9 research outputs found
Π€ΠΈΠ½Π°Π½ΡΠΎΠ²Π°Ρ Π³ΡΠ°ΠΌΠΎΡΠ½ΠΎΡΡΡ Π»ΡΠ΄Π΅ΠΉ ΠΏΡΠ΅Π΄ΠΏΠ΅Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΠΊΠ°ΠΊ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΈΡ Π±Π»Π°Π³ΠΎΡΠΎΡΡΠΎΡΠ½ΠΈΡ
ΠΠ±ΡΠ΅ΠΊΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΡ, ΠΈΠΌΠ΅ΡΡΠΈΠ΅ ΠΏΡΠΈΡΠΈΠ½Π½ΠΎ-ΡΠ»Π΅Π΄ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΡΠ²ΡΠ·ΠΈ Ρ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ Π³ΡΠ°ΠΌΠΎΡΠ½ΠΎΡΡΡΡ Π³ΡΠ°ΠΆΠ΄Π°Π½ ΠΏΡΠ΅Π΄ΠΏΠ΅Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ°.
ΠΡΠ΅Π΄ΠΌΠ΅Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΠΈΡΡΠ΅ΠΌΠ° ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΡ
Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ Π³ΡΠ°ΠΌΠΎΡΠ½ΠΎΡΡΠΈ Π³ΡΠ°ΠΆΠ΄Π°Π½ ΠΏΡΠ΅Π΄ΠΏΠ΅Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ°.
Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ: ΠΊΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΎΠΏΡΡΠ° ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ Π³ΡΠ°ΠΌΠΎΡΠ½ΠΎΡΡΠΈ Π»ΡΠ΄Π΅ΠΉ ΠΏΡΠ΅Π΄ΠΏΠ΅Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ ΠΏΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ Π΅Π΅ ΡΡΠΎΠ²Π½Ρ.
ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ ΡΠ°Π±ΠΎΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π°Ρ
ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΡ
ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΉ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ Π³ΡΠ°ΠΌΠΎΡΠ½ΠΎΡΡΠΈ Π³ΡΠ°ΠΆΠ΄Π°Π½ ΠΏΡΠ΅Π΄ΠΏΠ΅Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ°.Object of research: are socio-economic processes that have causal relationships with the financial literacy of citizens of pre-retirement age.
The subject of the research is: the system of relations arising in the process of forming the financial literacy of citizens of pre-retirement age.
The purpose of the final qualification work: a critical analysis of the modern experience in the formation of financial literacy of people of pre-retirement age and the development of recommendations for increasing its level.
The practical significance of the work lies in the prospects for the practical application of the proposed recommendations aimed at increasing the financial literacy of citizens
Element specific X-ray fluorescene microtomography
X-ray fluorescence is widely known as an element-specific scanning analytic tool. It is used in many fields of science and technology and has given major new insights into different problems. The relatively large penetration depth of x rays into matter makes them ideally suited for tomography. The combination of x-ray fluorescence analysis and scanning microtomography, hereafter called x-ray fluorescence microtomography, has been further developed and improved in this work. Employing the newly developed refractive x-ray lenses at a third generation synchrotron source the spatial resolution of this technique could be reduce to a micrometer. The fluorescence radiation that leaves the sample and can be detected from outside suffers absorption on its way to the detector. This problem of absorption is treated in this work, too and technical improvements of the setup are outlined. These improvements reduce the absorption of the fluorescence radiation. This in turn improves the signal to noise ratio of the fluorescence radiation and lowers the detection limit. Moreover, a tomographic reconstruction algorithm is described which is especially designed for x-ray fluorescence tomography. This algorithm takes into account the absorption of the fluorescence radiation within the sample. Some example applications are described to illustrate the success and power of this method as well as its advantages over other microanalytic methods. Also, the possible use of x-ray fluorescence microtomography in a laboratory is assessed. Besides this, the work briefly introduces into interactions of x rays with matter, into the concepts of the detectors that were used, and into the working principle of the refractive lenses
Discovering frequent patterns to bootstrap trust
Due to copyright restrictions, the access to the full text of this article is only available via subscription.When a new agent enters to an open multiagent system, bootstrapping its trust becomes a challenge because of the lack of any direct or reputational evidence. To get around this problem, existing approaches assume the same a priori trust for all newcomers. However, assuming the same a priori trust for all agents may lead to other problems like whitewashing. In this paper, we leverage graph mining and knowledge representation to estimate a priori trust for agents. For this purpose, our approach first discovers significant patterns that may be used to characterise trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate trustworthiness. Lastly, a priori trust for newcomers are estimated using the discovered features based on the trained model. Through extensive simulations, we have showed that the proposed approach significantly outperforms existing approaches