Invariant states for the time dynamics of a class of multidimensional lattice quantum Fermi systems

Subject. The working time of workers at any stage of economic development is a value reflecting the level of labor productivity. Any progress in productivity contributes to changes in the volume of labor costs and the number of employed. Depending on the relationship between the total volume of labor costs and the number of employed, the duration of working time per one worker may change (it may increase, decrease, or remain unchanged). Objectives. The study aims to confirm the importance of such a macroeconomic indicator as the number of employed in varying working hours. Methods. The study rests on the comparative analysis of countries with developed economies based on some indicators like dynamics of the working time fund, dynamics of the number of employed, average number of hours worked during the year per employee, etc. The analyzed timespan is 25 years (from 1991 to 2016). Results. The comparative analysis revealed that in the non-production sphere and the economy as a whole the macroeconomic determinants correlate so that the length of working time per worker reduces. When considering the analysis results for the manufacturing sector, no single trend was identified. Conclusions. One of the key factors affecting the change in working hours is the number of employed. The relationship between the working time fund and the number of employed directly determines the dynamics of working time per worker.

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Computational study of low energy excitations in cold atomic Fermi systems

10.26226/morressier.5f7839d09b74b699bf3945d9 ◽

2020 ◽

Author(s):

Kaelyn Dauer

Keyword(s):

Computational Study ◽

Low Energy ◽

Fermi Systems

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Second international scientific conference «Present fundamental problems of vegetation classification» (Yalta, 15–20 September 2019)

Vegetation of Russia ◽

10.31111/vegrus/2019.37.135 ◽

2019 ◽

pp. 135-142

Author(s):

K. V. Ivanova ◽

A. M. Lapina ◽

V. V. Neshataev

Keyword(s):

Plant Communities ◽

National Archive ◽

Botanical Garden ◽

Vegetation Classification ◽

Scientific Conference ◽

Scientific Journals ◽

Time Dynamics ◽

The Past ◽

Research Organizations

The 2nd international scientific conference «Fundamental problems of vegetation classification» took place at the Nikitskiy Botanical Garden (Yalta, Republic of Crimea, Russia) on 15–20 September 2019. There were 56 participants from 33 cities and 43 research organizations in Russia. The conference was mostly focused on reviewing the success in classification of the vegetation done by Russian scientists in the past three years. The reports covered various topics such as classification, description of new syntaxonomical units, geobotanical mapping for different territories and types of vegetation, studies of space-time dynamics of plant communities. The final discussion on the last day covered problems yet to be solved: establishment of the Russian Prodromus and the National archive of vegetation, complications of higher education in the profile of geobotany, and the issue of the data leakage to foreign scientific journals. In conclusion, it was announced that the 3rd conference in Nikitskiy Botanical Garden will be held in 2022.

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Study of the operation of an artificial neural network that works in the electric network in order to prevent emergency conditions.

Journal of Electrical and power engineering ◽

10.31474/2074-2630-2020-1-48-54 ◽

2020 ◽

Vol 14 (1) ◽

pp. 48-54

Author(s):

D. Ostrenko ◽

Keyword(s):

Neural Networks ◽

Learning Algorithm ◽

Electricity Consumption ◽

Electrical Energy ◽

Energy System ◽

Electrical Networks ◽

Electric Networks ◽

Electric Network ◽

Time Dynamics ◽

Emergency Situations

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models.

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