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This paper describes the importance of theoretical understanding of modern social health, educational and environmental behavior of students in the digital age. The purpose of this research is to analyze the concepts and trends of social health and environmental behavior of students in the context of modern social digitalization process. The paper deals with the framework and methodology of modern social and philosophical theories devoted to the problems of digital society and digital technologies. The theoretical and methodological approaches analyzed to the study of modern social digitalization processes, their negative and positive trend, and the impact of these trends on social health and environmental behavior of Students in education. In this paper, the process of digitalization is inconsistent: providing new technological opportunities for communication and education, including environmental education and students' environmental projects that has a negative impact on consciousness and behavior of young people. The results showed that there is a need to form creative personality in education through the modern digital era. The further research stated in the field of social health and formation of students' environmental behavior in the context of modern information, communication technologies and development of digital society in the education.

The program for searching areas of a sea body of water according to specified characteristics is presented. The parameters are the ranges of potential water temperature and salinity, as well as the date range, the initial data are three-dimensional arrays of relevant characteristics in the netCDF format (work with direct access data is provided). Copernicus marine reanalysis was used. The search is carried out at boundary points, the number of days from a given range in which the corresponding conditions are met (temperature and salinity from a given range) is calculated. Maps and binary arrays with metafiles for graphical display are created. The algorithm was tested in the White Sea and the southern part of the Barents Sea. According to calculation the Pomorskiy Coast of the Onezhskiy Bay is recognized as the most promising for trout farming in the White Sea when combined with “supporting” coastal infrastructure. However, to select the optimal site for mariculture using mathematical methods, further field work is required.

In this paper, we present a model of ocean ecosystem to serve as a component of the global Earth System Model. The global ocean circulation model was forced by a fixed atmospheric influence following the CORE-II protocol. Arbitrary number of advected scalars allowed configuring biogeochemical block only balancing between detailed description and computational performance. A quasi-equilibrium model of an ecosystem in a seawater column, suitable for computationally complex climate calculations, was developed, taking into account nitrogen and/or phosphorus based nutrients, gravitational deposition of suspended organic matter and transformation of carbon compounds. A software interface was developed in order to seamlessly interact between geophysical and biogeochemical components of the model. Test calculations have been performed on the INM RAS computing cluster for 160 years, and the results are presented. It is shown that the calculated fields do not contradict typical distributions available in literature and open atlases. Seasonality in plankton production, dissolved organic carbon, CO2 concentrations and suspended matter is clearly observed.

In this paper, we present the current stage of development of the INM-CM Earth system model family by the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. The major change from the previous model version INM-CM5 is a new computational platform for the Earth System modelling. We describe the main parts of this digital platform, such as ocean-atmosphere coupling, version control, compilation/configuration, and automated testing subsystems. We also discuss major modifications of the physical parts of the climate model whereby the model simulations of observed climate were significantly improved as well as the model computational performance. [ABSTRACT FROM AUTHOR]

We survey the areas of Desktop Grid task scheduling that seem to be insufficiently studied so far and are promising for efficiency, reliability, and quality of Desktop Grid computing. These topics include optimal task grouping, “needle in a haystack” paradigm, game-theoretical scheduling, domain-imposed approaches, special optimization of the final stage of the batch computation, and Enterprise Desktop Grids.

In this paper, we present a parallel version of the finite element model of the Arctic Ocean (FEMAO) configured for the White sea and based on the MPI technology. This model consists of two main parts: an ocean dynamics model and a surface ice dynamics model. These parts are very different in terms of the amount of computations because the complexity of the ocean part depends on the bottom depth, while that of the sea-ice component does not. In the first step, we decided to locate both submodels on the same CPU cores with the common horizontal partition of the computational domain. The model domain is divided into small blocks, which are distributed over the CPU cores using Hilbert-curve balancing. Partition of the model domain is static (i.e., computed during the initialization stage). There are three baseline options: single block per core, balancing of 2D computations and balancing of 3D computations. After showing parallel acceleration for particular ocean and ice procedures, we construct the common partition, which minimizes joint imbalance in both submodels. Our novelty is using arrays shared by all blocks that belong to a CPU core instead of allocating separate arrays for each block, as is usually done. Computations on a CPU core are restricted by the masks of not-land grid nodes and block-core correspondence. This approach allows us to implement parallel computations into the model that are as simple as when the usual decomposition to squares is used, though with advances of load balancing. We provide parallel acceleration of up to 996 cores for the model with resolution 500×500×39 in the ocean component and 43 sea-ice scalars, and we carry out detailed analysis of different partitions on the model runtime. [ABSTRACT FROM AUTHOR]