Computational Modeling Methods Research Articles

NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data.

To preserve scientific data created by publicly and/or philanthropically funded research projects and to make it ready for exploitation using recent and ongoing advances in advanced and large-scale computational modeling methods, publicly available data must use in common, now-evolving standards for formatting, identifying and annotating should share data. The OpenNeuro.org archive, built first as a repository for magnetic resonance imaging data based on the Brain Imaging Data Structure formatting standards, aims to house and share all types of human neuroimaging data. Here, we present NEMAR.org, a web gateway to OpenNeuro data for human neuroelectromagnetic data. NEMAR allows users to search through, visually explore and assess the quality of shared electroencephalography (EEG), magnetoencephalography and intracranial EEG data and then to directly process selected data using high-performance computing resources of the San Diego Supercomputer Center via the Neuroscience Gateway (nsgportal.org, NSG), a freely available web portal to high-performance computing serving a variety of neuroscientific analysis environments and tools. Combined, OpenNeuro, NEMAR and NSG form an efficient, integrated data, tools and compute resource for human neuroimaging data analysis and meta-analysis. Database URL: https://nemar.org.

From Macro- to Microscale: A combined modelling approach for near-surface wind flow on Mars at sub-dune length-scales.

The processes that initiate and sustain sediment transport which contribute to the modification of aeolian deposits in Mars' low-density atmosphere are still not fully understood despite recent atmospheric modelling. However, detailed microscale wind flow modelling, using Computational Fluid Dynamics at a resolution of <2 m, provides insights into the near-surface processes that cannot be modeled using larger-scale atmospheric modeling. Such Computational Fluid Dynamics simulations cannot by themselves account for regional-scale atmospheric circulations or flow modifications induced by regional km-scale topography, although realistic fine-scale mesoscale atmospheric modeling can. Using the output parameters from mesoscale simulations to inform the input conditions for the Computational Fluid Dynamics microscale simulations provides a practical approach to simulate near-surface wind flow and its relationship to very small-scale topographic features on Mars, particularly in areas which lack in situ rover data. This paper sets out a series of integrated techniques to enable a multi-scale modelling approach for surface airflow to derive surface airflow dynamics at a (dune) landform scale using High Resolution Imaging Science Experiment derived topographic data. The work therefore provides a more informed and realistic Computational Fluid Dynamics microscale modelling method, which will provide more detailed insight into the surface wind forcing of aeolian transport patterns on martian surfaces such as dunes.

Computer modeling and simulation to predict COVID-19 propagation patterns via factual cellular automata

Computer modelling and simulation methods are very important and play a critical role in the mitigation and response to the ongoing COVID-19 pandemic. In this study, we propose a computational modeling technique based on Cellular Automata (CA) with realistic proposed rules. The rules are designed to simulate the propagation of COVID-19 disease through a bounded area. Our proposed CA rules are novel in many respects. For on, the classification of neighbors to nearest neighbors and range of neighbors based on cellular layers is explained. Moreover, the concepts of time generation and access time are deployed for the first time to model the propagation of the disease over time in this work. Further details of the proposed model including the topology of the defined area, the initial states of the cells and four-layer transfer mechanism are explained as well. This work may be considered a criterion of spreading for COVID-19 from point source in a defined population area. The results of the proposed algorithm represent the percentage of the population whose infectious status is described by different cellular state objects after a defined generation time. The results are compared under different circumstances and analyzed equanimity.

Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model

Current experimental verification, computational modeling, and machine learning methods for predicting corrosion inhibition efficiency (IE) are limited to specific inhibitor categories with high cost and poor generalization. In this study, a cross-category corrosion inhibitor dataset is constructed and a three-level direct message passing neural network (3 L–DMPNN) model using molecular structure information that integrates atomic-level, chemical bond-level, and molecular-level features to predict the IEs of compounds in a specific environment is established. This work demonstrates that the 3 L–DMPNN model can predict IEs of cross-category corrosion inhibitors from other independent literature and experimental dataset effectively and quickly.

Comparative study of oxidants in gold cyanidation: A molecular picture

Oxygen plays a crucial role in the oxidation of gold in cyanidation. However, gold can be dissolved in oxygen free solutions by other oxidants. Here, the oxidizing potential of two other oxidants including peroxide (H2O2) and iron hydroxide (Fe(OH)4-) as the dominant Fe specie in alkaline cyanide solutions has been compared with oxygen. For this purpose, a computational modelling method using DFT has been used with BLYP functional. The interaction energy of O2,H2O2 and Fe(OH)4- with the 111 surface of Au is calculated and compared in absence and presence of cyanide ion. Oxygen adsorption energy was higher than the other species with energy values of −50. The H2O2 and the Fe(OH)4-, like oxygen, had a positive effect on the interaction of cyanide with 111 surface as well.

Analytical Model with Independent Control of Load–Displacement Curve Branches for Brittle Material Strength Prediction Using Pre-Peak Test Loads

The relevance of problems related to the fracturing of engineering materials and structures will not decrease over time. Fracture mechanics methods continue to be developed, which, combined with numerical methods of computer modeling, are implemented in software packages. However, this is only one facet of the complex of actual problems related to modeling and analyzing the behavior of brittle materials. No less important are the problems of developing not only numerical, but also new analytical models. In this paper, analytical models of only one class are considered, the distinguishing feature of which is that they describe the full load–strain curve using only one equation. However, the determination of model parameters requires tests for which the destruction of the test object is necessary, which may be unacceptable if controlled destruction is technically impossible or economically unreasonable. At the same time, in practice, it is possible to obtain values of stresses and strains caused by loads smaller than the peak load. Pre-peak loads can be used to predict strength using numerical methods, but it is desirable to have a suitable analytical model to extend the capabilities and to reduce the cost of applied research. Such a model was not found in the known literature, which motivated this work, which aims to modify the analytical model to predict strength and the full load–displacement (or stress–strain) curve using only pre-peak loading. This study is based on the analysis of known data and synthesis using mathematical modeling and fracture mechanics. The input data for the model do not include the particle size distribution and other physical and mechanical properties of the components of the material under study. These properties may remain unknown, but their influence is taken into account indirectly according to the “black box” methodology. Restrictions of the scope of the model are defined. The simulation results are consistent with experiments known from the literature.

Mathematical Simulation of Electron Transport in the Primary Photosynthetic Processes.

Summarized results of investigation of regulation of electron transport and associated processes in the photosynthetic membrane using methods of mathematical and computer modeling carried out at the Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, are presented in this review. Detailed kinetic models of processes in the thylakoid membrane were developed using the apparatus of differential equations. Fitting of the model curves to the data of spectral measurements allowed us to estimate the values of parameters that were not determined directly in experiments. The probabilistic method of agent-based Monte Carlo modeling provides ample opportunities for studying dynamics of heterogeneous systems based on the rules for the behavior of individual elements of the system. Algorithms for simplified representation of Big Data make it possible to monitor changes in the photosynthetic apparatus in the course of culture growth in a photobioreactor and for the purpose of environmental monitoring. Brownian and molecular models describe movement and interaction of individual electron carrier proteins and make it possible to study electrostatic, hydrophobic, and other interactions leading to regulation of conformational changes in the reaction complexes. Direct multiparticle models explicitly simulate Brownian diffusion of the mobile protein carriers and their electrostatic interactions with multienzyme complexes both in solution and in heterogeneous interior of a biomembrane. The combined use of methods of kinetic and Brownian multiparticle and molecular modeling makes it possible to study the mechanisms of regulation of an integral system of electron transport processes in plants and algae at molecular and subcellular levels.

Analysis of acoustic direction finding methods for unmanned aerial vehicles

Currently, classical means of detecting objects do not provide the necessary efficiency for detecting small UAVs, and acoustic location among the known methods for their observation is the most cost-effective solution.
 The article analyzes the well-known methods of direction finding of acoustic signals in order to select algorithms for processing UAV signals. Obtaining qualitative indicators of the analyzed algorithms was carried out by the method of statistical computer modeling in the Matlab environment.
 Based on the simulation results, it is shown that classical methods are the most stable under conditions of low signal-to-noise ratios. The GCC-PHAT direction finding algorithm, based on determining the difference in the time of arrival of a signal at spaced points, is computationally economical and simple enough to determine the direction to the UAV, but it is not capable of distinguishing more than one radiation source within the diagram orientation. Beamforming methods are also relatively easy to implement and computationally efficient, and are more robust at low signal-to-noise ratios. The SRP-NAM algorithm has a greater accuracy in determining angles than SRP-PHAT, so it can be an adequate replacement for the SRP-PHAT algorithm.
 High-resolution methods provide better directional resolution than classical methods, which, in the case of a limited microphone array aperture, is a positive factor in the design of an UAV direction finding station. High resolution methods were considered: non-coherent MUSIC, non-coherent normalized MUSIC and TOPS method. It is shown that incoherent MUSIC gives poor results in distinguishing close UAV signals, since unequal estimates of the entire frequency range are concentrated during bearing formation. The incoherent normalized MUSIC algorithm is able to efficiently use the entire frequency range of the UAV acoustic signal. The TOPS algorithm is inferior to the incoherent normalized MUSIC algorithm, and on the other hand, it does not require a priori estimates of the number of radiation sources.

Designing of Thiazolidinones for COVID-19 and its Allied Diseases: An In silico Evaluation.

In silico studies in terms of density functional theory (DFT), molecular docking, and ADMET (absorption, distribution, metabolism, excretion and toxicity) were performed for 55 thiazolidinones compounds derived from different amines and aldehydes. DFT is a computational quantum mechanical modeling method used to predict the various properties of the compounds. Different parameters such as Electronegativity (x), Chemical Hardness (ŋ), Chemical Potential (μ), Ionization potential (IP), and Electron Affinity (EA), etc. were calculated by Koopmans theorem. The compounds were docked with Molecular Operating Environment (MOE) software using already reported PDB files of BChE, AChE, and α‐glucosidase. To analyze the Spike Glycoprotein of SARS‐Cov‐2 and heterocyclic compounds, molecular interactions study was carried out between Spike Glycoprotein of SARS‐Cov‐2 (6VXX) and 55 synthetic heterocyclic compounds. It was performed by the utilization of PyRx Virtual Screening Tool and AutoDock Vina based virtual environment was used in PyRx. Maximum binding affinity was observed with compound A7 which was −8.7 kcal/mol and then with A5 which was −8.5 respectively. In the case of the AChE enzyme, B5 has a maximum docking score of −12.9027 kcal/mol while C7 depicted the maximum score for the BChE enzyme with a value of −8.6971 kcal/mol. The docking studies revealed that C6 compound has maximum binding capacity toward glucosidase (−14.8735 kcal/mol). ADMET properties of under consideration compounds were determined by Swiss online‐based software which concluded that these molecules have a drug‐like properties and having no violation.

Computational modelling and simulation to mitigate the risk of daylight exposure in tropical museum buildings

In museum design and operation, daylight is typically discouraged due to high risk of damaging the display objects. However, past studies in high-latitude regions have shown the possibility to apply daylight in museums, so long as it is carefully planned, and the display objects are not from the category of high responsive materials. In the tropical region, the influence of daylighting on light exposure on museum objects is still unknown. This study therefore aims to assess and mitigate the impact of annual daylight exposure on objects with low responsive materials in a tropical daylit museum building. Annual daylight modelling and simulation are performed to achieve the objective, followed with Morris sensitivity analysis and Mahalanobis distance classifier to optimise the outcome. It is found that either WWR or glazing transmissivity gives the greatest influence on the performance indicators. Based on the proposed optimisation algorithm, it is possible to determine the optimum solutions satisfying the performance indicators target, for a certain opening type. Overall, the contribution of this study is the proposed computational modelling and simulation methods to mitigate the exposure risk while optimising daylight as a renewable energy source.

A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2

PurposeNo method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers. MethodTo assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load). ResultsSimulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A vs. viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A vs. viral variant A with masks) given partial data aggregated across multiple cities. ConclusionsThis novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with nonpharmaceutical interventions and emergent viral strains.

Case tests as a tool for identifying of teacher’s professional deficits in the interpretation of complex knowledge

Introduction. The formation of functional (mathematical) literacy of students has been the basic trend of mathematical education in secondary school in recent years in connection with the results of the international PISA testing. Pedagogical experience, theory and practice, requests and challenges of real life show that fundamental mathematical abilities and situations of overcoming difficulties in mastering complex knowledge play a central role in various levels determining in the formation of mathematical literacy success. The purpose of the study is to identify the subject and methodological deficiencies of teachers in the interpretation of complex knowledge as a factor in the formation of mathematical literacy of students, using the original competence meters in case tests e form and using Google Forms. Materials and methods. Materials: diagnostic case-tests; online service Google Forms. Methods of descriptive statistics: arithmetic mean, standard deviation, etc. Results. It was revealed that teacher’s majority of (80%) adequately assesses their scientific and methodological potential, are tolerant of innovations in didactics. Low average results of teachers (43.7%) are shown, indicating the presence of subject deficits in mathematics teachers in the field of applying mathematical knowledge in solving practice-oriented tasks. The average results of teachers' methodological deficits (62.3%) indicate the teacher's readiness for professional self-development, the ability to adapt and knowledge using of science’s modern problems in solving practice-oriented tasks with student’s motivation and self-organization effects. Conclusions. The subject and methodological competencies of a teacher should cover the content and ways of complex knowledge adapting (modern achievements in science) to school mathematics, the variability of forms and means of generalized constructs presenting, knowledge of mathematical and computer modeling methods, knowledge of samples and standards of popular applications of complex knowledge to real life, technologies and industries. Practice-oriented tasks arise in teaching mathematics at the same time as a component of student’s project and research activities.

Computational modeling method to estimate secondhand exposure potential from exhalations during e-vapor product use under various real-world scenarios

Potential secondhand exposure of exhaled constituents from e-vapor product (EVP) use is a public health concern. We present a computational modeling method to predict air levels of exhaled constituents from EVP use. We measured select constituent levels in exhaled breath from adult e-vapor product users, then used a validated computational model to predict constituent levels under three scenarios (car, office, and restaurant) to estimate likely secondhand exposure to non-users. The model was based on physical/thermodynamic interactions between air, vapor, and particulate phase of the aerosol. Input variables included space setting, ventilation rate, total aerosol amount exhaled, and aerosol composition. Exhaled breath samples were analyzed after the use of four different e-liquids in a cartridge-based EVP. Nicotine, propylene glycol, glycerin, menthol, formaldehyde, acetaldehyde, and acrolein levels were measured and reported based on a linear mixed model for analysis of covariance. The ranges of nicotine, propylene glycol, glycerin, and formaldehyde in exhaled breath were 89.44–195.70 µg, 1199.7–3354.5 µg, 5366.8–6484.7 µg, and 0.25–0.34 µg, respectively. Acetaldehyde and acrolein were below detectable limits; thus, no estimated exposure to non-EVP users is reported. The model predicted that nicotine and formaldehyde exposure to non-users was substantially lower during EVPs use compared to cigarettes. The model also predicted that exposure to propylene glycol, glycerin, nicotine and formaldehyde among non-users was below permissible exposure limits.

Some aspects of the development of information-cybernetic thinking in pedagogical university students when studying the basics of cybernetics

The rapid development of various information-cybernetic systems and their introduction into all spheres of human life and activity necessitate a deeper study of the foundations of informatics and cybernetics in schools and pedagogical universities. Conducting an analysis of the features of the formation of information-cybernetic thinking of students of pedagogical universities and the development of training tasks that contribute to a deeper mastery of the basic provisions of cybernetics are key aspects for the teacher. At the same time, the method of analyzing educational and scientific-methodical literature, methods of programming and computer modeling, as well as mathematical methods are used. The main types of educational tasks that contribute to the development of informationcybernetic thinking of students are selected: 1) explanation of the principles of cybernetics and their justification on the example of specific technical systems; 2) explanation of the general principles of functioning of various types of information-cybernetic systems; 3) explanation of the operation of specific automatic control systems (ACS) and their individual components; 4) the invention of “new” control systems with desired properties; 5) creation of technical control systems and experimental study of their work; 6) creation of computer models of control systems and transmission links. Solutions of educational problems involving computer modeling of automatic control systems and various transmission links in the Pascal ABC environment are discussed. The proposed elements of the methodology can be used in any university when teaching students the basics of cybernetics.

Software Modeling Environment for Solving Problems of Structurally Inhomogeneous Materials

In this scientific study, the main properties of structurally inhomogeneous materials are predicted by computer modelling methods. The automatic combination of a scanning microscope and a program cell makes it possible to view the procedure in detail before and after etching with the necessary increase in resolution. Based on the results obtained, we constructed a graphical dependence of the particle sizes of 40 XН steel on the iterative process, and also studied in detail the procedure for the effect of different particle sizes on porosity. We modelled 2D and 3D drawings of the PRT – 7 shaft part. We justified the predicted number of properties, in particular: porosity, particle shape, grain size, microstructure of the sample surface, pre-etching process, post-etching process, as well as the main advantages of the iterative process.

Possibilities of personalized finite element segmental analysis of the cervical spine for predicting the course of dorsopathy

Introduction. The use of mathematical modeling methods in clinical practice will make it possible to identify the pathogenetic forms of dorsopathies and thus reasonably use the concept of targeted treatment in the management of patients of this category.Aim. To evaluate the possibilities of finite element segmental analysis of the cervical spine for personalized treatment and prediction of the course of dorsopathies.Material and methods. Based on the combined data of computed and magnetic resonance imaging of the patient (female, born in 1951), a model of the C5 – C7 segment was generated, including: vertebrae C5, C6, C7, IVD, anterior and posterior longitudinal ligaments, two pairs of facet joints, spinal cord, nuchal ligament. Computer modeling and finite element method were used to analyze the stress-strain state of the cervical spine of a patient with degenerative-dystrophic changes in the C2 – C7 segments. In the Abaqus/CAE 6.14 software, finite element analysis of the C5 – C7 stress-strain state was carried out in the state of flexion, rotation and compression. The data obtained during compression were compared with previous experiments in silico and in vitro for the norm.Results. For each state, stress and displacement diagrams, load-displacement curves, stress profiles in the MPD were obtained. The axial mobility of the segment under compressive load is two times lower compared to the norm under the same boundary conditions and material models. The degree of involvement of the spinal cord in conflicts with the surrounding anatomical structures was studied. When the model was rotated to the right, conflicts were observed between the spinal cord roots and the bone structures of the vertebrae in the foraminal zones, as well as at the level of the C5 – C6 and C6 – C7 discs with the left posterolateral surfaces of the fibrous rings. When the model was turned to the left, conflicts of the spinal cord were observed in all foraminal zones, as well as at the level of the C6 – C7 disc with the left posterolateral surface of the fibrous rings. Based on the data on stresses in the studied segment, further development of dorsopathies and degenerative changes in the cervical spine was predicted.Conclusions. The use of finite element segmental analysis of the cervical spine creates objective prerequisites for the formation of a combined personalized treatment and prediction of the course of dorsopathies.

A computational modeling method for root canal endoscopy using a specific CBCT filter: A new era in the metaverse of endodontics begins.

A contemporary technological revolution has started a new era in the metaverse of Endodontics, a world of virtual operational possibilities that use an exact replica of the natural structures of the maxillofacial complex. This study describes a modeling method for root canal endoscopy using modern cone-beam CT (CBCT) software in a series of clinical cases. The method consists in acquiring thin CBCT slices (0.10mm) in the coronal, sagittal, and axial planes. A specific 3D volume filter, the pulp cavity filter of the e-Vol DX CBCT software, was used to navigate anatomical root canal microstructures, and to scan them using root canal endoscopy. The pulp cavity filter should be set to synchronize CBCT scans from 2D mode - multiplanar reformations (MPR) - to 3D mode - volumetric reconstruction. This filter, when adopting the option of volumetric reconstruction, the developed algorithm leaves the dentin density in transparent mode so that the pulp cavity may be visualized. The algorithm applied performs the suppression (visual) of areas with dentin density. This ensures 3D visualization of the slices and the microanatomy of the root canal, as well as a dynamic navigation throughout the pulp cavity. This computational modeling method adds new resources to Endodontics, which may impact the predictability of root canal treatments positively. The virtual visualization of the internal anatomy of an exact replica of the canal ensures better communications, reliability, and clinical operationalization. Root canal endoscopy using this novel CBCT filter may be used for clinical applications together with innovative digital and virtual-reality resources that will be naturally incorporated into the principles of Endodontics.

Modeling the Temperature Field in Frozen Soil under Buildings in the City of Salekhard Taking into Account Temperature Monitoring

Most residential buildings and capital structures in the permafrost zone are constructed on the principle of maintaining the frozen state of the foundation soils. The changing climate and the increasing anthropogenic impact on the environment lead to changes in the boundaries of permafrost. These changes are especially relevant in the areas of piling foundations of residential buildings and other engineering structures located in the northern regions since they can lead to serious accidents caused by the degradation of permafrost and decrease the bearing capacity of the soil in such areas. Therefore, organization of temperature monitoring and forecasting of temperature changes in the soil under the buildings is an actual problem. To solve this problem, we use computer simulation methods of three-dimensional nonstationary thermal fields in the soil in combination with real-time monitoring of the temperature of the soil in thermometric wells. The developed approach is verified by using the temperature monitoring data for a specific residential building in the city of Salekhard. Comparison of the results of numerical calculations with experimental data showed good agreement. Using the developed computer software, nonstationary temperature fields under this building are obtained and, on this basis, the bearing capacities of all piles are calculated and a forecast of their changes in the future is given. To avoid decreasing the bearing capacity of piles it is necessary to prevent the degradation of permafrost and to supply the thermal stabilization of the soil. The proposed approach, based on a combination of the soil temperature monitoring and computer modeling methods, can be used to improve geotechnical monitoring methods.

Synthesis, stereochemistry and in vitro STD NMR and in silico HIV-1 PR enzyme-binding potential of MBH-derived inhibitors

Aza-Michael reactions of a pyridine-3-carbaldehyde-derived Morita-Baylis-Hillman (MBH) adduct with various amines have afforded a series of 10 diastereomeric products, stereochemical analysis of which has been achieved using a combination of NMR (1D, 2D and NOESY) and computer modelling methods. Saturation Transfer Difference (STD) 1H NMR spectroscopy and in silico molecular docking studies have been used to explore the HIV-1 protease sub-type C enzyme binding potential of these compounds in five different HIV-1 PR enzyme receptors.

Development of technology for minced beef products using by-products

The article studies the relevance and necessity of using by-products in the technology of food production, outlines the technology for the production of minced beef products using by-products, in particular, beef lung for the production of cutlet mass. The results of a sociological survey and analysis of consumer preferences of the population of Murmansk in relation to finished products and semi-finished products using by-products in technology are presented. A basic recipe for chopped products with by-products has been developed and its optimization has been carried out using the method of computer modeling. To search for a formulation close to the optimal formulation, the method of rotatable orthohanolic experiment planning was used. The results were processed according to standard methods using the Datafit 9.0 computer program. Experimental results of mathematical modeling are presented. The analysis of the obtained results has been carried out. It was found that chopped products made according to the specified recipe are well formed, during heat treatment they retain their shape, the taste of offal is weakly noticeable.