22 research outputs found
ΠΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π΄ΠΎΠΌΠΎΡ ΠΎΠ·ΡΠΉΡΡΠ² Π² Π·Π°Π΄Π°ΡΠ°Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΠ½Π΅ΡΠ³ΠΎΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΆΠΈΠ»ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΡΠΎΡΠ°
The aim of the work is to study the problem of optimizing energyΒ consumption and practical application of methods forΒ improvingΒ energy efficiency in the housing sector. Optimization ofΒ energyΒ efficiency management allows to reduce the expenditure ofΒ energyΒ resources in the performance of various works, heating ofΒ buildings, etc. The creation of optimization methods will makeΒ itΒ possible to reduce payments for utilities in a short time, andΒ inΒ general for the industry, will help reduce the consumption ofΒ variousΒ resources and improve the ecological state of the region.Β UnlikeΒ other approaches, the emphasis in this paper is on theΒ convenienceΒ and simplicity necessary for using this technique byΒ the population in households.Β The proposed integrated approach uses methods ofΒ probability theory,Β linear programming, heat exchange models. TheΒ conducted researchΒ confirms the effectiveness of the solutionΒ obtained and can serve asΒ a basis for the creation of training andΒ research stands. The article consists of two parts: the first partΒ analyzes the leading works in this field and identifies the reasonsΒ that make it difficult to apply the solutions proposed in these papers. Further, the statement of the problem was proposed and justified,Β and a number of basic requirements to the mathematical model ofΒ energy consumption, necessary for the constructed technique to beΒ used to optimize energy consumption in households, wereΒ formulated. In the second part, a mathematical model of theirΒ functioning is proposed using examples of specific householdΒ electrical appliances. When researching existing methods forΒ optimizing energy consumption in households, problems wereΒ identified that were difficult to apply these methods in practice andΒ recommendations were obtained that allowed to formulate the basicΒ principles of constructing an optimization technique that wasΒ convenient for practical application. It was shown that whenΒ constructing such a technique, the primary question is the data thatΒ the user can provide. The minimum composition of input data wasΒ determined, according to which the necessary algorithms forΒ optimizing energy consumption were designed. A number ofΒ algorithms for determining some input indicators that are easy to use in households have also been proposed. Thus, the general plan of research in this paper is as follows:β’ carry out grouping of devices by the way of setting functionalrequirements;β’ determine the acceptable composition and type of input data forthe user;β’ define the minimum set of input data for formalizing the limitationof the total power consumption;β’ design optimization algorithms that work with the input dataspecified above.Β The most important results of the work performed are the following:β’ the methodology for forecasting the graph of the maximum totalpower consumption has been developed.β’ methods for optimizing energy consumption for each of the selectedΒ subsets of household appliances have been developed.β’ the optimization algorithms obtained have been simulated, whichΒ showed their operability, efficiency and the possibility of their practicalΒ application without any adaptation.Thus, the article proposes the solution of the problem of optimizationΒ of energy consumption in the housing sector, oriented to practicalΒ application.Π¦Π΅Π»ΡΡ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈΒ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΠΈΒ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΠ½Π΅ΡΠ³ΠΎΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π² ΠΆΠΈΠ»ΠΈΡΠ½ΠΎΠΌ ΡΠ΅ΠΊΡΠΎΡΠ΅.Β ΠΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΡΒ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ½Π΅ΡΠ³ΠΎΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠΌΠ΅Π½ΡΡΠ°ΡΡ ΡΠ°ΡΡ
ΠΎΠ΄ΠΎΠ²Π°Π½ΠΈΠ΅Β ΡΠ½Π΅ΡΠ³ΠΎΡΠ΅ΡΡΡΡΠΎΠ² ΠΏΡΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡ, ΠΎΡΠΎΠΏΠ»Π΅Π½ΠΈΠ΅ Π·Π΄Π°Π½ΠΈΠΉ ΠΈ Ρ.Π΄. Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅Β ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡΒ Π² ΠΊΠΎΡΠΎΡΠΊΠΈΠ΅ ΡΡΠΎΠΊΠΈ ΡΠ½ΠΈΠ·ΠΈΡΡ ΠΏΠ»Π°ΡΠ΅ΠΆΠΈ Π·Π° ΠΊΠΎΠΌΠΌΡΠ½Π°Π»ΡΠ½ΡΠ΅ ΡΡΠ»ΡΠ³ΠΈ,Β Π°Β Π² ΡΠ΅Π»ΠΎΠΌ Π΄Π»Ρ ΠΎΡΡΠ°ΡΠ»ΠΈ, Π±ΡΠ΄Π΅Ρ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΠΎΠ²Π°ΡΡ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ²Β ΠΈ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡΒ ΡΠ΅Π³ΠΈΠΎΠ½Π°. Π ΠΎΡΠ»ΠΈΡΠΈΠΈ ΠΎΡ Π΄ΡΡΠ³ΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ², Π°ΠΊΡΠ΅Π½Ρ Π²Β Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅Β ΡΡΠ°Π²ΠΈΡΡΡ Π½Π° ΡΠ΄ΠΎΠ±ΡΡΠ²ΠΎ ΠΈ ΠΏΡΠΎΡΡΠΎΡΡ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ Π΄Π»Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΡΠΎΠΉΒ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΠ΅ΠΌ Π² Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π°Ρ
.Β Π ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠΌ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΌ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π΅Β ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΡΒ ΡΠ΅ΠΎΡΠΈΠΈ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ, Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΌΠΎΠ΄Π΅Π»ΠΈΒ ΡΠ΅ΠΏΠ»ΠΎΠΎΠ±ΠΌΠ΅Π½Π°. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π΅Ρ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΠΎΠ³ΠΎΒ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΌΠΎΠΆΠ΅Ρ ΡΠ»ΡΠΆΠΈΡΡ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ Π΄Π»ΡΒ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΡΠ΅Π±Π½ΠΎ-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΡ
ΡΡΠ΅Π½Π΄ΠΎΠ².Β Π‘ΡΠ°ΡΡΡΒ ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· Π΄Π²ΡΡ
ΡΠ°ΡΡΠ΅ΠΉ: Π² ΠΏΠ΅ΡΠ²ΠΎΠΉ ΡΠ°ΡΡΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Β Π°Π½Π°Π»ΠΈΠ· Π²Π΅Π΄ΡΡΠΈΡ
ΡΠ°Π±ΠΎΡ Π² ΡΡΠΎΠΉ ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ΅ ΠΈΒ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΏΡΠΈΡΠΈΠ½Ρ, Π·Π°ΡΡΡΠ΄Π½ΡΡΡΠΈΠ΅ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΡ
Π² ΡΡΠΈΡ
Β ΡΠ°Π±ΠΎΡΠ°Ρ
Β ΡΠ΅ΡΠ΅Π½ΠΈΠΉ. ΠΠ°Π»Π΅Π΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΈ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Π° ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠ°Β Π·Π°Π΄Π°ΡΠΈ ΠΈ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½ ΡΡΠ΄Β ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΠΉ ΠΊ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ
Π΄Π»Ρ ΡΠΎΠ³ΠΎ,Β ΡΡΠΎΠ±Ρ ΡΠΊΠΎΠ½ΡΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΡ ΠΌΠΎΠΆΠ½ΠΎ Π±ΡΠ»ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΒ Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈΒ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π² Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π°Ρ
. ΠΠΎΒ Π²ΡΠΎΡΠΎΠΉ ΡΠ°ΡΡΠΈ Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ°Ρ
ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
Β Π±ΡΡΠΎΠ²ΡΡ
ΡΠ»Π΅ΠΊΡΡΠΎΠΏΡΠΈΠ±ΠΎΡΠΎΠ² ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΈΡ
ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ.Β ΠΡΠΈΒ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π² Π΄ΠΎΠΌΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π°Ρ
Β Π±ΡΠ»ΠΈ Π²ΡΡΠ²Π»Π΅Π½Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ,Β Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΠΈΠ΅ΡΡ Π² ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π½Π°Β ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°ΡΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΒ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ,Β ΡΠ΄ΠΎΠ±Π½ΠΎΠΉ Π΄Π»Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ. ΠΡΠ»ΠΎ ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎΒ ΠΏΡΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΠΈ ΡΠ°ΠΊΠΎΠΉ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΡΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²ΠΎΠΏΡΠΎΡΒ ΠΎ Π΄Π°Π½Π½ΡΡ
, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠΆΠ΅ΡΒ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²ΠΈΡΡ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ. ΠΡΠ»Β ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ ΡΠΎΡΡΠ°Π² Π²Ρ
ΠΎΠ΄Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΏΠΎ ΠΊΠΎΡΠΎΡΡΠΌ ΡΠΊΠΎΠ½ΡΡΡΡΠΈΡΠΎΠ²Π°Π½Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈΒ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ. Π’Π°ΠΊ ΠΆΠ΅ Π±ΡΠ» ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΡΡΠ΄ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ²Β ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
Π²Ρ
ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅Β Π»Π΅Π³ΠΊΠΎΒ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π² Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π°Ρ
.Β Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΠΎΠ±ΡΠΈΠΉ ΠΏΠ»Π°Π½ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π²Β Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅Β Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΡΠ»Π΅Π΄ΡΡΡΠ΅ΠΌ:β’ ΠΏΡΠΎΠ²Π΅ΡΡΠΈ Π³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΡ ΠΏΡΠΈΠ±ΠΎΡΠΎΠ² ΠΏΠΎ ΡΠΏΠΎΡΠΎΠ±Ρ Π·Π°Π΄Π°Π½ΠΈΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΠΉ;β’ Π²ΡΡΡΠ½ΠΈΡΡ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΡΠΉ Π΄Π»Ρ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΠΎΡΡΠ°Π² ΠΈ Π²ΠΈΠ΄ Π²Ρ
ΠΎΠ΄Π½ΡΡ
Β Π΄Π°Π½Π½ΡΡ
;β’ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ Π½Π°Π±ΠΎΡ Π²Ρ
ΠΎΠ΄Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
Π΄Π»Ρ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡ ΡΡΠΌΠΌΠ°ΡΠ½ΠΎΠΉ ΠΏΠΎΡΡΠ΅Π±Π»ΡΠ΅ΠΌΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ;β’ ΡΠΊΠΎΠ½ΡΡΡΡΠΈΡΠΎΠ²Π°ΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΡ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ, ΡΠ°Π±ΠΎΡΠ°ΡΡΠΈΠ΅ ΡΒ ΡΠΊΠ°Π·Π°Π½Π½ΡΠΌΠΈ Π²ΡΡΠ΅ Π²Ρ
ΠΎΠ΄Π½ΡΠΌΠΈ Π΄Π°Π½Π½ΡΠΌΠΈ.ΠΠ°ΠΆΠ½Π΅ΠΉΡΠΈΠΌΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΡΡΡΡΒ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅:β’ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π³ΡΠ°ΡΠΈΠΊΠ° ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΡΠΌΠΌΠ°ΡΠ½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ.β’ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π΄Π»ΡΒ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΈΠ· Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΠΏΠΎΠ΄ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ² Π±ΡΡΠΎΠ²ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ².β’ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ, ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΎ ΠΈΡ
Β ΡΠ°Π±ΠΎΡΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ, ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΒ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΡ
ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π±Π΅Π· ΠΊΠ°ΠΊΠΎΠΉ- Π»ΠΈΠ±ΠΎΒ Π°Π΄Π°ΠΏΡΠ°ΡΠΈΠΈ.Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, Π² ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠ½Π΅ΡΠ³ΠΎΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ Π²Β ΠΆΠΈΠ»ΠΈΡΠ½ΠΎΠΌ ΡΠ΅ΠΊΡΠΎΡΠ΅, ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ΅Β Π½Π° ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅
The formation of human populations in South and Central Asia
By sequencing 523 ancient humans, we show that the primary source of ancestry in modern South Asians is a prehistoric genetic gradient between people related to early hunter-gatherers of Iran and Southeast Asia. After the Indus Valley Civilizationβs decline, its people mixed with individuals in the southeast to form one of the two main ancestral populations of South Asia, whose direct descendants live in southern India. Simultaneously, they mixed with descendants of Steppe pastoralists who, starting around 4000 years ago, spread via Central Asia to form the other main ancestral population. The Steppe ancestry in South Asia has the same profile as that in Bronze Age Eastern Europe, tracking a movement of people that affected both regions and that likely spread the distinctive features shared between Indo-Iranian and Balto-Slavic languages
EPR of Yb 3+ ions in a monoclinic KY(WO 4) 2 single crystal
The electron paramagnetic resonance (EPR) of Yb 3+ ions in a KY(WO 4) 2 single crystal was investigated at T=4.2Β K and fixed frequency of 9.38Β GHz. The resonance absorption observed on the lowest Kramers doublet represents the complex superposition of three spectra, corresponding to the ytterbium isotopes with different nuclear moments. The EPR spectrum is characterized by a strong anisotropy of the g-factors. The temperature dependence of the g-factors is shown to be caused by the strong spin-orbital and orbital-lattice coupling. The resonance lines broaden with increasing temperature due to the short spin-lattice relaxation times. Copyright EDP Sciences/SocietΓ Italiana di Fisica/Springer-Verlag 200775.30.Gw Magnetic anisotropy, 76.30.-v Electron paramagnetic resonance and relaxation,
Benefits of Printed Graphene with Variable Resistance for Flexible and Ecological 5G Band Antennas
The possibility of creating antennas of the 5G standard (5.2–5.9 GHz) with specified electrodynamic characteristics by printing layers of variable thickness using a graphene suspension has been substantiated experimentally and by computer simulation. A graphene suspension for screen printing on photographic paper and other flexible substrates was prepared by means of exfoliation from graphite. The relation between the graphene layer thickness and its sheet resistance was studied with the aim of determining the required thickness of the antenna conductive layer. To create a two-sided dipole, a technology has been developed for the double-sided deposition of graphene layers on photographic paper. The electrodynamic characteristics of graphene and copper antennas of identical design are compared. The antenna design corresponds to the operating frequency of 2.4 GHz. It was found that the use of graphene as a conductive layer made it possible to suppress the fundamental (first) harmonic (2.45 GHz) and to observe radiation at the second harmonic (5.75 GHz). This effect is assumed to observe in the case when the thickness of graphene is lower than that of the skin depth. The result indicates the possibility of changing the antenna electrodynamic characteristics by adjusting the graphene layer thickness