108 research outputs found

    ENHANCING E-COMMERCE CUSTOM REGULATION FOR SEAMLESS TRADE IN EAEU: A POLICY PAPER

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    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

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    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

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    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

    Π’Ρ‹Π±ΠΎΡ€ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° измСрСния Π²Π½ΡƒΡ‚Ρ€ΠΈΠ³Π»Π°Π·Π½ΠΎΠ³ΠΎ давлСния для ΠΎΡ†Π΅Π½ΠΊΠΈ Π³ΠΈΠΏΠΎΡ‚Π΅Π½Π·ΠΈΠ²Π½ΠΎΠΉ эффСктивности Π°Π½Ρ‚ΠΈΠ³Π»Π°ΡƒΠΊΠΎΠΌΠ½Ρ‹Ρ… ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΉ

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    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 patient’s 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).ЦСль β€” ΡΡ€Π°Π²Π½ΠΈΡ‚ΡŒ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ Ρ€Π°Π·Π½Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Ρ‚ΠΎΠ½ΠΎΠΌΠ΅Ρ‚Ρ€ΠΈΠΈ Π΄ΠΎ хирургичСского лСчСния Π³Π»Π°ΡƒΠΊΠΎΠΌΡ‹ ΠΈ Π² Ρ€Π°Π½Π½Π΅ΠΌ послСопСрационном ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π΅. ИсслСдованиС ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π² Π³Ρ€ΡƒΠΏΠΏΠ΅ ΠΈΠ· 50 ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² (50 Π³Π»Π°Π·) Π² возрастС ΠΎΡ‚ 55 Π΄ΠΎ 80 Π»Π΅Ρ‚ с нСкомпСнсированной ΠΏΠ΅Ρ€Π²ΠΈΡ‡Π½ΠΎΠΉ ΠΎΡ‚ΠΊΡ€Ρ‹Ρ‚ΠΎΡƒΠ³ΠΎΠ»ΡŒΠ½ΠΎΠΉ Π³Π»Π°ΡƒΠΊΠΎΠΌΠΎΠΉ, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ поступали Π² стационар для провСдСния Π°Π½Ρ‚ΠΈΠ³Π»Π°ΡƒΠΊΠΎΠΌΠ½ΠΎΠΉ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. Выполняли исслСдованиС с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ Π΄Π²ΡƒΠ½Π°ΠΏΡ€Π°Π²Π»Π΅Π½Π½ΠΎΠΉ ΠΏΠ½Π΅Π²ΠΌΠΎΠ°ΠΏΠ»Π°Π½Π°Ρ†ΠΈΠΈ Ρ€ΠΎΠ³ΠΎΠ²ΠΈΡ†Ρ‹ Π½Π° биомСханичСском Π°Π½Π°Π»ΠΈΠ·Π°Ρ‚ΠΎΡ€Π΅ Ocular Response Analyzer, Π±Π΅ΡΠΊΠΎΠ½Ρ‚Π°ΠΊΡ‚Π½ΡƒΡŽ Ρ‚ΠΎΠ½ΠΎΠΌΠ΅Ρ‚Ρ€ΠΈΡŽ ΠΏΡ€ΠΈΠ±ΠΎΡ€ΠΎΠΌ Canon TX-20P ΠΈ ΠΈΠ·ΠΌΠ΅Ρ€Π΅Π½ΠΈΠ΅ Ρ‚ΠΎΠ½ΠΎ- ΠΌΠ΅Ρ‚Ρ€ΠΎΠΌ Icare. Π­Ρ‚ΠΈ исслСдования ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π·Π° дСнь Π΄ΠΎ провСдСния Π°Π½Ρ‚ΠΈΠ³Π»Π°ΡƒΠΊΠΎΠΌΠ½ΠΎΠ³ΠΎ Π²ΠΌΠ΅ΡˆΠ°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π°, Π½Π° ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΉ дСнь ΠΈ Ρ‚Π°ΠΊΠΆΠ΅ Ρ‡Π΅Ρ€Π΅Π· 2 Π½Π΅Π΄Π΅Π»ΠΈ послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. ΠŸΡ€ΠΈ высоких значСниях Π²Π½ΡƒΡ‚Ρ€ΠΈΠ³Π»Π°Π·Π½ΠΎΠ³ΠΎ давлСния (Π’Π“Π”) (Π΄ΠΎ провСдСния Π°Π½Ρ‚ΠΈΠ³Π»Π°ΡƒΠΊΠΎΠΌΠ½ΠΎΠΉ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ) Π±Ρ‹Π»ΠΈ выявлСны достовСрныС различия тономСтричСских ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ ΠΌΠ΅ΠΆΠ΄Ρƒ всСми ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅ΠΌΡ‹ΠΌΠΈ ΠΏΡ€ΠΈΠ±ΠΎΡ€Π°ΠΌΠΈ. Π’Π°ΠΊΠΆΠ΅ Π±Ρ‹Π»ΠΈ выявлСны достовСрныС различия Π² показатСлях Π’Π“Π” ΠΏΡ€ΠΈ Ρ‚ΠΎΠ½ΠΎΠΌΠ΅Ρ‚Ρ€ΠΈΠΈ Icare Π² Ρ†Π΅Π½Ρ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ Π·ΠΎΠ½Π΅ Ρ€ΠΎΠ³ΠΎΠ²ΠΈΡ†Ρ‹ ΠΈ Π½Π° срСднСй ΠΏΠ΅Ρ€ΠΈΡ„Π΅Ρ€ΠΈΠΈ Π² носовом ΠΈ височном сСкторах. На ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΉ дСнь послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ достовСрная Ρ€Π°Π·Π½ΠΈΡ†Π° ΠΌΠ΅ΠΆΠ΄Ρƒ показатСлями ΡΠΎΡ…Ρ€Π°Π½ΡΠ»Π°ΡΡŒ, Π·Π° ΠΈΡΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ Icare. Π§Π΅Ρ€Π΅Π· 2 Π½Π΅Π΄Π΅Π»ΠΈ послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ тономСтричСскиС ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ ΠΌΠ΅ΠΆΠ΄Ρƒ собой достовСрно Π½Π΅ Ρ€Π°Π·Π»ΠΈΡ‡Π°Π»ΠΈΡΡŒ. Π ΠΎΠ³ΠΎΠ²ΠΈΡ‡Π½ΠΎ-компСнсированноС Π’Π“Π” являСтся Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½Ρ‹ΠΌ Π² клиничСском ΠΏΠ»Π°Π½Π΅ тономСтричСским ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΌ, ΠΏΠΎΡΠΊΠΎΠ»ΡŒΠΊΡƒ ΡƒΡ‡ΠΈΡ‚Ρ‹Π²Π°Π΅Ρ‚ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡƒΠ°Π»ΡŒΠ½Ρ‹Π΅ особСнности Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π½ΠΎΠΉ ΠΎΠ±ΠΎΠ»ΠΎΡ‡ΠΊΠΈ Π³Π»Π°Π·Π° ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ². ΠŸΡ€ΠΈ обслСдовании ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с Π³Π»Π°ΡƒΠΊΠΎΠΌΠΎΠΉ этот ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ достовСрно отличаСтся ΠΏΡ€ΠΈ нСкомпСнсированном Π’Π“Π”, Ρ‡Ρ‚ΠΎ Π²Π°ΠΆΠ½ΠΎ для опрСдСлСния ΠΏΡ€Π°Π²ΠΈΠ»ΡŒΠ½ΠΎΠΉ Ρ‚Π°ΠΊΡ‚ΠΈΠΊΠΈ лСчСния. ΠŸΡ€ΠΈ ΠΎΡ†Π΅Π½ΠΊΠ΅ Π’Π“Π” послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ данная тСндСнция ΡΠΎΡ…Ρ€Π°Π½ΡΠ»Π°ΡΡŒ, Ρ‡Ρ‚ΠΎ ΡƒΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚ Π½Π° ΡΠΈΡΡ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ Π½Π΅Π΄ΠΎΠΎΡ†Π΅Π½ΠΊΡƒ ΠΎΡ„Ρ‚Π°Π»ΡŒΠΌΠΎΡ‚ΠΎΠ½ΡƒΡΠ° (ΠΏΠ΅Ρ€Π΅ΠΎΡ†Π΅Π½ΠΊΡƒ эффСкта ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ). Π”ΠΎΡΡ‚ΠΎΠ²Π΅Ρ€Π½ΠΎΡΡ‚ΡŒ исслСдования подтвСрТдаСтся Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌΠΈ исслСдова- ния Π½Π° ΠΏΠ°Ρ€Π½Ρ‹Ρ… Π½Π΅ΠΎΠΏΠ΅Ρ€ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… Π³Π»Π°Π·Π°Ρ…
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