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The mechanical stability of cell adhesion protein Cadherin with explicit model of water is studied by the method of molecular dynamics. The protein in apo-form and with the ions of different types (Ca²⁺, Mg²⁺, Na⁺, K⁺) was unfolding with a constant speed by applying the force to the ends. Eight independent experiments were done for each form of the protein. It was shown that univalent ions stabilize the structure less than bivalent one under mechanical unfolding of the protein. A model system composed of two amino acids and the metal ion between them demonstrates properties similar to that of the cadherin in the stretching experiments. The systems with potassium and sodium ions have less mechanical stability then the systems with calcium and magnesium ions.

The data of episodic field measurements of carbon dioxide balance components (soil respiration — R_soil, ecosystem respiration — R_eco, net ecosystem exchange — NEE) of hayfields under use and abandoned one are interpreted by modelling. The field measurements were carried within five field campaigns in 2018 and 2019 on the drained part of the Dubna Peatland in Taldom District, Moscow Oblast, Russia. The territory is within humid continental climate zone. Peatland drainage was done out for milled peat extraction. After extraction was stopped, the residual peat deposit (1–1.5 m) was ploughed and grassed (Poa pratensis L.) for hay production. The current ground water level (GWL) varies from 0.3–0.5 m below the surface during wet and up to 1.0 m during dry periods. Daily dynamics of CO2 fluxes was measured using dynamic chamber method in 2018 (August) and 2019 (May, June, August) for abandoned ditch spacing only with sanitary mowing once in 5 years and the ditch spacing with annual mowing. NEE and Reco were measured on the sites with original vegetation, and Rsoil — after vegetation removal. To model a seasonal dynamics of NEE, the dependence of its components (Reco, Rsoil, and Gross ecosystematmosphere exchange of carbon dioxide — GEE) from soil and air temperature, GWL, photosynthetically active radiation, underground and aboveground plant biomass were used. The parametrization of the models has been carried out considering the stability of coefficients estimated by the bootstrap method. R2 (α = 0.05) between simulated and measured Reco was 0.44 (p < 0.0003) on abandoned and 0.59 (p < 0.04) on under use hayfield, and GEE was 0.57 (p < 0.0002) and 0.77 (p < 0.00001), respectively. Numerical experiments were carried out to assess the influence of different haymaking regime on NEE. It was found that NEE for the season (May 15 – September 30) did not differ much between the hayfield without mowing (4.5±1.0 tC·ha–1·season–1) and the abandoned one (6.2±1.4). Single mowing during the season leads to increase of NEE up to 6.5±0.9, and double mowing — up to 7.5±1.4 tC·ha–1·season–1. This means increase of carbon losses and CO2 emission into the atmosphere. Carbon loss on hayfield for both single and double mowing scenario was comparable with abandoned hayfield. The value of removed phytomass for single and double mowing was 0.8±0.1 tC·ha^–1·season^–1 and 1.4±0.1 (45% carbon content in dry phytomass) or 3.0 and 4.4 t·ha^–1·season^–1 of hay (17% moisture content). In comparison with the fallow, the removal of biomass of 0.8±0.1 at single and 1.4±0.1 tC·ha^–1·season^–1 double mowing is accompanied by an increase in carbon loss due to CO2 emissions, i.e., the growth of NEE by 0.3±0.1 and 1.3±0.6 tC·ha–1·season–1, respectively. This corresponds to the growth of NEE for each ton of withdrawn phytomass per hectare of 0.4±0.2 tС·ha–1·season–1 at single mowing, and 0.9±0.7 tС·ha^–1·season^–1 at double mowing. Therefore, single mowing is more justified in terms of carbon loss than double mowing. Extensive mowing does not increase CO2 emissions into the atmosphere and allows, in addition, to “replace” part of the carbon loss by agricultural production.

The purpose of this study is to develop a methodology for change points detection in time series, including financial data. The theoretical basis of the study is based on the pieces of research devoted to the analysis of structural changes in financial markets, description of the proposed algorithms for detecting change points and peculiarities of building classical and deep machine learning models for solving this type of problems. The development of such tools is of interest to investors and other stakeholders, providing them with additional approaches to the effective analysis of financial markets and interpretation of available data.

To address the research objective, a neural network was trained. In the course of the study several ways of training sample formation were considered, differing in the nature of statistical parameters. In order to improve the quality of training and obtain more accurate results, a methodology for feature generation was developed for the formation of features that serve as input data for the neural network. These features, in turn, were derived from an analysis of mathematical expectations and standard deviations of time series data over specific intervals. The potential for combining these features to achieve more stable results is also under investigation.

The results of model experiments were analyzed to compare the effectiveness of the proposed model with other existing changepoint detection algorithms that have gained widespread usage in practical applications. A specially generated dataset, developed using proprietary methods, was utilized as both training and testing data. Furthermore, the model, trained on various features, was tested on daily data from the S&P 500 index to assess its effectiveness in a real financial context.

As the principles of the model’s operation are described, possibilities for its further improvement are considered, including the modernization of the proposed model’s structure, optimization of training data generation, and feature formation. Additionally, the authors are tasked with advancing existing concepts for real-time changepoint detection.

Dynamic scenarios leading to multistability in the form of continuous families of stable solutions are studied for a system of autonomous differential equations. The approach is based on determining the cosymmetries of the problem, calculating stationary solutions, and numerically-analytically studying their stability. The analysis is carried out for equations of the Lotka –Volterra type, describing the interaction of two predators feeding on two related prey species. For a system of ordinary differential equations of the 4th order with 11 real parameters, a numerical-analytical study of possible interaction scenarios was carried out. Relationships are found analytically between the control parameters under which the cosymmetry linear in the variables of the problem is realized and families of stationary solutions (equilibria) arise. The case of multicosymmetry is established and explicit formulas for a two-parameter family of equilibria are presented. The analysis of the stability of these solutions made it possible to reveal the division of the family into regions of stable and unstable equilibria. In a computational experiment, the limit cycles branching off from unstable stationary solutions are determined and their multipliers corresponding to multistability are calculated. Examples of the coexistence of families of stable stationary and non-stationary solutions are presented. The analysis is carried out for the growth functions of logistic and “hyperbolic” types. Depending on the parameters, scenarios can be obtained when only stationary solutions (coexistence of prey without predators and mixed combinations), as well as families of limit cycles, are realized in the phase space. The multistability scenarios considered in the work allow one to analyze the situations that arise in the presence of several related species in the range. These results are the basis for subsequent analysis when the parameters deviate from cosymmetric relationships.

The article discusses the problem of the influence of the research goals on the structure of the multivariate model of regression analysis (in particular, on the implementation of the procedure for reducing the dimension of the model). It is shown how bringing the specification of the multiple regression model in line with the research objectives affects the choice of modeling methods. Two schemes for constructing a model are compared: the first does not allow taking into account the typology of primary predictors and the nature of their influence on the performance characteristics, the second scheme implies a stage of preliminary division of the initial predictors into groups, in accordance with the objectives of the study. Using the example of solving the problem of analyzing the causes of burnout of creative workers, the importance of the stage of qualitative analysis and systematization of a priori selected factors is shown, which is implemented not by computing means, but by attracting the knowledge and experience of specialists in the studied subject area. The presented example of the implementation of the approach to determining the specification of the regression model combines formalized mathematical and statistical procedures and the preceding stage of the classification of primary factors. The presence of this stage makes it possible to explain the scheme of managing (corrective) actions (softening the leadership style and increasing approval lead to a decrease in the manifestations of anxiety and stress, which, in turn, reduces the severity of the emotional exhaustion of the team members). Preclassification also allows avoiding the combination in one main component of controlled and uncontrolled, regulatory and controlled feature factors, which could worsen the interpretability of the synthesized predictors. On the example of a specific problem, it is shown that the selection of factors-regressors is a process that requires an individual solution. In the case under consideration, the following were consistently used: systematization of features, correlation analysis, principal component analysis, regression analysis. The first three methods made it possible to significantly reduce the dimension of the problem, which did not affect the achievement of the goal for which this task was posed: significant measures of controlling influence on the team were shown. allowing to reduce the degree of emotional burnout of its participants.

The problem of centralized planning for data transmission routes in delay tolerant networks is considered. The original problem is extended with additional requirements to nodes storage and communication process. First, it is assumed that the connection between the nodes of the graph is established using antennas. Second, it is assumed that each node has a storage of finite capacity. The existing works do not consider these requirements. It is assumed that we have in advance information about messages to be processed, information about the network configuration at specified time points taken with a certain time periods, information on time delays for the orientation of the antennas for data transmission and restrictions on the amount of data storage on each satellite of the grouping. Two wellknown algorithms — CGR and Earliest Delivery with All Queues are improved to satisfy the extended requirements. The obtained algorithms solve the optimal message routing problem separately for each message. The problem of validation of the algorithms under conditions of lack of test data is considered as well. Possible approaches to the validation based on qualitative conjectures are proposed and tested, and experiment results are described. A performance comparison of the two implementations of the problem solving algorithms is made. Two algorithms named RDTNAS-CG and RDTNAS-AQ have been developed based on the CGR and Earliest Delivery with All Queues algorithms, respectively. The original algorithms have been significantly expanded and an augmented implementation has been developed. Validation experiments were carried to check the minimum «quality» requirements for the correctness of the algorithms. Comparative analysis of the performance of the two algorithms showed that the RDTNAS-AQ algorithm is several orders of magnitude faster than RDTNAS-CG.

This work is devoted to molecular dynamic modeling of the thermal impact processes on the metal sample consisting of nickel atoms. For the solution of this problem, a continuous mathematical model on the basis of the classical Newton mechanics equations has been used; a numerical method based on the Verlet scheme has been chosen; a parallel algorithm has been offered, and its realization within the MPI and OpenMP technologies has been executed. By means of the developed parallel program, the investigation of thermodynamic equilibrium of nickel atoms’ system under the conditions of heating a sample to desired temperature has been executed. In numerical experiments both optimum parameters of calculation procedure and physical parameters of analyzed process have been defined. The obtained numerical results are well corresponding to known theoretical and experimental data.

Parameter sweep applications are a very important class of applications, which are typically defined as a set of computational experiments over a set of input parameters, each of which is executed with its own parameter combination. These computations arise in many scientific contexts. This article introduces the Parameter Sweep web service that runs such applications in distributed computing environment. Also discussed is the Everest cloud platform, on which this service is built.

This work describes the idea of the system of informational support for the scientific projects and the development of computational task tracking complex. Due to large requirements for computational experiments the problem of presentation of the information about HPC tasks becomes one of the most important. Nonstandard usage of the service desk system as a basis of the computational task tracking and support system can be the solution of this problem. Particular attention is paid to the analysis and the satisfaction of the conflicting requirements to the task tracking complex from the different user groups. Besides the web service kit used for the integration of the task tracking complex and the datacenter environment is considered. This service kit became the main interconnect between the parts of the scientific project support system and also this kit allows to reconfigure the whole system quickly and safely.

The review of the distributed computing infrastructure of the Tier-1 sites for the Alice, ATLAS, LHCb experiments at the LHC is given. The special emphasis is placed on the main tasks and services of the Tier-1 site, which operates in the Kurchatov Institute in Moscow.