425 research outputs found
Π£ΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ²: Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΡΠ΅ΠΊ Π·ΡΠ΅Π½ΠΈΡ Π²Π»Π°ΡΡΠΈ, Π±ΠΈΠ·Π½Π΅ΡΠ° ΠΈ Π½Π°ΡΠΊΠΈ
Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π°Π½Π°Π»ΠΈΠ·Ρ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ, ΠΊΠ°ΡΠ°ΡΡΠΈΡ
ΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ². ΠΡΠ° ΡΠ΅ΠΌΠ° ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°Π΅Ρ ΠΏΡΠΈΡΡΠ°Π»ΡΠ½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π°. Π ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ Π½Π°ΡΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡ ΠΏΠΎ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ² ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΠΌ Π·Π°ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ Π»ΠΈΡΠ°ΠΌΠΈ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ° Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΏΠΎ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»Π°ΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½Π°Ρ Π±Π°Π·Π° e-Library. Π ΠΊΡΡΠ³ Π·Π°ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°Π½Π½ΡΡ
Π»ΠΈΡ, Π½Π°ΠΏΡΡΠΌΡΡ Π·Π°Π²ΠΈΡΡΡΠΈΡ
ΠΎΡ ΠΏΡΠ°Π²ΠΈΠ» Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, Π²Ρ
ΠΎΠ΄ΡΡ Π±ΠΈΠ·Π½Π΅ΡΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ ΠΈ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΡΠ³Π°Π½Ρ. ΠΠ»Ρ Π½ΠΈΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΠΎΠΉ ΡΠ΅ΠΌΠ΅ ΡΠ²Π»ΡΡΡΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½Π°Ρ Π±Π°Π·Π° ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠΠΎΠΌΠΌΠ΅ΡΡΠ°Π½ΡΡΒ» ΠΈ Β«Π ΠΎΡΡΠΈΠΉΡΠΊΠ°Ρ Π³Π°Π·Π΅ΡΠ°Β». ΠΠ»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΡΠΎΠ±ΡΠ°Π½Π° 301 ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡ Π·Π° 2013-2015 Π³Π³. ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΏΡΡΠ΅ΠΌ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π² ΡΠ°Π·ΡΠ΅Π·Π΅ Π²ΠΈΠ΄ΠΎΠ² ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. ΠΠ° ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ» Π²ΡΠΏΠΎΠ»Π½Π΅Π½ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΊΠΎΠ½ΡΠ΅Π½Ρ-Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²ΠΎΠΌ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΠΈΡ
ΡΠ΅ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΡ
Π² ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡΡ
. ΠΠ°ΡΠ΅ΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠ΅ΡΠ΅Π· ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΏΠΎ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΉ ΡΠ΅ΠΌΠ΅ ΠΈΠ· ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°. ΠΠ»Ρ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ ΠΊΠ°ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π Π°ΡΡΠ΅ΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»ΠΈΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΠ° QDA Miner v.5.0 ΠΌΠΎΠ΄ΡΠ»Ρ WordStat v.7.1.7. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»ΠΈ ΡΠ΄Π΅Π»Π°Π½Ρ Π²ΡΠ²ΠΎΠ΄Ρ, ΡΡΠΎ ΡΠ°ΠΌΡΠΌΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠ½ΡΠΌΠΈ ΡΠ΅ΠΌΠ°ΠΌΠΈ, ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ Π½Π΅ ΠΌΠ΅Π½ΡΠ΅ΡΡΡ, ΡΠ²Π»ΡΡΡΡΡ: ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΡΡΠ²Π°, Π·Π°ΠΊΠΎΠ½ΠΎΡΠ²ΠΎΡΡΠ΅ΡΡΠ²ΠΎ ΠΈ ΡΡΠΈΠ»Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΠ½ΡΠΆΠ΄Π΅Π½ΠΈΡ. Π’Π΅ΠΌΡ, ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ Π·Π° ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠ½ΠΈΠ·ΠΈΠ»ΡΡ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ, ΠΊΠ°ΡΠ°ΡΡΡΡ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΡ
Π°ΡΠΏΠ΅ΠΊΡΠΎΠ² Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, ΡΠ΅Π½Π΅Π²ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΉ. ΠΡΠΌΠ΅ΡΠ΅Π½ΠΎ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΠ΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ°Π½ΠΈΠ΅ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π° ΠΊ ΡΠΈΡΠΌΠ°ΠΌ-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠ°ΠΌ, ΡΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ ΡΡΡΠ°ΡΠΎΠ² ΠΈ ΠΏΠ΅Π½ΠΈ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ Π²ΡΡΠ²ΠΈΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ΅ Π½Π΅ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ ΡΠ΅ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΡ
Π±ΠΈΠ·Π½Π΅ΡΠΎΠΌ ΠΈ Π²Π»Π°ΡΡΡΡ, ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠ΅ΠΌΠ°ΠΌΠΈ Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. Π Π°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ Π² Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡΡ
ΡΠ΅ΠΌΡ (ΡΠ΅Π½Π΅Π²Π°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°, ΠΊΠΎΡΡΡΠΏΡΠΈΡ, ΡΠΈΡΠΌΡ-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠΈ, Π²Π·Π½ΠΎΡΡ Π½Π° ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ΅ ΡΡΡΠ°Ρ
ΠΎΠ²Π°Π½ΠΈΠ΅), Π³ΠΎΡΠ°Π·Π΄ΠΎ ΡΠ΅ΠΆΠ΅ Π²ΡΡΡΠ΅ΡΠ°ΡΡΡΡ Π½Π° ΡΠ΅ΡΡΡΡΠ°Ρ
ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠΠΎΠΌΠΌΠ΅ΡΡΠ°Π½ΡΡΒ» ΠΈ Π² Β«Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π³Π°Π·Π΅ΡΠ΅Β», ΡΠΎΡΡΠ΅Π΄ΠΎΡΠ°ΡΠΈΠ²Π°ΡΡΠΈΡ
ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π° Π²ΠΎΠΏΡΠΎΡΠ°Ρ
Π·Π°ΠΊΠΎΠ½ΠΎΡΠ²ΠΎΡΡΠ΅ΡΡΠ²Π° ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΡΡΠ²Π΅. ΠΠ½Π°Π»ΠΈΠ· Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Π΅ΠΉ Π² ΡΠ΅ΠΊΡΡΠ°Ρ
Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈ Π³ΠΎΠ΄ΠΎΠΌ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ ΡΠ΅ΠΌΡ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠ±Π»ΠΈΠΆΠ°ΡΡΡΡ Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ, ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΠΌΠΈ Π²Π»Π°ΡΡΡΡ, Π° Π±ΠΈΠ·Π½Π΅Ρ-ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ Π² Π±ΠΎΠ»ΡΡΠ΅ΠΉ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π²ΠΎΠ²Π»Π΅ΠΊΠ°Π΅ΡΡΡ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ, Ρ. Π΅. ΡΠΎΡΠΊΠ° Π·ΡΠ΅Π½ΠΈΡ Π²Π»Π°ΡΡΠΈ Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΌΡ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ Π½Π°Π»ΠΎΠ³ΠΎΠ² Π±ΠΈΠ·Π½Π΅Ρ-ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎΠΌ, ΠΈ Π½Π°ΡΡΠ½ΡΠΌΠΈ ΠΊΡΡΠ³Π°ΠΌΠΈ. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΅ΠΊΡΡΠΎΠ² ΠΌΠΎΠ³ΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, ΡΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΠ±Π·ΠΎΡΠΎΠ² Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΈ ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠΈΡΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ.This article analyzes the publications relating to the problem of tax evasion. This topic is attractive not only for the academic community, but also for public at whole. The article explores to what extent the scientific publications on tax evasion correspond to practical issues discussed among the stakeholders. We used the electronic database of e-Library as a source of scientific publications on the subject. The principal stakeholders directly dependent on the taxation are the taxpayers and public authorities. We used the electronic database of publications Β«KommersantΒ» publishing house and the Β«Rossiyskaya GazetaΒ» to reflect issues discussed among the stakeholders. We selected for analyze 301 publications for the period of 2013-2015. The study was conducted by comparing the publication activity by types and period of publications. In the first stage of the study we have done the qualitative content analysis by identification the common themes discussed in hole sample of publications. Then, a quantitative analysis was conducted by comparing the distribution of publications on a particular topic from each source. We used bibliometric analysis method for the quantitative and bibliographic mapping method to visualize the results of research. Calculations were performed using the software QDA Miner v.5.0 module WordStat v.7.1.7. As a result, studies have concluded that the most popular topics of interest for which no changes are: changes in legislation, legislation and increased enforcement. Using the results of the conducted study, we can identify the main similarities and differences between the monitored sources. We can see the special attention to the: Legislation changes, Law enforcement, Entrepreneurship. Marked reduction of interest can be noted regarding to the following topics: International aspects of taxation, Shadow economy, Ownership, property, investment. The growth of interest can be noted in relation to the following topics: Directorship, Article of the Tax Code, Short-lived companies, Arrears and fines. The study revealed a certain disparity between the topics discussed among academic community and stakeholders. The topics discussed in the majority of scientific texts (shadow economy, corruption, the firm one-day, social security contributions), a much rarer can be found in the publication of Β«KommersantΒ» and Β«Rossiyskaya GazetaΒ» which focuses mainly on matters of legislation. Analysis of the relationships in the texts according to the source and year of publication showed that research topics converge with issues considered by the public authorities. The business community more involved in discussion the legal issues, because the government notion works upon the impression about tax evasion of the business community and academia. Thus, bibliometric text analysis techniques can be used for research, preparation of literature reviews and thematic information retrieval
Antihyperlipidemic effects of Pleurotus ostreatus (oyster mushrooms) in HIV-infected individuals taking antiretroviral therapy
<p>Abstract</p> <p>Background</p> <p>Antiretroviral treatment (ART) regimens in HIV patients commonly cause significant lipid elevations, including increases in both triglycerides and cholesterol. Standard treatments for hypercholesterolemia include the HMG CoA reductase inhibitors, or "statins." Because many ART agents and statins share a common metabolic pathway that uses the cytochrome P450 enzyme system, coadministration of ART with statins could increase statin plasma levels significantly. The oyster mushroom, <it>Pleurotus ostreatus</it>, has been shown in animal models to decrease lipid levels - a finding that has been supported by preliminary data in a small human trial.</p> <p>Methods</p> <p>To assess the safety and efficacy of <it>P. ostreatus </it>in patients with HIV and ART-induced hyperlipidemia, a single-arm, open-label, proof-of-concept study of 8 weeks' duration with a target enrollment of 20 subjects was conducted. Study patients with ART-induced elevated non-HDL cholesterol levels (> 160 mg/dL) were enrolled. Participants received packets of freeze-dried <it>P. ostreatus </it>(15 gm/day) to be administered orally for the 8 week trial period. Lipid levels were drawn every two weeks to assess efficacy. Safety assessments included self-reported incidence of muscle aches and measurement of liver and muscle enzymes. Mean within-person change in lipid levels were estimated using generalized estimating equations to account for repeated observations on individuals. A 30 mg/dL decrease in non-HDL cholesterol was deemed clinically significant.</p> <p>Results</p> <p>126 patients were screened to enroll 25, of which 20 completed the 8-week study. The mean age was 46.4 years (36-60). Patients had a mean 13.7 yrs of HIV infection. Mean non-HDL cholesterol was 204.5 mg/dL at day 0 and 200.2 mg/dL at day 56 (mean within-person change = -1.70; 95% confidence interval (CI) = -17.4, 14.0). HDL cholesterol levels increased from 37.8 mg/dL at day 0 to 40.4 mg/dL on day 56 (mean within-person change = 2.6; 95% CI = -0.1, 5.2). Triglycerides dropped from 336.4 mg/dL on day 0 to 273.4 mg/dL on day 56 (mean within-person change = -63.0; 95% CI = -120.9, -5.1). Only 3 individuals achieved a sustained clinically significant (30 mg/dL) decline in non-HDL cholesterol after 8 weeks of therapy. There were no adverse experiences reported other than patients' distaste for the preparation. Liver function tests and muscle enzymes were not affected by the 8 weeks of treatment.</p> <p>Conclusions</p> <p><it>Pleurotus ostreatus </it>as administered in this experiment did not lower non-HDL cholesterol in HIV patients with ART-induced hypercholesterolemia. Small changes in HDL and triglycerides were not of a clinical magnitude to warrant further study.</p> <p>Trial Registration</p> <p>clinicaltrials.gov Identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00069524">NCT00069524</a></p
Uncertainty in context-aware systems: A case study for intelligent environments
Data used be context-aware systems is naturally incomplete and not always reflect real situations. The dynamic nature of intelligent environments leads to the need of analysing and handling uncertain information. Users can change their acting patterns within a short space of time. This paper presents a case study for a better understanding of concepts related to context awareness and the problem of dealing with inaccurate data. Through the analysis of identification of elements that results in the construction of unreliable contexts, it is aimed to identify patterns to minimize incompleteness. Thus, it will be possible to deal with flaws caused by undesired execution of applications.Programa Operacional TemΓ‘tico Factores de Competitividade (POCI-01-0145-
Knowledge inference through analysis of human activities
Monitoring human activities provides context data to be used by computational systems, aiming a better understanding of users and their surroundings. Uncertainty still is an obstacle to overcome when dealing with context-aware systems. The origin of it may be related to incomplete or outdated data. Attribute Grammars emerge as a consistent approach to deal with this problem due to their formal nature, allowing the definition of rules to validate context. In this paper, a model to validate human daily activities based on an Attribute Grammar is presented. Context data is analysed through the execution of rules that implement semantic statements. This processing, called semantic analysis, will highlight problems that can be raised up by uncertain situations. The main contribution of this paper is the proposal of a rigorous approach to deal with context-aware decisions (decisions that depend on the data collected from the sensors in the environment) in such a way that uncertainty can be detected and its harmful effects can be minimized.This work has been supported by national funds through FCT β Fundação para a CiΓͺncia e Tecnologia Λ
within the Project Scope: UID/CEC/00319/2019
Π£ΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ²: Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΡΠ΅ΠΊ Π·ΡΠ΅Π½ΠΈΡ Π²Π»Π°ΡΡΠΈ, Π±ΠΈΠ·Π½Π΅ΡΠ° ΠΈ Π½Π°ΡΠΊΠΈ
This article analyzes the publications relating to the problem of tax evasion. This topic is attractive not only for the academic community, but also for public at whole. The article explores to what extent the scientific publications on tax evasion correspond to practical issues discussed among the stakeholders. We used the electronic database of e-Library as a source of scientific publications on the subject. The principal stakeholders directly dependent on the taxation are the taxpayers and public authorities. We used the electronic database of publications Β«KommersantΒ» publishing house and the Β«Rossiyskaya GazetaΒ» to reflect issues discussed among the stakeholders. We selected for analyze 301 publications for the period of 2013-2015. The study was conducted by comparing the publication activity by types and period of publications. In the first stage of the study we have done the qualitative content analysis by identification the common themes discussed in hole sample of publications. Then, a quantitative analysis was conducted by comparing the distribution of publications on a particular topic from each source. We used bibliometric analysis method for the quantitative and bibliographic mapping method to visualize the results of research. Calculations were performed using the software QDA Miner v.5.0 module WordStat v.7.1.7. As a result, studies have concluded that the most popular topics of interest for which no changes are: changes in legislation, legislation and increased enforcement. Using the results of the conducted study, we can identify the main similarities and differences between the monitored sources. We can see the special attention to the: Legislation changes, Law enforcement, Entrepreneurship. Marked reduction of interest can be noted regarding to the following topics: International aspects of taxation, Shadow economy, Ownership, property, investment. The growth of interest can be noted in relation to the following topics: Directorship, Article of the Tax Code, Short-lived companies, Arrears and fines. The study revealed a certain disparity between the topics discussed among academic community and stakeholders. The topics discussed in the majority of scientific texts (shadow economy, corruption, the firm one-day, social security contributions), a much rarer can be found in the publication of Β«KommersantΒ» and Β«Rossiyskaya GazetaΒ» which focuses mainly on matters of legislation. Analysis of the relationships in the texts according to the source and year of publication showed that research topics converge with issues considered by the public authorities. The business community more involved in discussion the legal issues, because the government notion works upon the impression about tax evasion of the business community and academia. Thus, bibliometric text analysis techniques can be used for research, preparation of literature reviews and thematic information retrieval.Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π°Π½Π°Π»ΠΈΠ·Ρ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ, ΠΊΠ°ΡΠ°ΡΡΠΈΡ
ΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ². ΠΡΠ° ΡΠ΅ΠΌΠ° ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°Π΅Ρ ΠΏΡΠΈΡΡΠ°Π»ΡΠ½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π°. Π ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ Π½Π°ΡΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡ ΠΏΠΎ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ² ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΠΌ Π·Π°ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ Π»ΠΈΡΠ°ΠΌΠΈ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ° Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΏΠΎ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»Π°ΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½Π°Ρ Π±Π°Π·Π° e-Library. Π ΠΊΡΡΠ³ Π·Π°ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°Π½Π½ΡΡ
Π»ΠΈΡ, Π½Π°ΠΏΡΡΠΌΡΡ Π·Π°Π²ΠΈΡΡΡΠΈΡ
ΠΎΡ ΠΏΡΠ°Π²ΠΈΠ» Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, Π²Ρ
ΠΎΠ΄ΡΡ Π±ΠΈΠ·Π½Π΅ΡΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ ΠΈ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΡΠ³Π°Π½Ρ. ΠΠ»Ρ Π½ΠΈΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΠΎΠΉ ΡΠ΅ΠΌΠ΅ ΡΠ²Π»ΡΡΡΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½Π°Ρ Π±Π°Π·Π° ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠΠΎΠΌΠΌΠ΅ΡΡΠ°Π½ΡΡΒ» ΠΈ Β«Π ΠΎΡΡΠΈΠΉΡΠΊΠ°Ρ Π³Π°Π·Π΅ΡΠ°Β». ΠΠ»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΡΠΎΠ±ΡΠ°Π½Π° 301 ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡ Π·Π° 2013-2015 Π³Π³. ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΏΡΡΠ΅ΠΌ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π² ΡΠ°Π·ΡΠ΅Π·Π΅ Π²ΠΈΠ΄ΠΎΠ² ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. ΠΠ° ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ» Π²ΡΠΏΠΎΠ»Π½Π΅Π½ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΊΠΎΠ½ΡΠ΅Π½Ρ-Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²ΠΎΠΌ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΠΈΡ
ΡΠ΅ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΡ
Π² ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡΡ
. ΠΠ°ΡΠ΅ΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠ΅ΡΠ΅Π· ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΏΠΎ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΉ ΡΠ΅ΠΌΠ΅ ΠΈΠ· ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°. ΠΠ»Ρ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ ΠΊΠ°ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π Π°ΡΡΠ΅ΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»ΠΈΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΠ° QDA Miner v.5.0 ΠΌΠΎΠ΄ΡΠ»Ρ WordStat v.7.1.7. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»ΠΈ ΡΠ΄Π΅Π»Π°Π½Ρ Π²ΡΠ²ΠΎΠ΄Ρ, ΡΡΠΎ ΡΠ°ΠΌΡΠΌΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠ½ΡΠΌΠΈ ΡΠ΅ΠΌΠ°ΠΌΠΈ, ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ Π½Π΅ ΠΌΠ΅Π½ΡΠ΅ΡΡΡ, ΡΠ²Π»ΡΡΡΡΡ: ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΡΡΠ²Π°, Π·Π°ΠΊΠΎΠ½ΠΎΡΠ²ΠΎΡΡΠ΅ΡΡΠ²ΠΎ ΠΈ ΡΡΠΈΠ»Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΠ½ΡΠΆΠ΄Π΅Π½ΠΈΡ. Π’Π΅ΠΌΡ, ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ Π·Π° ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠ½ΠΈΠ·ΠΈΠ»ΡΡ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ, ΠΊΠ°ΡΠ°ΡΡΡΡ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΡ
Π°ΡΠΏΠ΅ΠΊΡΠΎΠ² Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, ΡΠ΅Π½Π΅Π²ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΉ. ΠΡΠΌΠ΅ΡΠ΅Π½ΠΎ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΠ΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ°Π½ΠΈΠ΅ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π° ΠΊ ΡΠΈΡΠΌΠ°ΠΌ-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠ°ΠΌ, ΡΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ ΡΡΡΠ°ΡΠΎΠ² ΠΈ ΠΏΠ΅Π½ΠΈ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ Π²ΡΡΠ²ΠΈΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ΅ Π½Π΅ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ ΡΠ΅ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΡ
Π±ΠΈΠ·Π½Π΅ΡΠΎΠΌ ΠΈ Π²Π»Π°ΡΡΡΡ, ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠ΅ΠΌΠ°ΠΌΠΈ Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. Π Π°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ Π² Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡΡ
ΡΠ΅ΠΌΡ (ΡΠ΅Π½Π΅Π²Π°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°, ΠΊΠΎΡΡΡΠΏΡΠΈΡ, ΡΠΈΡΠΌΡ-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠΈ, Π²Π·Π½ΠΎΡΡ Π½Π° ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ΅ ΡΡΡΠ°Ρ
ΠΎΠ²Π°Π½ΠΈΠ΅), Π³ΠΎΡΠ°Π·Π΄ΠΎ ΡΠ΅ΠΆΠ΅ Π²ΡΡΡΠ΅ΡΠ°ΡΡΡΡ Π½Π° ΡΠ΅ΡΡΡΡΠ°Ρ
ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠΠΎΠΌΠΌΠ΅ΡΡΠ°Π½ΡΡΒ» ΠΈ Π² Β«Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π³Π°Π·Π΅ΡΠ΅Β», ΡΠΎΡΡΠ΅Π΄ΠΎΡΠ°ΡΠΈΠ²Π°ΡΡΠΈΡ
ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π° Π²ΠΎΠΏΡΠΎΡΠ°Ρ
Π·Π°ΠΊΠΎΠ½ΠΎΡΠ²ΠΎΡΡΠ΅ΡΡΠ²Π° ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΡΡΠ²Π΅. ΠΠ½Π°Π»ΠΈΠ· Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Π΅ΠΉ Π² ΡΠ΅ΠΊΡΡΠ°Ρ
Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈ Π³ΠΎΠ΄ΠΎΠΌ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ ΡΠ΅ΠΌΡ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠ±Π»ΠΈΠΆΠ°ΡΡΡΡ Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ, ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΠΌΠΈ Π²Π»Π°ΡΡΡΡ, Π° Π±ΠΈΠ·Π½Π΅Ρ-ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ Π² Π±ΠΎΠ»ΡΡΠ΅ΠΉ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π²ΠΎΠ²Π»Π΅ΠΊΠ°Π΅ΡΡΡ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ, Ρ. Π΅. ΡΠΎΡΠΊΠ° Π·ΡΠ΅Π½ΠΈΡ Π²Π»Π°ΡΡΠΈ Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΌΡ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ Π½Π°Π»ΠΎΠ³ΠΎΠ² Π±ΠΈΠ·Π½Π΅Ρ-ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎΠΌ, ΠΈ Π½Π°ΡΡΠ½ΡΠΌΠΈ ΠΊΡΡΠ³Π°ΠΌΠΈ. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΅ΠΊΡΡΠΎΠ² ΠΌΠΎΠ³ΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, ΡΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΠ±Π·ΠΎΡΠΎΠ² Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΈ ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠΈΡΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ
INSULIN AND ANALOGS: NARRATIVE LITERATURE REVIEW
As insulinas e seus anΓ‘logos sΓ£o divididos em trΓͺs tipos principais: os de ação curta, distribuΓdas nas categorias de ação curta e ultracurta ou rΓ‘pida; ação intermediΓ‘ria e ação longa. A insulina aspart e lispro possuem ação ultracurta e rΓ‘pida, a insulina glulisina sofre absorção duas vezes mais rΓ‘pida que a insulina regular e atinge um pico plasmΓ‘tico duas vezes maior. A insulina regular Γ© uma insulina zinco cristalina, de ação curta, empregada em casos emergenciais hiperglicΓͺmicos. As de ação intermediΓ‘ria sΓ£o: protamina neutra de Hagedorn (NPH) ou isΓ³fana e a lente. A insulina glargina Γ© um anΓ‘logo de insulina modificada, a qual foi desenvolvida para proporcionar uma concentração constante de insulina. A insulina detemir Γ© um anΓ‘logo solΓΊvel, com ação prolongada, caracteriza por nΓ£o possuir pico de ação. Este trabalho Γ© uma revisΓ£o narrativa, descritiva e exploratΓ³ria sobre a insulina e seus anΓ‘logos, voltados para o estudo da insulina no Diabetes mellitus (DM), pois, Γ© a melhor escolha para o tratamento do DM tipo 1.
Custom order entry for Parkinsonβs medications in the hospital improves timely administration: an analysis of over 31,000 medication doses
Background: Patients with Parkinson's disease (PD) are at increased risk for hospital acquired complications. Deviations from home medication schedules and delays in administration are major contributing factors. We had previously developed a protocol to ensure adherence to home medication schedules using "custom" ordering. In this study we are assessing the impact this order type may have on reducing delays in PD medication administration in the hospital.
Material and methods: We reviewed 31,404 orders placed for PD medications from January 2, 2016 to April 30 2021. We evaluated the orders to determine if they were placed in a Custom format or using a default non-custom order entry. We further evaluated all orders to determine if there was a relationship with the order type and timely administration of medications. We compared medications that were administered within 1 min, 15 min, 30 min and 60 min of due times across custom orders vs. non-custom default orders. We also evaluated the relationship between ordering providers and type of orders placed as well as hospital unit and type of orders placed.
Results: 14,204 (45.23%) orders were placed using a custom schedule and 17,200 (54.77%) orders were placed using non-custom defaults. The custom group showed a significantly lower median delay of 3.06 minutes compared to the non-custom group (p<.001). Custom orders had a significantly more recent median date than non-custom default orders (2019-10-07 vs. 2018-01-06, p<0.001). In additional analyses, medication administration delays were significantly improved for custom orders compared to non-custom orders, with likelihoods 1.64 times higher within 1 minute, 1.40 times higher within 15 minutes, and 1.33 times higher within 30 minutes of the due time (p<0.001 for all comparisons).
Conclusion: This is the largest study to date examining the effects of order entry type on timely administration of PD medications in the hospital. Orders placed using a custom schedule may help reduce delays in administration of PD medications
EVALUATION OF THE USE OF CEPHALOSPORINS IN A HOSPITAL OF THE CITY OF PONTA GROSSA, PARANΓ
As cefalosporinas sΓ£o uma classe de antimicrobianos com amplo espectro de ação, pertencente ao grupo dos beta-lactΓ’micos. O presente trabalho teve como objetivo avaliar a utilização de cefalosporinas em um hospital da cidade de Ponta Grossa, ParanΓ‘. Foi realizada a coleta dos dados a partir de prescriçáes mΓ©dicas e relatΓ³rios do hospital. O estudo apresentou um total de 882 utilizaçáes de cefalosporinas nos meses analisados, obtendo como o antimicrobiano mais utilizado a cefazolina, com cerca de 80,27%. A finalidade de uso das cefalosporinas em nΓΊmero de utilizaçáes foi maior na profilaxia cirΓΊrgica (707 ou 80,15%) do que na terapia curativa (158 ou 17,9%). No emprego terapΓͺutico curativo a ceftriaxona prevaleceu (70,25%). Das utilizaçáes terapΓͺuticas, 45% foram totalmente empΓricas; 30% tiveram auxΓlio de cultura e 25% foram prescritas mediante cultura e antibiograma, porΓ©m, apenas 16 ou 10% do total de tratamentos com cefalosporinas foram comprovados como uma terapia especΓfica.
Virtual reality-based parallel coordinates plots enhanced with explainable ai and data-science analytics for decision-making processes
We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others.</jats:p
Explainable Predictive Maintenance
Explainable Artificial Intelligence (XAI) fills the role of a critical
interface fostering interactions between sophisticated intelligent systems and
diverse individuals, including data scientists, domain experts, end-users, and
more. It aids in deciphering the intricate internal mechanisms of ``black box''
Machine Learning (ML), rendering the reasons behind their decisions more
understandable. However, current research in XAI primarily focuses on two
aspects; ways to facilitate user trust, or to debug and refine the ML model.
The majority of it falls short of recognising the diverse types of explanations
needed in broader contexts, as different users and varied application areas
necessitate solutions tailored to their specific needs.
One such domain is Predictive Maintenance (PdM), an exploding area of
research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights
the gap between existing XAI methodologies and the specific requirements for
explanations within industrial applications, particularly the Predictive
Maintenance field. Despite explainability's crucial role, this subject remains
a relatively under-explored area, making this paper a pioneering attempt to
bring relevant challenges to the research community's attention. We provide an
overview of predictive maintenance tasks and accentuate the need and varying
purposes for corresponding explanations. We then list and describe XAI
techniques commonly employed in the literature, discussing their suitability
for PdM tasks. Finally, to make the ideas and claims more concrete, we
demonstrate XAI applied in four specific industrial use cases: commercial
vehicles, metro trains, steel plants, and wind farms, spotlighting areas
requiring further research.Comment: 51 pages, 9 figure
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