bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data assimilation algorithm based on an approximate solution of the optimal nonlinear filtering problem
Authors: Klimova E G 
Published: January 2021

Abstract
In the ensemble Kalman filter, a currently popular data assimilation algorithm, it is assumed that the density of the estimation error distribution is a Gauss function, which is not fulfilled in the general case of a nonlinear model and a nonlinear observation operator. Statement of the optimal filtering problem in the general case is based on the Bayesian approach. One way to approximate the nonlinear optimal filtration problem is to represent the distribution density as a sum of Gaussian distributions with given mean values and covariance matrices. Then the optimal estimate is the weighted sum of estimates obtained in the Kalman filter for the corresponding parameters of the Gaussian distribution in this sum. The paper considers the algorithm for implementing this approach, which uses the previously developed effective local ensemble data assimilation algorithm (ensemble pi-algorithm). The results of numerical experiments to evaluate the properties of the proposed algorithm with the 1-dimensional nonlinear model are presented.
Eddy mixing of momentum and heat in stably stratified boundary layers: Numerical study
Authors: Kurbatskaya L I 
Published: January 2021

Abstract
The paper analyzes the features of turbulent momentum and heat transfer in a stably stratified atmospheric boundary layer (ABL), in the upper troposphere and lower stratosphere, and the possibility of taking them into account in RANS (Reynolds Average Navier Stokes) turbulence models. Such features, for example, include the transfer of momentum (but not heat) by internal gravitational waves under conditions of strong stability, the formation of a low-level jet. Models for the coefficients of eddy diffusion of momentum and heat were obtained by writing the differential equations for the Reynolds stresses and the turbulent heat flux vector in the weakly equilibrium approximation, in which the advection and diffusion of some dimensionless quantities are neglected. In the considered version of the algebraic model built on the physical principles of the RANS approximation for stratified turbulence, three prognostic equations are used: for the kinetic energy of turbulence, the rate of its spectral consumption and the dispersion of temperature fluctuations. It is shown that the profile of the vertical diffusivity for momentum, which was calculated using three-parametric turbulent model, is in agreement with data of direct measurements either within a stable stratified atmospheric boundary layer or beyond this layer in free atmosphere.
An algorithm for estimating greenhouse gas fluxes using satellite data for a global transport and diffusion model
Authors: Platonova M V , Klimova E G 
Published: January 2021

Abstract
The task of estimating the greenhouse gas fluxes in the atmosphere from satellite data is relevant. This problem is solved based on a data assimilation system using a model of transport and diffusion of an impurity in the atmosphere. The paper discusses an approach to solving the problem of estimating the greenhouse gas fluxes based on the use of the ensemble Kalman filter for the global model of transport and diffusion. The paper proposes a numerical algorithm based on the decomposition of the model domain. The originality of this work is the application of the local ensemble Kalman filter algorithm. The case of this study when the greenhouse gas fluxes are considered as a variable is discussed. Here are the results of the simulated numerical experiments on the implementation of the step of analyzing the data assimilation algorithm in the case of the global model of transfer and diffusion.
Numerical modeling of the liquid film flow with evaporation on the basis of the generalized interface conditions
Authors: Rezanova Ekaterina 
Published: January 2021

Abstract
A thin layer of a viscous incompressible liquid flowing down an inclined, non-uniformly heated substrate under conditions of a concomitant gas flow is studied. The flow is accompanied by evaporation at the thermocapillary interface. Dynamic processes in a gas are not taken into account (one-side model). The mathematical model of a thin liquid layer flow is based on the Navier-Stokes equation and the heat transfer equation, as well as generalized kinematic, dynamic and energetic conditions at the thermocapillary boundary. The local mass flux at the interface is determined using the Hertz-Knudsen equation. Analytical solutions of the problem are constructed for the zeroth-order terms of the expansions of the desired functions in powers of a small parameter of the problem. Parametric analysis of the problem is performed. An evolutionary equation for determining the thickness of the liquid layer is obtained. An algorithm for numerical solution of the evolutionary equation is constructed. The results of numerical studies of flows of a thin layer of ethanol and HFE-7100 with evaporation at the interface are obtained.
On solving the slice-by-slice three-dimensional 2-tensor tomography problems using the approximate inverse method
Authors: Louis A K , Maltseva S V , Polyakova A P , Schuster T , Svetov I E 
Published: January 2021

Abstract
A numerical solution of the problem of recovering the solenoidal part of a three-dimensional symmetric 2-tensor field using the incomplete tomographic data is proposed. The initial data of the problem are values of the ray transform for all straight lines, which are parallel to at least one of the planes from a finite set of planes. We consider two sets of planes, the number of planes in which are three and six. The recovery algorithms are based on the approximate inverse method.
An iterative algorithm for reconstructing a 2D vector field by its limited-angle ray transform
Authors: Maltseva S V , Svetov I E , Louis A K 
Published: January 2021

AbstractIn this paper, we consider problem of reconstructing a vector field in the unit disc by using the ray transform in a limited angular domain. We propose an approach to solving this problem. The approach is based on the Papoulis-Gerchberg algorithm. It is an iterative algorithm including iterations in the main space of vector fields and in the frequency domain. The results of numerical simulation are demonstrated.
On the problem of modeling the acoustic radiation pattern of source for the 2D first-order system of hyperbolic equations
Authors: Kabanikhin S I , Klyuchinskiy D V , Novikov N S , Shishlenin M A 
Published: January 2021

Abstract
In this paper the problem of modeling the acoustic radiation pattern of source is considered. The problem is formulated in the form of control problem for the 2D first-order system of hyperbolic equations. This problem is related to ultrasound tomography for early breast cancer detection. The modelling of the acoustic radiation patterns of sources allows us to improve the resolution of acoustic tomography.
Approximate filtered back-projection algorithm for plane curves in tomography problems
Authors: Pickalov V V 
Published: January 2021

Abstract
In previous works by the author and colleagues, it was proposed in some tomography problems to convert the fan-shaped projection measurement scheme into a system of parallel rays by deforming reconstructed image. The deformation of the tomogram for each direction of observation will be different, but the one-to-one nature of these deformations allows one to return back to the original coordinate system. Further, this method is generalized to a family of plane curvilinear trajectories that allow one-to-one transitions to parallel rays. For each back projection, the image is modulated by a known function following from the path differential of a given trajectory. Illustrations of the operation of this algorithm are given on examples of parabolic, sinusoidal and fan-beam paths of rays. The applicability of the algorithm in problems of visualization of discontinuities, in problems of ROI-tomography (local tomography) is shown.References
The singular value decomposition of the dynamic ray transforms operators acting on 2-tensor fields in ℝ2
Authors: Polyakova A P , Svetov I E 
Published: January 2021

AbstractWe consider the problem of the dynamic two-dimensional 2-tensor tomography. An object motion is a combination of rotation and shifting. Properties of the dynamic longitudinal, mixed and transverse ray transform operators are investigated. The singular value decompositions of the operators with usage of the classic orthogonal polynomials are constructed.
The approximation neural-network method for solving nonlinear multi-criteria inverse problems of geophysics
Authors: Shimelevich M I , Obornev E A , Obornev I E , Rodionov E A 
Published: January 2021

Abstract
A multi-criteria inverse problem is reduced to a system of operator equations on compact sets. It is shown that some a posteriori error estimates of solutions of the multi-criteria problem decrease with an increasing number of the criteria used. The approximation neural-network method for solving the multi-criteria inverse problem is presented. An example of a numerical solution of the two-criterion problem of geoelectrics is given, and the a posteriori error estimates are calculated.
The method of approximate inverse for the normal Radon transform operator
Authors: Svetov I E , Polyakova A P 
Published: January 2021

AbstractWe propose an approach for reconstructing a three-dimensional function from the known values of the Radon transform. The approach is based on the method of approximate inverse. The result obtained is the basis of two approaches for reconstructing a potential part of the vector and symmetric 2-tensor fields from the known values of the normal Radon transform. The first approach allows the recovery of components of the potential part of fields, and the second one reconstructs a potential of the potential part of fields.
Towards Optimizing Cloud Computing Using Residue Number System
Authors: Kucherov N , Kuchukova E , Tchernykh A , Kuchukov V , Babenko M 
Published: January 2021

Abstract
In the work methods for building systems of distributed data storage based on the system of residual classes are considered. The use of direct conversion of data from the positional system of calculation to the residue number system will have a large computational complexity, the use of modules of a particular type allows you to solve this problem. The operation of scaling and expansion of the base system, which is necessary to restore the number of stored parts in case of failure of one or more cloud servers, is considered.
Application of the Dorovsky model for taking into account the fluid saturation of geological media
Authors: Golubev V I , Shevchenko A V , Petrov I B 
Published: January 2021

Abstract
The discovery of oil deposits with complex internal structures requires an improvement of available seismic survey methods. The development of modern high-performance computing systems provides an opportunity to use more sophisticated mathematical models. This paper aims to investigate seismic wave propagation in porous fluid-saturated media. The Dorovsky three velocity model was formulated in the two-dimensional case and its numerical solution was obtained with the grid-characteristic method. The computational domain consisted of three layers with different rheology: a water layer, a porous fluid-saturated layer, and an elastic layer. Explicit contact conditions were derived between them and successfully applied with the help of Riemann invariants. The curvature of geological layers was taken into consideration by means of structured hexahedral grids. The time evolution of the spatial distribution of stress tensors and material velocity vectors were calculated and analyzed. These signals contain a mixture of volume, surface, transmitted and reflected waves.
The numerical solution of the problem of the contact interaction in models with gas pockets
Authors: Stognii P.V. , Khokhlov N.I. , Petrov I.B. 
Published: January 2021

Abstract
We present the solution to the problem of detecting the gas layers in the Arctic conditions through the numerical modelling of the seismic waves spread in the heterogeneous media. The grid-characteristic method of the third order of accuracy is used in all the computations. We build the models with the different numbers of the gas layers and analyze the full wave fields in the heterogeneous media and the anomalous fields, excluding the seismic reflections from all the geological layers except for the gas layers. The results demonstrate the possibility of applying the grid-characteristic method to solving these problems. The result seismograms allow us to determine the location of the gas layers through collecting the time moments of the coming reflections from the gas layers to the seismic receivers.
Statistical simulation of radiation transfer in horizontally inhomogeneous stratus clouds
Authors: Kablukova E G , Prigarin S M 
Published: January 2021

Abstract
The aim of the paper is to analyze the influence of a cloud layer horizontal inhomogeneity on radiation fluxes in the atmosphere using the models based on observation data of the optical thickness for stratus clouds. To reproduce the correlation structure and one-dimensional distributions of the observed stratus optical thickness fields we have developed numerical stochastic cloud models based on the inverse distribution function method and autoregressive schemes. We used Monte Carlo methods to simulate the radiation fluxes for visible and infrared ranges of wavelength.
On vortex sheet intensity reconstruction in meshless vortex particle method for two-dimensional flows simulation
Authors: Soldatova I , Kuzmina K , Yakutina M 
Published: January 2021

Abstract
The problem of numerical solution of the boundary integral equation arising in 2D Lagrangian meshless vortex particle methods is considered. The Viscous vortex domains method used for viscous flow simulation, that leads to a large number of vortex particles in the flow domain, especially in the near-wall region. From a mathematical point of view, it leads, in turn, to the fast oscillating right-hand side of the integral equation. An original correction technique is developed that permits one to obtain a numerical solution with high resolution without grid refining. It is based on the Galerkin approach where additional terms are included in numerical solution expansion, that correspond to the subgrid-scale of the solution variation.
Building ontologies for solving compute-intensive problems
Authors: Glinskiy B M , Zagorulko Y A , Zagorulko I M Kulikov G B , Sapetina A F , Titov P A , Zhernyak G F 
Published: January 2021

Abstract
The aim of the study, results of which are presented in this paper, is to analyse methods and tools for constructing an ontology related to solving compute-intensive problems and to form algorithms of its use. This problem arises from the need to solve problems using modern and future supercomputers, containing millions and, in the long term, billions of simultaneously operating computing cores and having a huge degree of parallelism. In solving such problems, the researcher should be well versed in both computational methods for solving the problem and modern supercomputer technologies, which is not always the case. One of the solutions to this problem is the creation of a knowledge base that includes ontological descriptions of methods for solving compute-intensive problems and architectures of supercomputers that can be used to solve them. The development of ontologies for a given subject area is one of the most important stage in creating an intelligent support system for solving specific compute-intensive problems. The paper discusses the methods and tools that are used to build the ontology. The paper also presents examples of the development of ontologies for astrophysics and geophysics problems.
A fractional numerical study on advection-dispersion equation with fractional order
Authors: Fadugba SE , Edogbanya HO , Ogunyebi SN , Babalola BT , Okunlola JT 
Published: January 2021

Abstract
This paper proposes a fractional numerical study on Advection-Dispersion Equation (ADE) with Fractional Order (FO) via the Caputo Fractional Reduced Differential Transform Method (CFRDTM). CFRDTM is the combination of the Caputo Fractional Derivative (CFD) and the well known Transform Method (RDTM). A convergent series solution for ADE with FO is obtained via CFRDTM. The performance of CFRDTM is tested on two illustrative examples. Hence, CFRDTM is found to be accurate and efficient.
Diagnosis of COVID-19 using artificial intelligence based model
Authors: Omoniyi TA , Alabere HO , Sule E E 
Published: January 2021

Abstract
Scientists have shown that early diagnosis of COVID-19 and self-isolation can help curb the spread of the virus. Hence, there is a need to develop methods for early and fast diagnosis of the disease. This research focuses on the development and use of an AI based model that would help medical professionals in easy and fast detection of COVID-19 that can be found in X-ray images, CT scan images, and patient symptoms. The model would then be deployed to the web for easy accessibility. To increase the confident level of decision made by the model, different data augmentation technique was deployed to create variance to the dataset and thereby increasing the accuracy and validation of the model. This project proposes the use of Convolutional Neural Network for classification purpose of both the X-ray image of the Lung of a positive and negative pneumonia patient. Further processes were done with CT scan images of both positive and negative COVID-19 patients. This eliminated biasness of the model. Lastly, symptoms of the disease were added to the model as conditional statement convolutional neural network model. The model was able to reach an overall accuracy of 95% for Pneumonia X-ray image and 89.65% for CT scan image of unseen data (test/evaluation). Artificial intelligence based model is therefore, encouraged for better, easier and more accurate diagnosis of COVID-19. Also, the accuracy of the model can further be improved by certain feature engineering technique and adding more complex deep neural network technique known as ensembles.
Iterative methods for approximate solution of the Ornstein-Uhlenbeck process with normalised brownian motion
Authors: Ogundile O. P. , Edeki S. O. 
Published: January 2021

AbstractThis work considers the concept of the Normalised Brownian motion for the solutions of the Ornstein-Uhlenbeck process using the Daftardar-Jafari Method (DJM) and Picard Iterative Method (PIM) as the approximate-analytical methods of solutions. The results obtained from DJM are compared with those of the PIM. The obtained results, therefore, show the effectiveness of the proposed methods.
Analysis and applicability of a new quartic polynomial one-step method for solving COVID-19 model
Authors: Fadugba S E , Shaalini V J , Ibrahim A A 
Published: January 2021

Abstract
This paper presents the Quartic Polynomial One-Step Method (QPOSM) based on quartic polynomial interpolating function for solving first order Ordinary Differential Equations (ODEs). The validity of the paper is analysed through consistency, order of convergence and stability. Also, the stability polynomial of this method is derived and the corresponding stability region is obtained. The applicability of the method has been demonstrated by COVID-19 model.
Application of the shooting method for the solution of second order boundary value problems
Authors: Edun Ifeoluwa F. , Akinlabi Grace O. 
Published: January 2021

Abstract
Due to the applications of Boundary Value Problems (BVPs) in real-life phenomena, the shooting method has proven itself useful and efficient in handling BVP. The shooting method works by first reducing the BVP to an Initial Value Problem (IVP), then one/two initial value guesses are made. The IVPs are then solved using an iterative solution, and this process is then repeated until the second boundary condition is reached to a satisfactory level. The iterative formula used in this study is the Euler’s method while the initial value estimation method used is the Secant method (interpolation formula).
Demulsification of a Nigerian crude emulsion using ethoxylated-resoles and their xylene modified blends
Authors: Efeovbokhan Vincent E. , Chibuzor Chukwuebuka C. , Babalola Rasheed , Abatan Olubunmi G. , Oladimeji Temitayo E. 
Published: January 2021

Abstract
Demulsification is a method used to reduce or disrupt the water - crude oil emulsion system without uttering the initial composition of the crude oil. This process is done by the introduction of chemicals called demulsifiers, which break the emulsion into aqueous and organic phases. In this study, the demulsifier formulated was the base-catalyzed phenol-formaldehyde resin known as the resoles, with the ratios of phenol to formaldehyde, varied between 1.0:1.2 and 1.0:2.0. The different samples of resoles where then ethoxylated to make them more hydrophilic using different weights (10, 15 & 20 g) of polyethylene glycol (PEG). Screening of the ethoxylated demulsifiers was done using the established bottle test procedure, at 70 °C, the concentration of 50 ppm, and 20 minutes of residence or separation time, in order to select the most effective demulsifier, based on the amount of water removed from the emulsion. The best chemical-demulsifier produced was the ethoxylated resole, which was then blended with xylene at varying percentages (0, 20, 40, 50, and 80 % weight/weight) and was further screened, using the bottle test method. From the analysis, it was obtained that the most effective ethoxylate-xylene demulsifier blend was sample DR3, and made of formaldehyde to phenol ratio of 1.8:1, 20 g of PEG 400 blended with 20 % xylene. The demulsifier gave a water separation efficiency of 85.7 %, compared with the commercial demulsifier, which yielded 72.7 %. The result indicates the practical significance of solvent modified demulsifiers for separating crude oil emulsions in the petroleum industries.
A feedforward artificial neural network model for classification and detection of type 2 diabetes
Authors: Frimpong Enoch A. , Oluwasanmi Ariyo , Baagyere Edward Y. , Zhiguang Qin 
Published: January 2021

Abstract
Efforts to enhance accuracy in medical diagnostics in molecular medicine have contributed to the wide use of artificial neural network (ANN) algorithms for disease detection due to its ability to process large medical datasets and integrate them into characterized outputs to avoid misdiagnosis. Typically, the application of ANNs have proven useful in sample analyses of patients with diabetes and in decision support systems. Over the years, various ANN models have been utilized in medical diagnostics; however, these approaches still maintain certain levels of error and have lesser training and testing accuracies in disease detection. In this study, we propose a Feedforward Artificial Neural Network (FFANN) model with a dense neural network architecture suitable for processing numeric and textual dataset. We carefully designed our model structure to have the ability to maximize the number of layers and nodes required to learn every feature of the dataset and also to perform effective computations but avoiding model under fitting and overfitting which occurs when less or more layers are used respectively. This approach puts our model ahead of other state-of-the-art prediction models which have been proposed in terms of performance as it achieved 97.27% and 96.09% training and testing accuracies, respectively, for type 2 diabetes detection on Pima Indian Diabetes dataset.