• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.17-26
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• 2023

Blockchain technology has emerged as one of the most crucial solutions in numerous industries, including healthcare. The combination of blockchain technology and cloud computing results in improving access to high-quality telemedicine and healthcare services. In addition to developments in healthcare, the operational strategy outlined in Vision 2030 is extremely essential to the improvement of the standard of healthcare in Saudi Arabia. The purpose of this survey is to give a thorough analysis of the current state of healthcare technologies that are based on blockchain and cloud computing. We highlight some of the unanswered research questions in this rapidly expanding area and provide some context for them. Furthermore, we demonstrate how blockchain technology can completely alter the medical field and keep health records private; how medical jobs can detect the most critical, dangerous errors with blockchain industries. As it contributes to develop concerns about data manipulation and allows for a new kind of secure data storage pattern to be implemented in healthcare especially in telemedicine fields is discussed diagrammatically.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• 한국화재소방학회논문지
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• 제34권1호
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• pp.135-141
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• 2020

2015년 바레인에서 귀국한 한국인이 처음으로 메르스 확진 판정을 받으면서 대한민국 메르스 유입이 확인되었다. 최초 메르스 확진 판정 후 감염자가 병원으로 후송 된 후 2차 감염자가 늘어났고 그 이후 대한민국은 메르스 공포에 떨어야 했다. 이 때 격리구급차 도입이 이루어지고 있었으나 들것 이송 중 오염에 대해서는 뾰족한 방법이 없었고 격리구급차 도입이 제한적으로만 이루어지고 있었기에 서울대학교 병원과 서울시립대학교에서는 이송용 격리 이송 장치 및 공기정화장치(양·음압기)를 개발하였다. 하지만 메르스 사태가 발생한지 4년이 지난 현재까지도 격리이송장치 및 공기정화장치에 대한 성능평가 시험법이 미흡한바 본 연구에서는 시뮬레이션 및 시험을 통하여 도출된 차압, 공기의 흐름에 따른 결과를 통하여 소형격리장비 내부와 외부의 차압을 측정하여 바이러스의 외부유출 및 내부밀폐가 가능한 최소 수치를 제안 하였으며, 이를 통하여 소형격리이송장비의 성능평가 및 시험법에 대하여 제안하였다. 해당 소형격리이송장비의 중요 기능을 정의하고 이를 평가항목으로 선정하여 현실과 유사한 상황을 모사하여 성능평가를 수행하였다. 그 결과 격리이송장치 내부의 양압과 음압의 적절한 압력 구성과 헤파필터의 명확한 기준이 명시되어 격리이송장치 및 공기정화장치의 성능기준을 명확하게 규정해야 함을 도출하였다.

• Journal of Korea Trade
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• 제27권1호
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• pp.139-158
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• 2023

Purpose - The study of co-movements between stock markets is a crucial area of finance and has recently received much interest in a variety of studies, especially in international finance. Stock market co-movements are a major phenomenon in financial markets, but they are not necessarily independent of the real market. Several studies support the idea that bilateral trade linkages significantly impact stock market correlations. Motivated by this perspective, this study investigates whether real market integration due to trade agreements brings about financial market integration in terms of stock market co-movement. Design/methodology - Over the 10 free trade agreements (FTAs) signed by the United States, using a dynamic conditional correlations (DCC) multivariate GARCH (MGRACH) model, we empirically measure the degree of integration by finding DCCs between the US market and the partner country's market. We then track how these correlations evolve over time and compare the results before and after trade agreements. Findings - According to the empirical results, there are positive return spillover effects from the US market to eight counterpart equity markets, except Jordan, Morocco, and Singapore. Especially Mexico, Canada, and Chile have large return spillover effects at the 1% significance level. All partner countries of FTAs generally have positive correlations with the US over the entire period, but the size and variance are somewhat different by country. Meanwhile, not all countries that signed trade agreements with the United States showed the same pattern of stock market co-movement after the agreement. Korea, Mexico, Chile, Colombia, Peru, and Singapore show increasing DCC patterns after trade agreements with the US. However, Canada, Australia, Bahrain, Jordan, and Morocco do not show different patterns before and after trade agreements in DCCs. These countries generally have the characteristic of relatively lower or higher co-movements in stock markets with the US before the signing of the FTAs. Originality/value - To our knowledge, few studies have directly examined the linkages between trade agreements and stock markets. Our approach is novel as it considers the problem of conditional heteroscedasticity and visualizes the change of correlations with time variations. Moreover, analyzing several trade agreements based on the United States enables the results of cross-country pairs to be compared. Hence, this study provides information on the degree of stock market integration with countries with which the United States has trade agreements, while simultaneously allowing us to track whether there have been changes in stock market integration patterns before and after trade agreements.