108 research outputs found
ENHANCING E-COMMERCE CUSTOM REGULATION FOR SEAMLESS TRADE IN EAEU: A POLICY PAPER
This policy paper provides a comprehensive analysis of the e-commerce sector within the Eurasian Economic Union (EAEU), highlighting its challenges, opportunities, and potential for driving economic growth. It sets forth a series of well-crafted policy recommendations aimed at establishing a conducive environment for e-commerce development. By implementing these measures, policymakers can fully unleash the potential of e-commerce, fostering economic growth, and advancing digital inclusion. As the e-commerce sector flourishes, it necessitates the establishment of a fair and efficient regulatory framework to facilitate smooth cross-border transactions. Addressing the custom regulation and taxation of e-commerce transactions requires collaborative efforts. Policymakers must strive to develop transparent and equitable customs and taxation systems that prevent tax evasion while easing the burden on small and medium-sized enterprises. One essential consideration is the implementation of destination-based taxation, ensuring that taxes are paid in the countries where products and services are consumed. This approach fosters fairness and discourages tax avoidance strategies. The policy paper aims to propose specific measures to enhance e-commerce custom regulation, effectively tackling the unique challenges presented by online trade. Leveraging technology and fostering international cooperation are pivotal in designing streamlined customs processes, creating an enabling environment for e-commerce growth. Such endeavors will ensure compliance, minimize illicit activities, and foster a flourishing e-commerce landscape within the EAEU
THE NECESSITY OF INTRODUCTION THE DRUG INSURANCE SYSTEM IN ARMENIA
The increase in the cost of the medicinal component of the treatment, the spread of chronic diseases, and the maintenance of socio-economic inequality in access to health services require the provision of adequate access to medicines. These issues create prerequisites for the improvement of the state health policy and, first, the drug supply system, which is an integral part of the treatment process. The financing of healthcare in Armenia is mainly formed from budget allocations and out of pocket expenditures of the population. Reducing the financial burden on the state and ensuring the rational use of drugs contributes to improving the health of the population. The implementation of a drug insurance scheme, which partially or fully cover the cost of drugs in RA, is one of the solutions for resolving the issue of access to medicines. This article studies the problems of financing healthcare system in Armenia and highlights the need of introduction a drug insurance system in Armeni
Challenges and Experiences in Designing Interpretable KPI-diagnostics for Cloud Applications
Automated root cause analysis of performance problems in modern cloud computing infrastructures is of a high technology value in the self-driving context. Those systems are evolved into large scale and complex solutions which are core for running most of today’s business applications. Hence, cloud management providers realize their mission through a “total” monitoring of data center flows thus enabling a full visibility into the cloud. Appropriate machine learning methods and software products rely on such observation data for real-time identification and remediation of potential sources of performance degradations in cloud operations to minimize their impacts. We describe the existing technology challenges and our experiences while working on designing problem root cause analysis mechanisms which are automatic, application agnostic, and, at the same time, interpretable for human operators to gain their trust. The paper focuses on diagnosis of cloud ecosystems through their Key Performance Indicators (KPI). Those indicators are utilized to build automatically labeled data sets and train explainable AI models for identifying conditions and processes “responsible” for misbehaviors. Our experiments on a large time series data set from a cloud application demonstrate that those approaches are effective in obtaining models that explain unacceptable KPI behaviors and localize sources of issues
ΠΡΠ±ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π²Π½ΡΡΡΠΈΠ³Π»Π°Π·Π½ΠΎΠ³ΠΎ Π΄Π°Π²Π»Π΅Π½ΠΈΡ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ Π³ΠΈΠΏΠΎΡΠ΅Π½Π·ΠΈΠ²Π½ΠΎΠΉ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π°Π½ΡΠΈΠ³Π»Π°ΡΠΊΠΎΠΌΠ½ΡΡ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ
Purpose of this study β to compare the results of different tonometry methods before surgical treatment of glaucoma and in the early postoperative period.The study was conducted on a group of 50 patients (50 eyes) aged 55 to 80 years with uncompensated primary open-angle glaucoma, who were admitted to in-patient department for glaucoma surgery. Patients were examined using bidirectional applanation tonometry of the cornea performed on Ocular Response Analyzer, pneumotonometry on Canon TX-20P device, and with Icare tonometer. These studies were carried out on the day before the surgery, the next day, and 2 weeks after the operation.Significant differences in tonometry readings were revealed between all tested devices at high intraocular pressure (IOP) levels (before glaucoma surgery). Significant differences were also found in IOP values obtained with Icare tonometer in the central zone of the cornea and in the middle periphery in the nasal and temporal sectors. A significant difference between the indicators remained on the next day after surgery, except for the Icare readings. After two weeks, the tonometric parameters did not differ significantly from each other.Corneal compensated IOP (IOPcc) is the most important tonometric indicator in clinical practice because it takes into account the individual biomechanical characteristics of the patientβs cornea. When examining patients with glaucoma, the IOPcc indicator significantly differed in uncompensated IOP, which is important for determining the correct treatment tactics. When assessing the level of IOP after surgery this trend persisted, indicating a systematic underestimation of IOP level (overestimation of the effect of glaucoma surgery). The reliability of the study is confirmed by the results of measurements on unoperated fellow eyes (control).Π¦Π΅Π»Ρ β ΡΡΠ°Π²Π½ΠΈΡΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΠ°Π·Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΠΈ Π΄ΠΎ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»Π΅ΡΠ΅Π½ΠΈΡ Π³Π»Π°ΡΠΊΠΎΠΌΡ ΠΈ Π² ΡΠ°Π½Π½Π΅ΠΌ ΠΏΠΎΡΠ»Π΅ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π² Π³ΡΡΠΏΠΏΠ΅ ΠΈΠ· 50 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² (50 Π³Π»Π°Π·) Π² Π²ΠΎΠ·ΡΠ°ΡΡΠ΅ ΠΎΡ 55 Π΄ΠΎ 80 Π»Π΅Ρ Ρ Π½Π΅ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ ΠΎΡΠΊΡΡΡΠΎΡΠ³ΠΎΠ»ΡΠ½ΠΎΠΉ Π³Π»Π°ΡΠΊΠΎΠΌΠΎΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΠΎΡΡΡΠΏΠ°Π»ΠΈ Π² ΡΡΠ°ΡΠΈΠΎΠ½Π°Ρ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π°Π½ΡΠΈΠ³Π»Π°ΡΠΊΠΎΠΌΠ½ΠΎΠΉ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. ΠΡΠΏΠΎΠ»Π½ΡΠ»ΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π΄Π²ΡΠ½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ ΠΏΠ½Π΅Π²ΠΌΠΎΠ°ΠΏΠ»Π°Π½Π°ΡΠΈΠΈ ΡΠΎΠ³ΠΎΠ²ΠΈΡΡ Π½Π° Π±ΠΈΠΎΠΌΠ΅Ρ
Π°Π½ΠΈΡΠ΅ΡΠΊΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π°ΡΠΎΡΠ΅ Ocular Response Analyzer, Π±Π΅ΡΠΊΠΎΠ½ΡΠ°ΠΊΡΠ½ΡΡ ΡΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡ ΠΏΡΠΈΠ±ΠΎΡΠΎΠΌ Canon TX-20P ΠΈ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠ½ΠΎ- ΠΌΠ΅ΡΡΠΎΠΌ Icare. ΠΡΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π·Π° Π΄Π΅Π½Ρ Π΄ΠΎ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π°Π½ΡΠΈΠ³Π»Π°ΡΠΊΠΎΠΌΠ½ΠΎΠ³ΠΎ Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ²Π°, Π½Π° ΡΠ»Π΅Π΄ΡΡΡΠΈΠΉ Π΄Π΅Π½Ρ ΠΈ ΡΠ°ΠΊΠΆΠ΅ ΡΠ΅ΡΠ΅Π· 2 Π½Π΅Π΄Π΅Π»ΠΈ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. ΠΡΠΈ Π²ΡΡΠΎΠΊΠΈΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΡΡ
Π²Π½ΡΡΡΠΈΠ³Π»Π°Π·Π½ΠΎΠ³ΠΎ Π΄Π°Π²Π»Π΅Π½ΠΈΡ (ΠΠΠ) (Π΄ΠΎ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π°Π½ΡΠΈΠ³Π»Π°ΡΠΊΠΎΠΌΠ½ΠΎΠΉ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ) Π±ΡΠ»ΠΈ Π²ΡΡΠ²Π»Π΅Π½Ρ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡ ΡΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΌΠ΅ΠΆΠ΄Ρ Π²ΡΠ΅ΠΌΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠΌΠΈ ΠΏΡΠΈΠ±ΠΎΡΠ°ΠΌΠΈ. Π’Π°ΠΊΠΆΠ΅ Π±ΡΠ»ΠΈ Π²ΡΡΠ²Π»Π΅Π½Ρ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡ Π² ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΡ
ΠΠΠ ΠΏΡΠΈ ΡΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΠΈ Icare Π² ΡΠ΅Π½ΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π·ΠΎΠ½Π΅ ΡΠΎΠ³ΠΎΠ²ΠΈΡΡ ΠΈ Π½Π° ΡΡΠ΅Π΄Π½Π΅ΠΉ ΠΏΠ΅ΡΠΈΡΠ΅ΡΠΈΠΈ Π² Π½ΠΎΡΠΎΠ²ΠΎΠΌ ΠΈ Π²ΠΈΡΠΎΡΠ½ΠΎΠΌ ΡΠ΅ΠΊΡΠΎΡΠ°Ρ
. ΠΠ° ΡΠ»Π΅Π΄ΡΡΡΠΈΠΉ Π΄Π΅Π½Ρ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½Π°Ρ ΡΠ°Π·Π½ΠΈΡΠ° ΠΌΠ΅ΠΆΠ΄Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ ΡΠΎΡ
ΡΠ°Π½ΡΠ»Π°ΡΡ, Π·Π° ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Icare. Π§Π΅ΡΠ΅Π· 2 Π½Π΅Π΄Π΅Π»ΠΈ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΎΠ±ΠΎΠΉ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ Π½Π΅ ΡΠ°Π·Π»ΠΈΡΠ°Π»ΠΈΡΡ. Π ΠΎΠ³ΠΎΠ²ΠΈΡΠ½ΠΎ-ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ΅ ΠΠΠ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½ΡΠΌ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΏΠ»Π°Π½Π΅ ΡΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΌ, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΡΡΠΈΡΡΠ²Π°Π΅Ρ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΈΠ±ΡΠΎΠ·Π½ΠΎΠΉ ΠΎΠ±ΠΎΠ»ΠΎΡΠΊΠΈ Π³Π»Π°Π·Π° ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². ΠΡΠΈ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π³Π»Π°ΡΠΊΠΎΠΌΠΎΠΉ ΡΡΠΎΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ ΠΎΡΠ»ΠΈΡΠ°Π΅ΡΡΡ ΠΏΡΠΈ Π½Π΅ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΌ ΠΠΠ, ΡΡΠΎ Π²Π°ΠΆΠ½ΠΎ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½ΠΎΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΈ Π»Π΅ΡΠ΅Π½ΠΈΡ. ΠΡΠΈ ΠΎΡΠ΅Π½ΠΊΠ΅ ΠΠΠ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ Π΄Π°Π½Π½Π°Ρ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡ ΡΠΎΡ
ΡΠ°Π½ΡΠ»Π°ΡΡ, ΡΡΠΎ ΡΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΡΡ Π½Π΅Π΄ΠΎΠΎΡΠ΅Π½ΠΊΡ ΠΎΡΡΠ°Π»ΡΠΌΠΎΡΠΎΠ½ΡΡΠ° (ΠΏΠ΅ΡΠ΅ΠΎΡΠ΅Π½ΠΊΡ ΡΡΡΠ΅ΠΊΡΠ° ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ). ΠΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΡΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π΅ΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°- Π½ΠΈΡ Π½Π° ΠΏΠ°ΡΠ½ΡΡ
Π½Π΅ΠΎΠΏΠ΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π³Π»Π°Π·Π°Ρ
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