine learning method, which is one of the best implementations of decision trees, was used. We conducted experiments on a scientific dataset that included 5 million publications from the top conferences in artificial intelligence and data mining areas to demonstrate the effectiveness of the proposed model. The accuracy rate of three-year forecasts for a number of experiments from 1997 to 2014 was about 60%. To visualize the forecast, the t-SNE and Word2Vec methods were used. Clusters of trending keywords on the semantic map helped to accurately identify promising directions. Two examples of forecast visualizations for the topic “Intelligent methods for data and image analysis” are presented. The presented visualizations serve as the analytical method for predicting topic trends and promising directions.
Syntactic Analysis of Graphical Information Using Recursive Scoping
Authors: Razumowsky Alexei
Published: December 2021

The report presents a new method for structuring, segmentation, and algorithmic design of the character array parser using VRML data as an example. The key feature of the method is the possibility of forming a hierarchically complex object by means of recursive data structuring, which allows you to cover the entire contents of the object, including its arbitrary nesting of child objects. This leads to a highly manageable development of the parsing algorithm, allowing you to focus each time on a specific piece of data, while not losing sight of the entire aggregate coherence of the information. The results obtained can easily be used in plans for creating convenient data storage structures related to information security, solving the problem of containing the amount of data in files, data management problems in heterogeneous systems, and hierarchical data solutions in the Internet of Things.
Using a Variational Autoencoder in the Task of Overlaying Multiple Images
Authors: Malyshev Gregory , Andreev Vyacheslav , Andreeva Olga , Chistyakov Oleg , Sveshnikov Dmitriy
Published: December 2021

The possibility and expediency of using variational autoencoders when expanding training datasets of neural networks for cases when the training set consists of several dozen samples is tested. Investigations are carried out on the example of images of «crack»-type defects. Brief information on the theory of variational autoencoders is given. Practical recommendations are given for constructing training sets of variational autoencoders. It is shown that deviation from the suggested recommendations will most likely not allow generating realistic images for the case of a small dataset. For the case of a dataset of eighty images, the distribution of «crack»- type defects in the hidden space after training the variational autoencoder is demonstrated. Examples of images with defects sampled from different parts of the distribution of latent factors are given. For the case of images of cracks, the continuity of the hidden space is demonstrated, when one image is sufficiently smoothly transformed into another on the way through the space of hidden features. A method for obtaining superimposed images based on the use of variational autoencoders is proposed. This method seems to be promising, since it allows automating the process of obtaining superimposed images. Examples of generated cracked superimposed images are shown.
A Mathematical Model for Ranking High-Level User Interface Regression Tests
Authors: Zarubin Ilya , Filinskikh Aleksandr
Published: December 2021

A mathematical model has been developed for ranking high-level verification scenarios (manual tests) when forming a pool of regression tests. The model takes into account the significance of selection methods based on the analysis of previous runs of regression tests, the opinion of the expert group, the specifics of the changes made in the current release, and also has the ability to use an arbitrary number of additional test selection methods. The model can be used to analyze historical data of regression test runs using neural networks in order to identify the most effective approaches to selecting tests for regression testing. The model can be implemented in a software package that interacts with various testing management systems in order to significantly accelerate the formation of a pool of regression tests with a different approach to selection, which can be used by a test engineer of average qualification and without a deep understanding of the features and architecture of the information system being developed.
An Auto-Programming Approach to Vulkan
Authors: Frolov Vladimir Alexandrovich , Sanzharov Vadim , Galaktionov Vladimir Alexandrovich , Scherbakov Alexandr
Published: December 2021

We propose a novel high-level approach for software development on GPU using Vulkan API. Our goal is to speed-up development and performance studies for complex algorithms on GPU, which is quite difficult and laborious for Vulkan due to large number of HW features low level details. The proposed approach uses auto programming to translate ordinary C++ to optimized Vulkan implementation with automatic shaders generation, resource binding and fine-grained barriers placement. Our model is not general-purpose programming, but is extendible and customer-focused. For a single C++ input our tool can generate multiple different implementations of algorithm in Vulkan for different cases or types of hardware. For example, we automatically detect reduction in C++ source code and then generate several variants of parallel reduction on GPU: with optimization for different warp size, with or without atomics, using or not subgroup operations. Another example is GPU ray tracing applications for which we can generate different variants: pure software implementation in compute shader, using hardware accelerated ray queries, using full RTX pipeline. The goal of our work is to increase productivity of developers who are forced to use Vulkan due to various required hardware features in their software but still do care about cross-platform ability of the developed software and want to debug their algorithm logic on the CPU. Therefore, we assume that the user will take generated code and integrate it with hand-written Vulkan code.
Simulation of Cosmonaut Rescue Using Virtual Reality
Authors: Mikhaylyuk Mikhail , Maltsev Andrey , Strashnov Evgeny
Published: December 2021

This paper presents original solutions for creation of training complex learning cosmonauts to control a space jet pack on purpose self-rescue when emergency happens. An approach is proposed in which training is carried out in a virtual environment using virtual reality gloves and headset. The idea is that control of virtual space jet pack model is performed by interaction of virtual hands, copying movements of cosmonaut's hands, with three-dimensional model of jet pack's control panel. To implement the training complex, methods and approaches were developed for movement synchronization simulation of virtual and real hands, as well as simulation of jet pack's control panel and thrusters. Approbation of proposed methods and approaches was carried out as part of our virtual environment system VirSim developed at the SRISA RAS. Results obtained in the paper can be used to create training complex for learning cosmonauts to rescue when they accidentally separate from the International Space Station.
Experimental Digital Animation in Fluid Dynamics
Authors: Znamenskaya Irina , Sysoev Nikolay , Doroshchenko Igor
Published: December 2021

Digital imaging became one of the main tools for studying unsteady flows. Modern high-speed cameras support video recording at high frame rates which makes it possible to study extended high-speed processes. We demonstrate here different animations: water temperature field evolution with a frame rate of 115 Hz; high-speed shadowgraph visualisation of different flows - water jet formation process (100 000 frames / s), shadowgraph animations of the shock waves created by the pulsed discharges (124 000 frames / s). Also, as an example of plasma flow visualization technique, we offer 9 sequential images of the shock wave - pulse gas discharge visualization obtained by the high-speed CCD camera with the 100 ns delay between frames. We developed in-house software based on the machine vision and learning techniques for automatic flow animations processing. The examples of the automatic oblique shock detection using Canny edge detection and Hough transform and thermal plume detection based on the pre-trained convolutional neural network are provided and discussed.
Visualization of the Influence of Changes in the Controlled Dissipative Properties on the Accuracy of the QGDFoam Solver
Authors: Bondarev Alexander Evgenyevich , Kuvshinnikov Artyom Evgenyevich
Published: December 2021

This work is devoted to the study of the influence of variation of the controlled dissipative properties on the accuracy of the QGDFoam solver and the visual representation of this influence. The work continues a series of studies on the comparative assessment of the accuracy of various numerical methods and solvers built on their basis. To carry out a comparative assessment, a generalized computational experiment for classes of problems with a reference solution is constructed and implemented. A generalized computational experiment based on the synthesis of solutions of mathematical modeling problems, parallel technologies and visual analysis tools makes it possible to obtain solutions not only for individual problems, but for whole classes of problems determined by the specified ranges of key parameters. Accordingly, a comparative assessment of the accuracy of numerical methods is also carried out for a class of problems. Earlier, a similar computational experiment was carried out for a comparative assessment of the accuracy for solvers of the OpenFOAM open source software package on the well-known classical problem of an oblique shock wave formation. One of the solvers participating in the calculations, namely the QGDFoam solver, was the only one of all to have controlled dissipative properties. New generalized computational experiment was implemented to study the effect of variation of the parameter that controls the dissipative properties. The target was to reduce the error in comparison with the reference solution. The research results are presented in this work.
Visualizing Generalized Computational Experiment State
Authors: Zakharova Alena Alexandrovna , Korostelyov Dmitriy Alexandrovich , Podvesovskii Aleksandr , Galaktionov Vladimir Alexandrovich
Published: December 2021

The paper considers the problem of evaluating the state of a generalized computational experiment in the context of a general problem of creating methods for adaptive planning and control of a generalized computational experiment in mathematical modeling of real physical processes. A generalized computational experiment implies multiple solution of the numerical simulation problem for various sets of values of defining model parameters. As a method for assessing a generalized computational experiment state, it is proposed to visualize a set of experimental data specifying this state then followed by analysis of the resulting visual image. An approach to visualization of a generalized computational experiment state is proposed based on the sequential applying of two methods: visualization of a series of dependencies of the output simulation parameters on the input ones for a given set of approximating functions and visualization of approximation accuracy parameters for different ranges of values of the input parameters. A description of each of these methods is given. Examples of their application are considered when assessing the accuracy of numerical models of the OpenFOAM software platform for a three-dimensional problem of inviscid flow around a cone.
Development of an Approach to Building Representation Metaphors for Visualizing Graph Models (by the Example of Cognitive Maps)
Authors: Isaev Ruslan , Podvesovskii Aleksandr
Published: December 2021

The paper presents development of the authors’ approach to visualization of graph models of various types based on the use of visualization metaphors and aimed at increasing cognitive clarity of these models. One of the key problems of this approach is investigated – namely, formalization of the process of constructing representation metaphors for graph models. Features of graph models that allow formalizing the process of their visualization are considered, the necessary terminology is introduced. A number of principles have been formulated that must be considered when forming metaphors for representing graph models. On the basis of the introduced principles, a general approach to the construction of representation metaphors for visualization of arbitrary graph models is proposed. The main ideas for applying the proposed approach are demonstrated by the example of a fuzzy cognitive map when constructing a metaphor for representing the results of its structure and target analysis. The directions of advanced research have been determined, within the framework of which it is planned to further formalize the approach in order to ensure the possibility of its software implementation. In the future, the presented approach can become an important component of an integrated approach to building a visualization mechanism for an arbitrary graph model, which provides support for efficient visual analysis throughout all stages of modeling.
The Visualization of Educational Measurement Results in Adaptive eLearning Systems for the Analysis of the Assessment Materials Quality
Authors: Lavdina Yulia , Gustun Oleg , Antonov Evgeniy
Published: December 2021

In the adaptive eLearning system educational measurements are used to control the learning process realized on the feedback approach. Based on the current educational measurements results of the students’ achievements the decision is made on the formation of the educational content given to them at the next stage of learning. To obtain reliable and accurate assessments of the students’ educational achievements, it is necessary to use assessment materials that satisfy the requirements for reliability, homogeneity, discriminatory power, validity and other characteristics. In order to evaluate the values of these quality indicators of assessment tools, it is necessary to do sufficient statistical research of the educational measurements results. At the same time the researcher often encounters the situation when the measurement data are insufficient to achieve the required significance level of the conclusions obtained. In this case assistance in the control decision support can be acquired by analyzing the visual presentation of the educational measurements results. We propose the visual presentation of the assessment materials characteristics, which helps to evaluate the values of quality indicators of assessment tools during the accumulation of new measurement data. This information combined with a visual analysis of the results of students’ achievements makes it possible to identify the trends in the changes of dynamics in students’ competencies and to form the educational control action corresponding to their academic performance levels.
Visualization of the Neutron Fields Behavior According to the Operational Archives of the Nuclear Reactor RBMK
Authors: Bukalin Alexey , Zagrebayev Andrey , Pilyugin Viktor
Published: December 2021

This paper describes the calculation algorithm, software package and visualization of the three- dimensional neutron field deformation according to the archive of the RBMK (high-power pressure-tube reactor) reactor operational parameters. The determination of the field deformation is based on finding "natural" functions for approximating the readings of in- reactor control discretely located sensors. This paper considers the use of generally accepted harmonic functions and "natural" functions for describing the spatial deformation of the neutron field. It is shown that to describe the deformation of the neutron field, it is sufficient to use only a few "natural" functions, in contrast to the use of all harmonic functions from a given set. This, in turn, opens up new possibilities, in particular, for solving the problems of predictive diagnostics of in-reactor control sensors.
Rendering the Real-Time Caustics with DirectX Raytracing
Authors: Komarov Egor , Zhdanov Dmitry , Zhdanov Andrey
Published: December 2021

Caustic illumination frequently appears in the real life, however, this type of illumination is especially hard to be rendered in real-time. Currently, some solutions allow to render the caustic illumination caused by the water surface, but these methods could not be applied to the arbitrary geometry. In the scope of the current article, we present a method of real-time caustics rendering that uses the DirectX Raytracing API and is integrated into the rendering pipeline. The method is based on using additional forward caustic visibility maps and backward caustics visibility maps that are created for light sources and the virtual camera correspondingly. The article presents the algorithm of the developed real-time caustics rendering method and the results of testing its implementation on test scenes. The analysis of the dependence of the rendering speed on the depth of specular ray tracing, the number of light sources, and the number of rays per pixel is carried out. Testing shows promising results which can be used in the modern game industry to increase the realism of the virtual world visualization.
Influence of the Tool Visibility and the Object Highlighting when Interacting with a Virtual Object in a VR Environment
Authors: Averbukh Natalya , Starodubtsev Illya
Published: December 2021

Interaction methods with virtual objects are an important issue for the development of VR. The user experience directly depends on the success of interactions with virtual objects and the environment. Therefore, the factors affecting the effectiveness of human-computer interaction has become a hotspot in this field. The paper describes an experiment focused on the influence of different visibility conditions of the interaction tool and the highlighting of a virtual object of interaction (with indirect interaction). We present the conditions, methodology and results of this study. The influence of different levels of abstraction of objects is considered: from geometric bodies (balls and parallelepipeds) to cartoon fruits, and photo realistic objects after then. For these conditions, a classical two-factor experiment is carried out, in which the first factor is the visibility of the control beam, and the second is the visibility of the highlighting of the captured object. The influence of one of these factors or their combination on the success of the movement of the captured object is expected. The success is determined by the time of movement (the less time, the more successful) and the optimality of the trajectory. The formula is proposed that determines the optimality of the trajectory. It is surprising that the movement of an already captured object is not affected by any of the above factors. Apparently, it is necessary to carefully study the capture process itself, not including the post-capture movement in the calculation.
Detection of Good Matching Areas Using Convolutional Neural Networks in Scene Matching-Based Navigation Systems
Authors: Shahoud Ayham , Shashev Dmitriy , Shidlovskiy Stanislav
Published: December 2021

This paper presents a solution for false matching detection in scene matching-based aerial navigation systems. A navigation system that uses normalized cross-correlation to match a captured image with a reference image was designed. While traditional methods rely on statistical indicators to detect false matchings, this research relied on deep learning using Convolutional Neural Network (CNN). A CNN was trained to online predict the probability of a matching result to be true or false. The training dataset of images was constructed depending on the knowledge of where good matching areas are expected to be. The probability numbers were stored as an assistant map to be used again with the same reference map without classification. The system was implemented and tested in a 3D simulation environment using models for a drone, camera, and flight environment. The Robot Operating System (ROS) and the 3D dynamic simulator Gazebo were used for simulation. The results proved the efficiency of the proposed method in excluding the false matchings. Using the assistant map without classification resulted in an execution time of 41ms and RMS error of position less than 1.2m.
Rare Traffic Signs Recognition
Authors: Badmaev Tingir , Shakhuro Vlad , Konushin Anton
Published: December 2021

Recognition of road signs is an important part of the control systems of autonomous vehicles and driver assistance systems. Modern recognition methods based on neural networks require large well-labeled datasets. Marking up data is quite expensive, but it is even more difficult to mark up rare classes of objects. To solve this problem in this article, we use synthetic data. We improve the marking of the Russian traffic signs dataset (RTSD) in semi-automatic mode adding 9 thousand new road signs. We perform an experimental evaluation of the currently best classifiers and detectors in the task of recognizing road signs. To improve the performance of classification, we use stochastic weight averaging (SWA) and contrastive loss. The use of modern methods allows us to train a high-quality neural network on synthetic data, which was previously impossible, and significantly improves the metrics of recognition of both rare and frequent road signs.
Segmentation of Seismic Images
Authors: Tolstaya Ekaterina , Egorov Anton
Published: December 2021

In this paper we propose a method of seismic facies labeling. Given the three-dimensional image cube of seismic sounding data, labeled by a geologist, we first train on the part of the cube, then we propagate labels to the rest of the cube. We use open-source fully annotated 3D geological model of the Netherlands F3 Block. We apply state-of-the-art deep network architecture, adding on top a 3D fully connected conditional random field (CRF) layer. This allows to get smoother labels on data cube cross-sections. Pseudo labeling technique is used to overcome training data scarcity and predict more reliable labels for geological units. Additional data augmentation allows also to enlarge training dataset. The results show superior network performance over existing baseline mode.
Experience in Binary Classification of Sea Ice by SAR Images Based on Neural Networks
Authors: Mikhaylyukova Polina , Semenova Marina , Shurygina Anastasia , Shabalin Nickolay , Antonova Anna , Valeev Vladimir , Mitrofanov Andrey , Kokhan Andrey , Bekhtin Sergey , Golubev Pavel
Published: December 2021

The paper presents the results of the first version algorithm development for sea ice binary classification by SAR images. To create training, test, and validation samples, we have used 81 images acquired with the Sentinel-1 radar for the area of the Pechora Sea (the south-western part of the Barents Sea) for the 2019–2020 ice period. We conducted the preprocessing procedure for each image aimed at better image quality, noise removal, including gradient noise, and geospatial reference. The marking images was carried out semi-automatically using the K-means clustering algorithm. The result of clustering is a bitmap file with a class number assigned to each pixel. The raster was then vectorized and the expert manually divided the resulting vector polygons into water and ice classes. Validation images were monitored using a set of metrics with the following average result achieved: 0.86 (Jaccard), 0.14 (Binary Crossentropy), 0.90 (Precision), 0.95 (Recall). Expert analysis of binary classification errors has shown that they are typical for the periods when ice is being actively formed or destructed, which results in alternating small areas of ice and open water offshore.
Digital Earth for Smart Municipalities
Authors: Eremchenko Eugene , Rylskiy Ilya
Published: December 2021

The paper discusses the prospects of integrating the concepts of Digital Earth and Smart Cities to achieve synergy and improve global governance by harmonizing decision supports at different scales. Achieving this goal requires overcoming the scale-dependent differentiation of information systems and thus integration of Smart Cities with their geospatial context. A possible approach can be based on the unique property of spatial and temporal localizations – their invariance for all subjects. Spatial and temporal localizations are fundamentally different from classical "thematic" ontologies, inevitably contradictory and relative. Implementing the concept of "ontological pluralism" will allow the seamless integration of heterogeneous information into a single spatial and temporal volume. The urgency of practical implementation of such systems is demonstrated briefly against the background of the COVID-19 pandemic and the need to find new approaches to the processing and analyzing environmental factors.
Elimination of Information Redundancy of Hyperspectral Raster Images Technology Enhancement
Authors: Vasin Dmitriy , Pahomov Pavel , Rotkov Sergey
Published: December 2021

The work is a continuation of the authors' research on the problem of adaptive compression of raster hyperspectral images of Earth remote sensing. In the first part of the article, the authors give an overview of the current state of affairs in the processing of images of remote sensing of the Earth, the characteristic properties of raster hyperspectral images in the context of the prospects for lossy compression, the problems of the effectiveness of existing compression methods of this type of graphic documents are indicated. Further, the article highlights the issues of increasing the efficiency of methods for eliminating information redundancy of raster hyperspectral images of Earth remote sensing. The problems of designing and creating parallel methods and algorithms for the compression of raster hyperspectral ERS images are considered. A method for the development of a parallel algorithm for constructing a system of local homogeneous "well-adapted" basis functions for raster hyperspectral images, based on the Chebyshev approximation for systems using the CUDA graphics processor, is proposed.
Geometric Aspects of the Functional-Voxel Implementation of the ORCA Algorithm
Authors: Tolok Alexey , Sycheva Anastasiya
Published: December 2021

The problem of avoiding a collision between moving agents constantly arises in multi-agent systems with decentralized control. The various algorithms for solving this problem are accompanied by computational complexity and increasing computational power requirements as the number of agents in question increases. There are difficulties in adapting these algorithms to practical applications on mobile platforms. It is necessary to develop simpler computational schemes and to apply appropriate models. The most computationally expensive step in the classical collision avoidance algorithm ORCA is to calculate the mutual half-planes of possible collision for each pair of robots and use linear programming to calculate the new velocity from them. The application of the functional-voxel method will simplify the necessary calculations by storing in graphical images the local geometric characteristics of the searched domain. Moreover, the application of such models will make it possible to perform most of the necessary calculations in advance, which will accelerate the work of the algorithm. This paper proposes the construction of a functional-voxel model of a required geometric domain by interpolating the contour of the domain using Bezier curves. The local geometric modelling by means of local zeroing function is used as a tool for functional-voxel curve modelling. The obtained functional-voxel model represents a static case of possible mutual positioning of two agents. A four-dimensional graphical model is proposed to solve the dynamic case. This model performs the distribution of the static case modelling results in the space-time characteristics.
A Constructive Algorithm for Transforming Conics in a Circle on Imaginary Laguerre Points
Authors: Boyashova Elena , Voloshinov Denis , Musaeva Tatyana
Published: December 2021

The article is devoted to the consideration of the properties of Laguerre points in their constructive relationship with the solution of the problem of transforming a pair of conical curves into two circles. Geometric schemes are presented that reveal the principle of specifying a collinear transformation that establishes a correspondence between conics and their images in the form of circles, including if the Laguerre points defining this transformation are imaginary. In the solution of the problem, a geometric scheme for the formation of the main conjugations of conics is given, one of which is a circle. The conclusion is substantiated that the radical axis of the bundle of circles is a circle that has split into a pair of straight lines, one of which is infinitely distant. An algorithm for the formation of a series of elliptic curves induced by Laguerre points is presented.
Improvement of an Algorithm for Determining the Interplanar Distances of the Crystal Structure of a Substance from Transmission Electron Microscopy Images
Authors: Nebaba Stepan
Published: December 2021

The paper considers the previously developed automated algorithm for determining the interplanar distances of the crystalline structure of a substance from images of transmission electron microscopy (TEM images), and also proposes a modification of the algorithm that allows to increase the automation level of obtaining the result. The question of automation of the process of normalization of images of crystal structures by angles of rotation is considered. Also, an alternative is proposed at the step of image binarization with a given threshold in the form of adaptive binarization with an automatically selected binarization window size. The improved algorithm was tested on a number of publicly available TEM images, and the interplanar distance measurements were compared with the measurements in the specialized software package named Digital Micrograph GMS 1.8. Comparison of the results showed that the proposed improved algorithm determines the distance with sufficient precision and fits within the range of measurement error for the considered images.
Computer Graphics in Lighting Engineering Education: Teaching Photometry and the Basics of the Light Field
Authors: Budak Vladimir , Makarov Denis
Published: December 2021

The modern level of computer graphics programs (CGP) has long been able to solve most design, architecture, and lighting design problems. However, the use of CGP in teaching students is still underestimated. The existing traditional teaching methods cannot effectively teach the basics of lighting engineering and photometry to students. This is especially true for the concepts of illumination that are difficult to understand – luminance (radiance), color temperature. The article offers a method of teaching photometry based on the wide use of CGP. Within the methodology framework, 3D laboratories were developed. Students can study the basics of lighting engineering and optical effects, visually observe most lighting quantities, see their relationship, and interact with the surrounding space and objects. More than ten 3D training models on interior, architectural, and road lighting have been modeled. Within each model, students are given tasks for independent work. Checking the received material from students at several leading Russian universities allowed us to conclude on the methodology's effectiveness and outline many key aspects of its development.
Lighting Quality Criteria Based on the Luminance Spatial-Angular Distribution
Authors: Boos George , Budak Vladimir , Meshkova Tatyana , Zheltov Victor
Published: December 2021

The article is devoted to assessing lighting quality based on lighting engineering design's spatial-angular brightness distribution (LSAD). The main problems of modern lighting design related to the modeling of scenes based on the emissivity equation and restrictions on the use, in this regard, as the main criterion for the lighting quality of Unified Glare Rating (UGR), are considered. The mathematical foundations of the use of LSAD in the practice of lighting engineering design are proposed. The integral equation LSAD is obtained, which allows modeling the brightness at an arbitrary point of the scene volume. A method for solving the formulated equation based on double local estimates of the Monte Carlo method is proposed. The formulated algorithm for calculating LSAD is view-independent: LSAD visualizes the lighting scene at all fixed points. Methods for storing the calculated LSAD are proposed. Based on the LSAD, a new criterion for lighting quality is formulated, which was experimentally tested in a full-scale experiment to evaluate the lighting of Moscow Metro stations. In the experiment, the proposed criterion and UGR were compared. One calculated the quality criteria by the station's photos, and their correlation with observers' assessment was found. Computer models of stations were created, in which quality criteria were also calculated from the experiment viewpoints – obtained good correspondence of the calculations with the estimates of observers.
Rendering Semisynthetic FIB-SEM Images of Rock Samples
Authors: Safonov Ilia , Kornilov Anton , Reimers Iryna
Published: December 2021

Digital rock analysis is a prospective approach to estimate properties of oil and gas reservoirs. This concept implies constructing a 3D digital twin of a rock sample. Focused Ion Beam - Scanning Electron Microscope (FIB-SEM) allows to obtain a 3D image of a sample at nanoscale. One of the main specific features of FIB-SEM images in case of porous media is pore-back (or shine-through) effect. Since pores are transparent, their back side is visible in the current slice, whereas, in fact, it locates in the following ones. A precise segmentation of pores is a challenging problem. Absence of annotated ground truth complicates fine-tuning the algorithms for processing of FIB-SEM data and prevents successful application of machine- learning-based methods, which require a huge training set. Recently, several synthetic FIB- SEM images based on stochastic structures were created. However, those images strongly differ from images of real samples. We propose fast approaches to render semisynthetic FIB- SEM images, which imply that intensities of voxels of mineral matrix in a milling plane, as well as geometry of pore space, are borrowed from an image of rock sample saturated by epoxy. Intensities of voxels in pores depend on the distance from milling plane to the given voxel along a ray directed at an angle equal to the angle between FIB and SEM columns. The proposed method allows to create very realistic FIB-SEM images of rock samples with precise ground truth. Also, it opens the door for numerical estimation of plenty of algorithms for processing FIB-SEM data.
Exploratory Analysis of Biomedical Data in Order to Construct Intelligent Analytical Models for Assessing the Risk of Cancer
Authors: Lagerev Dmitry , Korsakov Anton , Zakharova Alena
Published: December 2021

This article substantiates the need to use data from an integrated electronic medical record of a patient to assess the risk of cancer. An exploratory analysis of the data of the integrated electronic medical record of patients in the Bryansk region who received a diagnosis of "malignant neoplasm" is being carried out. The influence of the patient's age on the risk of oncological diseases is evaluated by the example of the nosologies C50, C61. Provides an overview of the capabilities of the Auto ML Libraries and their limitations. The article describes the result of constructing models for assessing the risk of oncological diseases based on the ML.NET and Auto-WEKA libraries. It is concluded that it is impossible to constructing models for assessing the risk of oncological diseases based on the data of an integrated electronic medical record using Auto ML libraries without preliminary preparation and preprocessing of data. And since it is required to constructing separate models for each nosology and regular retraining of these models, it is advisable to develop an add-on over the Auto ML libraries that will extract and convert the data of the integrated electronic medical record into a form suitable for analysis. In addition, to improve the quality of the model, it is advisable to use patient history data, data obtained after vectorization of laboratory tests, aggregated data on visits to specialized specialists and related diagnoses, data from online patient questionnaires filled out during the course of medical examination, as well as data on environmental pollution.
Intelligent Image Labeling System for Recognizing Traffic Violations
Authors: Titarev Dmitriy , Korostelyov Dmitriy , Titarev Valentin , Kopeliovich Dmitriy
Published: December 2021

The article examines the problems of traffic violations and possible changes in the city, including in the road infrastructure, based on their analysis. A conclusion is made about the applicability of machine learning methods for preparing and marking images for solving the problem. The article describes in detail the algorithms for automatic marking of images for recognizing traffic violations in order to create a comfortable urban environment. The existing information systems that solve this problem are analyzed, with an indication of their strengths and weaknesses. The description of an intelligent system developed by the authors and combining manual and automatic object recognition is given. The system development tools are described, including the libraries used. The experimental part contains the results of testing the system, incl. neural network training. Information on the number of images and objects on them is given, as well as information on the percentage of correctly detected objects for the automatic image labeling.
The Dynamic Impact of Structural Oil Price Shocks on the Macroeconomy
Authors: Li Ping , Li Jie , Zhang Ziyi
Published: December 2021

In this paper, we apply the structural vector autoregression (SVAR) model to decompose the international oil price shock into oil supply shocks, aggregate demand shocks and oil-specific demand shocks, and then use the DCC-GARCH model to analyse the dynamic correlations between these three kinds of oil price shocks and the macroeconomic variables of several oil importing and exporting countries. To quantify the intensity of the effect of oil shocks on these variables, we propose a measure, conditional expectation (CoE), to capture the percent change of the economic variable under oil price shocks relative to the median state. The time-varying copula model is employed to estimate the proposed measure through time. The empirical results show that, for instance, the impacts of oil price shocks on macroeconomic variables are different in different periods, showing the time-varying characteristics. Additionally, the impacts of oil price shocks on macroeconomic variables show great differences and some similarities among different countries. Finally, we give some policy suggestions for these countries, in particular for China’s special results.
Associations of Sociodemographic and Clinical Factors with Late Presentation for Early Infant HIV Diagnosis (EID) Services in Kenya
Authors: Langat Agnes , Callahan Tegan , Yonga Isabella , Ochanda Boniface , Waruru Anthony , Ng'anga Lucy , Katana Abraham , Onyango Brian , Singa Benson , Oyule Stephen , Githuka George , Omoto Lennah , Muli Jane , Tylleskar Thorkild , Modi Surbhi
Published: December 2021

Background: Understanding the missed opportunities in early infant HIV testing within the PMTCT program is essential to address any gaps. The study set out to describe the clinical and sociodemographic characteristics of the infants presenting late for early infant diagnosis in Kenya.
Methods: We abstracted routinely collected clinical and sociodemographic characteristics, in a cross-sectional study, on all HIV-infected infants with a positive polymerase chain reaction (PCR) test from 1,346 President’s Emergency Plan for AIDS Relief (PEPFAR) supported health facilities for the period October 2016 to September 2018. We used multivariate logistic regression to examine the association of sociodemographic and clinical characteristics with late (>2 months after birth) presentation for infant HIV testing.
Results: Of the 4,011 HIV-infected infants identified, the median infant age at HIV diagnosis was 3 months [interquartile range (IQR), 1-16 months], and two-thirds [2,669 (66.5%)] presented late for infant HIV testing. Factors that were associated with late presentation for infant testing were: maternal ANC non-attendance, adjusted odds ratio (aOR) 1.41 (95% confidence interval (CI) 1.18 -1.69); new maternal HIV diagnosis, aOR 1.45, (95%CI 1.24 -1.7); and lack of maternal antiretroviral therapy(ART), aOR 1.94, (95% CI 1.64 - 2.30). There was a high likelihood of identifying HIV-infected infants among infants who presented for medical services in the outpatient setting (aOR 18.9; 95% CI 10.2 - 34.9) and inpatient setting (aOR 12.2; 95% CI 6.23-23.9) compared to the infants who presented late in maternity.
Conclusion and Global Health Implications: Gaps in early infant HIV testing suggest the need to increase maternal pre-pregnancy HIV diagnosis, timely antenatal care, early infant diagnosis services, early identification of mothers who seroconvert during pregnancy or breastfeeding and improved HIV screening in outpatient and inpatient settings. Early referral from the community and access to health facilities should be strengthened by the implementation of national PMTCT guidelines.
 
Copyright © 2021 Langat et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.
The power to lead
Authors: Ahrens Katharina
Published: December 2021

The staff in local organisations are taking on the largest proportion of risk in a humanitarian response by being on the frontlines and endangering their physical and mental well-being. This paper reflects on how local organisations are taking leadership over the responses within their countries despite the challenges of bringing the localisation agenda and commitments into reality. Further, it recommends how international actors can reflect on their localisation efforts to reach a more tangible change that aligns with the Grand Bargain commitments. In addition to advocating for more access to direct funding, the paper also provides examples of how to shift leadership to a more community-driven response aligned with the concept of the triple nexus, and shares firsthand experience from the work of a local organisation that is active in the Syria response and driven by the commitment to create youth-led change.
Linguagem audiovisual na educação básica:
Authors: Souza Anna Carime , Mesquita Deise Nanci de Castro , Carvalho Maria Alice Sousa
Published: December 2021

Este relato de experiência apresenta e discute uma das ações desenvolvidas, em parceria, por dois projetos de extensão do Centro de Ensino e Pesquisa Aplicada à Educação da Universidade Federal de Goiás: “Rompendo o Silêncio” e “Sessão Corujinha”. A atividade presencial consistia na exibição de filmes, seguida de roda de conversa entre estudantes e especialistas em questões de infância e adolescência. Devido à pandemia causada pelo Corona vírus, ela foi repensada e organizada de forma virtual, por meio do canal oficial dessa universidade no Youtube. Para as sessões cinematográficas mensais, agora disponíveis no período noturno, passaram a ser exibidos apenas curtas metragens, cujo foco de reflexão e debate traz à tona questões que se mostraram caras a estudantes dos anos finais de ensino fundamental e de ensino médio, que em 2019 buscaram o Setor de Psicologia Escolar com queixas principalmente relacionadas à violência doméstica e escolar. A partir de uma perspectiva teórica que compreende que toda e qualquer linguagem, seja ela alfabética ou não, se presta a apresentar o mundo em seu aspecto simbólico como uma forma de manifestação e criação humana, os diálogos proporcionados por esses encontros visuais e virtuais têm contribuído para a ampliação do debate sobre as diversas formas de violência, buscando possibilidades de prevenção e conscientização, bem como para fruição estética em linguagem visual e audiovisual da comunidade participante.