• The Korean Society of Law and Medicine
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• v.24 no.3
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• pp.59-104
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• 2023

This essay attempts to analyze the public health welfare conception within the text of the Beveridge Report and its realization via the NHS in Great Britain. Once referring to the influence of the Report to create the foundations of the 20th-century welfare system, the quest to scrutinize the original intentions of the Report and its succession to the NHS is certainly intriguing. Furthermore, when regarding the significance of public health policy for a modern state, the effort to engage in Beveridge's conception and its realization is more than timely. In light of such a premise, this paper indulges in its study by the following methods. First, the historical background of the Report - namely, the role of the spirit of the age and the experience of the Emergency Medical Service are to be analyzed to identify the origins of the welfare policies proposed by Beveridge. Furthermore, the public health welfare conception of the Report conceived from its time is reflected upon by engaging on the goal towards social welfare and public health scheme. Lastly, the aims of the NHS and its management, treatment classification, and rehabilitation program are reviewed for comparative analysis with the Report to survey the realization of Beveridge's design. In this process, this paper not only takes into account the original text of the Report - but also other essential works of law and public policy, including the NHS Constitution for England and the National Health Service Act of 1946. The intentions of this study are not bound by merely coinciding with the Report, but resonate significance via reflecting upon the Beveridgian legacy on the modern welfare state from the current perspective. The structured analysis to research the aims and policies of the Report and to compare them to the reality of the NHS may provide an opportunity to confirm the realization of Beveridge's scheme in British society. In addition, this essay is part of an academic endeavor to critically assess the past and the present of the welfare institution in the public health sector. As such, it is hopeful that the essay sheds light on further studies concerning the constructive remedies of the Korean welfare system as well.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.293-300
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• 2024

On October 31, 2023, the revision of the Medical Technologist Act made it mandatory to complete field training courses in order to obtain a license as a radiologic technologist. Therefore, we would like to survey the actual situation of field training in medical institutions to inform the revised Medical Technologist Act and propose improvement measures to increase the effectiveness of field training. A survey was conducted from March to April, 2023, among radiologic technologists working in medical institutions. The questionnaire was sent through a form on a domestic portal site, Company N, and 120 respondents completed it. Eighty-two respondents, or 68.3 percent, had experience in educating on-the-job training students. 58% of the respondents were aware of the fact that the amendment to the Act on Medical Technologist etc. made field training mandatory to obtain a radiologic technologist license. In accordance with Article 9 of the Medical Technologist Act, which prohibits unlicensed persons from practicing, 50% of the respondents were aware that those who are in training to complete an education course equivalent to the license they are seeking to obtain at a university or other institution are allowed to practice as medical Technologists. When asked what is currently taught during fieldwork, 6% of respondents said that they are required to perform radiation-generating activities in addition to observing, guiding patients, and positioning and moving patients. When asked about the future direction of education as fieldwork becomes mandatory for licensure, 77% of respondents said that they will teach more than they currently do. When asked about the appropriate total length of fieldwork, 35% said 12 weeks and 480 hours, 33% said 8 weeks and 320 hours, and 27% said 16 weeks and 640 hours. It can be seen that the current on-the-job training is inadequate according to various regulations, and students' satisfaction is low. However, with the revision of the Act on Medical Technologists, field training has become mandatory to obtain a license as a radiologist, and it is necessary to improve the educational conditions of field training. Therefore, it is necessary to comply with the Nuclear Safety Act and the Rules on the Safety Management of Diagnostic Radiation Generating Devices, introduce standardized training objectives and evaluation systems, designate training hospitals and radiologists in charge of training, and introduce extended training periods and simulation exercises to internalize field training.

• Korean Journal of Poultry Science
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• v.51 no.2
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• pp.57-63
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• 2024

As global warming worsens, it is feared that higher ambient temperatures and relative humidity might result in a more intense heat stress for livestock animals, especially broilers, which lack sweat glands for thermoregulation and have been selectively bred for rapid growth. Therefore, strategic livestock management is needed to mitigate the adverse effects of heat stress on broilers. In Korea's poultry farming systems, tunnel-ventilated broiler houses and pad cooling systems are commonly installed to lower indoor temperatures during the summer. However, caution is advised with pad cooling systems as they can increase the humidity inside the houses, potentially causing further harm. This study aimed to evaluate the effectiveness of pad cooling systems in tunnel-ventilated broiler house by assessing the reduction in indoor temperature using the Temperature-Humidity Index (THI), which accounts for the impact of relative humidity. Temperature and humidity data were collected during the summer (Jun to Sep) from eight farms with tunnel-ventilated broiler house located in different regions of Korea. The farms were divided into two groups based on the use of pad cooling systems is used, and temperature and humidity data, along with THI values, were analyzed two weeks before the birds were marketed. Meta-analysis results showed that at the hottest time of the day, 14:00, farms with pad cooling systems had significantly lower indoor temperatures compared to the control group, but observed an increase in indoor temperatures by 16:00 (p<0.05). There is no significant difference in relative humidity (p>0.05). The THI values decreased in the treatment group with cooling pads compared to the control group starting from 15:00, suggesting a diminished effect (p<0.05). This study indicates the potential for developing optimal operational guidelines for cooling pads to reduce heat stress in broilers during the summer season.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.4
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• pp.29-39
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• 2023

In this study, an analysis of medical waste generation characteristics was conducted, differentiating between ordinary situation and the outbreaks of massive infectious diseases. During ordinary situation, prediction models for medical waste quantities by type, general medical waste(G-MW), hazardous medical waste(H-MW), infectious medical waste(I-MW), were established through regression analysis, with all significance values (p) being <0.0001, indicating statistical significance. The determination coefficient(R2) values for prediction models of each category were analyzed as follows : I-MW(R2=0.9943) > G-MW(R2=0.9817) > H-MW(R2=0.9310). Additionally, factors such as GDP(G-MW), the number of medical institutions (H-MW), and the elderly population ratio(I-MW), utilized as influencing factors and consistent with previous literature, showed high correlations. The total MW generation, evaluated by combining each model, had an MAE of 2,615 and RMSE of 3,353. This indicated accuracy levels similar to the medical waste models of H-MW(2,491, 2,890) and I-MW(2,291, 3,267). Due to limitations in accurately estimating the quantity of medical waste during the rapid and outbreaks of massive infectious diseases, the generation unit of I-MW was derived to analyze its characteristics. During the early unstable stage of infectious disease outbreaks, the generation unit was 8.74 kg/capita·day, 2.69 kg/capita·day during the stable stage, and an average of 0.08 kg/capita·day during the reduction stage. Correlation analysis between generation unit of I-MW and lethality rates showed +0.99 in the unstable stage, +0.52 in the stable stage, and +0.96 in the reduction period, demonstrating a very high positive correlation of +0.95 or higher throughout the entire outbreaks of massive infectious diseases. The results derived from this study are expected to play a useful role in establishing an effective medical waste management system in the field of health care.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Proceedings of the KSEEG Conference
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• 2001.05b
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• pp.42-63
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• 2001

The detail survey on the Songsanri tomb site including the Muryong royal tomb was carried out during the period from May 1 , 1996 to April 30, 1997. A quantitative analysis was tried to find changes of tomb itself since the excavation. Main subjects of the survey are to find out the cause of infiltration of rain water and groundwater into the tomb and the tomb site, monitoring of the movement of tomb structure and safety, removal method of the algae inside the tomb, and air controlling system to solve high humidity condition and dew inside the tomb. For these purposes, detail survery inside and outside the tombs using a electronic distance meter and small airplane, monitoring of temperature and humidity, geophysical exploration including electrical resistivity, geomagnetic, gravity and georadar methods, drilling, measurement of physical and chemical properties of drill core and measurement of groundwater permeability were conducted. We found that the center of the subsurface tomb and the center of soil mound on ground are different 4.5 meter and 5 meter for the 5th tomb and 7th tomb, respectively. The fact has caused unequal stress on the tomb structure. In the 7th tomb (the Muryong royal tomb), 435 bricks were broken out of 6025 bricks in 1972, but 1072 bricks are broken in 1996. The break rate has been increased about 250% for just 24 years. The break rate increased about 290% in the 6th tomb. The situation in 1996 is the result for just 24 years while the situation in 1972 was the result for about 1450 years. Status of breaking of bircks represents that a severe problem is undergoing. The eastern wall of the Muryong royal tomb is moving toward inside the tomb with the rate of 2.95 mm/myr in rainy season and 1.52 mm/myr in dry season. The frontal wall shows biggest movement in the 7th tomb having a rate of 2.05 mm/myr toward the passage way. The 6th tomb shows biggest movement among the three tombs having the rate of 7.44mm/myr and 3.61mm/myr toward east for the high break rate of bricks in the 6th tomb. Georadar section of the shallow soil layer represents several faults in the top soil layer of the 5th tomb and 7th tomb. Raninwater flew through faults tnto the tomb and nearby ground and high water content in nearby ground resulted in low resistance and high humidity inside tombs. High humidity inside tomb made a good condition for algae living with high temperature and moderate light source. The 6th tomb is most severe situation and the 7th tomb is the second in terms of algae living. Artificial change of the tomb environment since the excavation, infiltration of rain water and groundwater into the tombsite and bad drainage system had resulted in dangerous status for the tomb structure. Main cause for many problems including breaking of bricks, movement of tomb walls and algae living is infiltration of rainwater and groundwater into the tomb site. Therefore, protection of the tomb site from high water content should be carried out at first. Waterproofing method includes a cover system over the tomvsith using geotextile, clay layer and geomembrane and a deep trench which is 2 meter down to the base of the 5th tomb at the north of the tomv site. Decrease and balancing of soil weight above the tomb are also needed for the sfety of tomb structures. For the algae living inside tombs, we recommend to spray K101 which developed in this study on the surface of wall and then, exposure to ultraviolet light sources for 24 hours. Air controlling system should be changed to a constant temperature and humidity system for the 6th tomb and the 7th tomb. It seems to much better to place the system at frontal room and to ciculate cold air inside tombs to solve dew problem. Above mentioned preservation methods are suggested to give least changes to tomb site and to solve the most fundmental problems. Repairing should be planned in order and some special cares are needed for the safety of tombs in reparing work. Finally, a monitoring system measuring tilting of tomb walls, water content, groundwater level, temperature and humidity is required to monitor and to evaluate the repairing work.

• Food preservation and processing industry
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• v.8 no.2
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• pp.6-12
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• 2009

면역반응은 외부 감염원으로부터 신체를 보호하고 외부감염원을 제거하고자 하는 주요항상성 유지기전의 하나이다. 이들 반응은 골수에서 생성되고 비장, 흉선 및 임파절 등에서 성숙되는 면역세포들에 의해 매개된다. 보통 태어나면서부터 얻어진 선천성 면역반응을 매개하는 대식세포, 수지상 세포 등과, 오랜기간 동안 감염된 다양한 면역원에 대한 경험을 토대로 얻어진 획득성 면역을 담당하는 T 임파구 등이 대표적인 면역세포로 알려져 있다. 다양한 면역질환이 최근 주요 사망률의 원인이 되고 있다. 최근, 암, 당뇨 및 뇌혈관질환 등이 생체에서 발생되는 급 만성염증에 의해 발생된다고 보고됨에 따라 면역세포 매개성 염증질환에 대한 치료제 개발을 서두르고 있다. 또한 암환자의 급격한 증가는 암발생의 주요 방어기전인 면역력 증강에 대한 요구들을 가중시키고 있다. 예로부터 사용되어 오던 고려인삼과 홍삼은 기를 보호하고 원기를 회복하는 명약으로 알려진 대표적인 우리나라 천연생약이다. 특별히, 홍삼은 단백질과 핵산의 합성을 촉진시키고, 조혈작용, 간기능 회복, 혈당강하, 운동수행 능력증대, 기억력 개선, 항피로작용 및 면역력 증대에 매우 효과가 좋은 것으로 보고되고 있다. 홍삼에 관한 많은 연구에 비해, 현재까지 홍삼이 면역력 증강에 미치는 효과에 대한 분자적 수준에서의 연구는 매우 미미한 것으로 확인되어져 있다. 홍삼의 투여는 NK 세포나 대식세포의 활성이 증가하고 항암제의 암세포 사멸을 증가시키는 것으로 확인되어졌다. 현재까지 알려진 주요 면역증강 성분은 산성다당류로 보고되었다. 또 한편으로 일부 진세노사이드류에서 항염증 효능이 확인되어졌으며, 이를 통해 피부염증 반응과 관절염에 대한 치료 효과가 있는 것으로 추측되고 있다 [본 연구는 KT&G 연구출연금 (2009-2010) 지원을 받아 이루어졌기에 이에 감사드린다]. 면역반응은 외부 감염물질의 침입으로 유도된 질병환경을 제거하고 수복하는 중요한 생체적 방어작용의 하나이다. 이들 과정은 체내로 유입된 미생물이나 미세화학물질들과 같은 독성물질을 소거하거나 파괴하는 것을 주요 역할로 한다. 외부로 부터 인체에 들어온 이물질에 대한 방어기전은 현재 두 가지 종류의 면역반응으로 구분해서 설명한다. 즉, 선천성 면역 반응 (innate immunity)과 후천성 면역 반응 (adaptive immunity)이 그것이다. 선천성 면역반응은 1) 피부나 점막의 표면과 같은 해부학적인 보호벽 구조와 2) 체온과 낮은 pH 및 chemical mediator (리소자임, collectin류) 등과 같은 생리적 방어구조, 3) phagocyte류 (대식세포, 수지상세포 및 호중구 등)에 의한 phagocytic/endocytic 방어, 그리고 4) 마지막으로 염증반응을 통한 감염에 저항하는 면역반응 등으로 구분된다. 후천성 면역반응은 획득성면역이라고도 불리고 특이성, 다양성, 기억 및 자기/비자기의 인식이라는 네 가지의 특징을 가지고 있으며, 외부 유입물질을 제거하는 반응에 따라 체액성 면역 반응 (humoral immune response)과 세포성 면역반응 (cell-mediated immune response)으로 구분된다. 체액성 면역은 침입한 항원의 구조 특이적으로 생성된 B cell 유래 항체와의 반응과 간이나 대식세포 등에서 합성되어 분비된 혈청내 보체 등에 의해 매개되는 반응으로 구성되어 있다. 세포성 면역반응은 T helper cell (CD4+), cytotoxic T cell (CD8+), B cell 및antigen presenting cell 중개를 통한 세포간 상호 작용에 의해 발생되는 면역반응이다. 선천성 면역반응의 하나인 염증은 우리 몸에서 가장 빈번히 발생되고 있는 방어작용의 하나이다. 예를 들면 감기에 걸렸을 경우, 환자의 편도선내 대식세포나 수지상세포류는 감염된 바이러스 단독 혹은 동시에 감염된 박테리아를 상대로 다양한 염증성 반응을 유도하게 된다. 또한, 상처가 생겼을 경우에도 감염원을 통해 유입된 병원성 세균과 주위조직내 선천성 면역담당 세포들 간의 면역학적 전투가 발생되게 된다. 이들 과정을 통해, 주위 세포나 조직이 손상되면, 즉각적으로 이들 면역세포들 (주로 phagocytes류)은 신속하게 손상을 극소화하고 더 나가서 손상된 부위를 원상으로 회복시키려는 일련의 염증반응을 유도하게 된다. 이들 반응은 우리가 흔히 알고 있는 발적 (redness), 부종 (swelling), 발열 (heat), 통증 (pain) 등의 증상으로 나타나게 된다. 즉, 손상된 부위 주변에 존재하는 모세혈관에 흐르는 혈류의 양이 증가하면서 혈관의 직경이 늘어나게 되고, 이로 인한 조직의 홍반과, 부어 오른 혈관에 의해 발열과 부종이 초래되는 것이다. 확장된 모세혈관의 투과성 증가는 체액과 세포들이 혈관에서 조직으로 이동하게 하는 원동력이 되고, 이를 통해 축적된 삼출물들은 단백질의 농도를 높여, 최종적으로 혈관에 존재하는 체액들이 조직으로 더 많이 이동되도록 유도하여 부종을 형성시킨다. 마지막으로 혈관 내 존재하는 면역세포들은 혈판 내벽에 점착되고 (margination), 혈관벽의 간극을 넓히는 역할을 하는 히스타민 (histamine)이나 일산화질소(nitric oxide : NO), 프로스타그린딘 (prostagladins : PGE2) 및 류코트리엔 (leukotriens) 등과 같은 chemical mediator의 도움으로 인해 혈관벽 사이로 삼출하게 되어 (extravasation), 손상된 부위로 이동하여 직접적인 외부 침입 물질의 파괴나 다른 면역세포들을 모으기 위한 cytokine (tumor necrosis factor [TNF]-$\alpha$, interleukin [IL]-1, IL-6 등) 혹은 chemokine (MIP-l, IL-8, MCP-l등)의 분비 등을 수행함으로써 염증반응을 매개하게 된다. 염증과정시 발생되는 여러 mediator 중 PGE2나 NO 및 TNF-$\alpha$ 등은 실험적 평가가 용이하여 이들 mediator 자체나 생성관련효소 (cyclooxygenase [COX] 및 nitric oxide synthase [NOS] 등)들은 현재항염증 치료제의 개발 연구시 주요 표적으로 연구되고 있다. 염증 반응은 지속기간에 따라 크게 급성염증과 만성염증으로 나뉘며, 삼출물의 종류에 따라서는 장액성, 섬유소성, 화농성 및 출혈성 염증 등으로 구분된다. 급성 염증 (acute inflammation)반응은 수일 내지 수주간 지속되는 일반적인 염증반응이라고 볼 수 있다. 국소반응은 기본징후인 발열과 발적, 부종, 통증 및 기능 상실이 특징적이며, 현미경적 소견으로는 혈관성 변화와 삼출물 형성이 주 작용이므로 일명 삼출성 염증이라고 한다. 만성 염증 (chronic inflammation)은, 급성 염증으로부터 이행되거나 만성으로 시작된다. 염증지속 기간은 보통 4주 이상 장기화 된다. 보통 염증의 경우에는 염증 생성 cytokine인 Th1 cytokine (IL-2, interferone [IFN]-$\gamma$ 및 TNF-$\alpha$ 등)의 생성 후, 거의 즉각적으로 항 염증성 cytokine인 Th2 cytokine(IL-4, IL-6, IL-10 및 transforming growth factor [TGF]-$\beta$ 등)이 생성되어 정상반응으로 회복된다. 그러나, 어떤 원인에서든 면역세포에 의한 염증원 제거 반응이 문제가 되면, 만성염증으로 진행된다. 이 반응에 주로 작용을 하는 염증세포로는 단핵구와 대식세포, 림프구, 형질세포 등이 있다. 암은 전세계적으로 사망률 1위의 원인이 되는 면역질환의 하나이다. 산화적 스트레스나 자외선 조사 혹은 암유발 물질들에 의해 염색체내 protooncogene, tumor-suppressor gene 혹은 DNA repairing gene의 일부 DNA의 돌연변이 혹은 결손 등이 발행되면 정상세포는 암화과정을 시작하게 된다. 양성세포 수준에서 약 5에서 10여년 후 악성수준의 암세포가 생성되게 되면 이들 세포는 새로운 환경을 찾아 전이하게 되는데 이를 통해 암환자들은 다양한 장기에 동인 오리진의 암세포들이 생성한 종양들을 가지게 된다. 이들 종양세포는 정상 장기의 기능을 손상시켜며 결국 생명을 잃게 만든다. 이들 염색체 수준에서의 돌연변이 유래 암세포는 거의 대부분이 체내 면역시스템에 의해 사멸되는 것으로 알려져 있다. 그러나 계속되는 스트레스나 암유발 물질의 노출은 체내 면역체계를 파괴하면서 최후의 방어선을 무너뜨리면서 암발생에 무방비 상태를 만들게 된다. 이런 이유로 체내 면역시스템의 정상적 가동 및 증강을 유도하게 하는 전략이 암예방시 매우 중요한 표적으로 인식되면서 다양한 형태의 면역증강 물질 개발을 시도하고 있다. 인삼은 두릅나무과의 여러해살이 풀로써, 오랜동안 한방 및 민간에서 원기를 회복시키고, 각종 질병을 치료할 수단으로 사용되고 있는 대표적인 전통생약이다. 예로부터 불로(不老), 장생(長生), 익기(益氣), 경신(經身)의 명약으로 구전되어졌는데, 이는 약 2천년 전 중국의 신농본초경(神農本草經)에서 "인삼은 오장(五腸)을 보하고, 정신을 안정시키고, 혼백을 고정하며 경계를 멈추게 하고, 외부로부터 침입하는 병사를 제거하여주며, 눈을 밝게 하고 마음을 열어 더욱 지혜롭게 하고 오랫동안 복용하면 몸이 가벼워지고 장수한다" 라고 기술되어있는 데에서 유래한 것이다. 다양한 연구를 통해 우리나라에서 생산되는 고려인삼 (Panax ginseng)이 효능 면에서 가장 탁월한 것으로 알려져 있으며 특별이 고려인삼으로부터 제조된 고려홍삼은 전세계적으로도 그 효능이 우수한 것으로 보고되어 있다. 대부분의 홍삼 약효는 dammarane계열의 triterpenoid인 ginsenosides라고 불리는 인삼 saponin에 의해 기인된 것으로 알려져 있다. 이들 화합물군의 기본 골격에 따라, protopanaxadiol (PD)계 (22종) 및 protopanaxatriol (PT)계 (10종)으로 구분되고 있다 (표 1). 실험적 접근을 통해 인삼의 약리작용 이해를 위한 다양한 노력들이 경주되고 있으나, 여전히 많은 부분에서 충분히 이해되고 있지 않다. 그러나, 현재까지 연구된 인삼의 약리작용 관련 연구들은 심혈관, 당뇨, 항암 및 항스트레스 등과 같은 분야에서 인삼효능이 우수한 것으로 보고하고 있다. 그러나 면역조절 및 염증현상과 관련된 최근 연구결과들은 많지 않으나, 향후 다양하게 연구될 효능부분으로 인식되고 있다.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.2
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• pp.101-108
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• 2017

Purpose: The proton used in proton therapy has a characteristic of giving a small dose to the normal tissue in front of the tumor site while forming a Bragg peak at the cancer tissue site and giving up the maximum dose and disappearing immediately. It is very important to verify the proton arrival position. In this study, we used the off-line PET CT method to measure the distribution of positron emitted from nucleons such as 11C (half-life = 20 min), 150 (half-life = 2 min) and 13N The range and distal falloff point of the proton were verified by measurement. Materials and Methods: In the IEC 2001 Body Phantom, 37 mm, 28 mm, and 22 mm spheres were inserted. The phantom was filled with water to obtain a CT image for each sphere size. To verify the proton range and distal falloff points, As a treatment planning system, SOBP were set at 46 mm on 37 mm sphere, 37 mm on 28 mm, and 33 mm on 22 mm sphere for each sphere size. The proton was scanned in the same center with a single beam of Gantry 0 degree by the scanning method. The phantom was scanned using PET-CT equipment. In the PET-CT image acquisition method, 50 images were acquired per minute, four ROIs including the spheres in the phantom were set, and 10 images were reconstructed. The activity profile according to the depth was compared to the dose profile according to the sphere size established in the treatment plan Results: The PET-CT activity profile decreased rapidly at the distal falloff position in the 37 mm, 28 mm, and 22 mm spheres as well as the dose profile. However, in the SOBP section, which is a range for evaluating the range, the results in the proximal part of the activity profile are different from those of the dose profile, and the distal falloff position is compared with the proton therapy plan and PET-CT As a result, the maximum difference of 1.4 mm at the 50 % point of the Max dose, 1.1 mm at the 45 % point at the 28 mm sphere, and the difference at the 22 mm sphere at the maximum point of 1.2 mm were all less than 1.5 mm in the 37 mm sphere. Conclusion: To maximize the advantages of proton therapy, it is very important to verify the range of the proton beam. In this study, the proton range was confirmed by the SOBP and the distal falloff position of the proton beam using PET-CT. As a result, the difference of the distally falloff position between the activity distribution measured by PET-CT and the proton therapy plan was 1.4 mm, respectively. This may be used as a reference for the dose margin applied in the proton therapy plan.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.