• Title/Summary/Keyword: 에너지기술

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Evaluation of Growth Characteristics and Feed Value of Korean Native Sweet Sorghum as Forage Crop (사료작물로서 국내 재래종 단수수의 생육 특징 및 사료가치 평가)

  • Hyun-Sik Choi;Ha Guyn Sung
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.43 no.4
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    • pp.232-239
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    • 2023
  • This study was to evaluate the values of Korean native sweet sorghum as a new feed crop for ruminants. Sweet sorghum was the Muan native species (Bioenergy Crop Research Center, National Institute of Crop Science), and cultivated from May to October 2021 at Sangji University (Wonju-si, Gangwon-do, Korea). There were a non-treated group (Con), a recommended amount treatment (RD) and a treatment with double the recommended amount (Double RD) by an oil cake fertilizer. Plant height was measured at weekly intervals for 12 weeks after planting sweet sorghum seedlings, and was a significant difference in the order of Double RD, followed by RD and Con in 7 weeks (p<0.05). Feed values and sugar contents were measured in 7, 9, and 11 weeks. Crude protein of Double RD was higher than that of the other treatments in 7 and 9 weeks (p<0.05). Crude fat was higher at Double RD than the other one in 9 weeks (p<0.05). ADF and NDF of Double RD were higher than the other one (p<0.05). When it was compared to corn and sudangrass hybrids grown on farms, Crude protein was lower in sweet sorghum than other crops (p<0.05), and crude fat was higher in sweet sorghum than corn (p<0.05). Crude fiber, ADF and NDF were higher in sweet sorghum compared to corn and sudangrass (p<0.05). The sugar contents of sweet sorghum were 4.07 ± 0.12~7.63 ± 0.21 brix, and showed higher than corn and sudangrass hybrid (p<0.05). The rumen in situ digestibility of sweet sorghum was 30.73~38.13% at the 9th and 11th weeks, and showed higher than that of corn and sudangrass hybrids (p<0.05). Therefore, it is considered that Korean native sweet sorghum has sufficient value as a new forage crop for ruminants, and good value as yield, nutrients and digestibility, when the grass height is 273.33~332.50 cm.

A Study on the Medical Application and Personal Information Protection of Generative AI (생성형 AI의 의료적 활용과 개인정보보호)

  • Lee, Sookyoung
    • The Korean Society of Law and Medicine
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    • v.24 no.4
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    • pp.67-101
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    • 2023
  • The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

A Study of Equipment Accuracy and Test Precision in Dual Energy X-ray Absorptiometry (골밀도검사의 올바른 질 관리에 따른 임상적용과 해석 -이중 에너지 방사선 흡수법을 중심으로-)

  • Dong, Kyung-Rae;Kim, Ho-Sung;Jung, Woon-Kwan
    • Journal of radiological science and technology
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    • v.31 no.1
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    • pp.17-23
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    • 2008
  • Purpose : Because there is a difference depending on the environment as for an inspection equipment the important part of bone density scan and the precision/accuracy of a tester, the management of quality must be made systematically. The equipment failure caused by overload effect due to the aged equipment and the increase of a patient was made frequently. Thus, the replacement of equipment and additional purchases of new bonedensity equipment caused a compatibility problem in tracking patients. This study wants to know whether the clinical changes of patient's bonedensity can be accurately and precisely reflected when used it compatiblly like the existing equipment after equipment replacement and expansion. Materials and methods : Two equipments of GE Lunar Prodigy Advance(P1 and P2) and the Phantom HOLOGIC Spine Road(HSP) were used to measure equipment precision. Each device scans 20 times so that precision data was acquired from the phantom(Group 1). The precision of a tester was measured by shooting twice the same patient, every 15 members from each of the target equipment in 120 women(average age 48.78, 20-60 years old)(Group 2). In addition, the measurement of the precision of a tester and the cross-calibration data were made by scanning 20 times in each of the equipment using HSP, based on the data obtained from the management of quality using phantom(ASP) every morning (Group 3). The same patient was shot only once in one equipment alternately to make the measurement of the precision of a tester and the cross-calibration data in 120 women(average age 48.78, 20-60 years old)(Group 4). Results : It is steady equipment according to daily Q.C Data with $0.996\;g/cm^2$, change value(%CV) 0.08. The mean${\pm}$SD and a %CV price are ALP in Group 1(P1 : $1.064{\pm}0.002\;g/cm^2$, $%CV=0.190\;g/cm^2$, P2 : $1.061{\pm}0.003\;g/cm^2$, %CV=0.192). The mean${\pm}$SD and a %CV price are P1 : $1.187{\pm}0.002\;g/cm^2$, $%CV=0.164\;g/cm^2$, P2 : $1.198{\pm}0.002\;g/cm^2$, %CV=0.163 in Group 2. The average error${\pm}$2SD and %CV are P1 - (spine: $0.001{\pm}0.03\;g/cm^2$, %CV=0.94, Femur: $0.001{\pm}0.019\;g/cm^2$, %CV=0.96), P2 - (spine: $0.002{\pm}0.018\;g/cm^2$, %CV=0.55, Femur: $0.001{\pm}0.013\;g/cm^2$, %CV=0.48) in Group 3. The average error${\pm}2SD$, %CV, and r value was spine : $0.006{\pm}0.024\;g/cm^2$, %CV=0.86, r=0.995, Femur: $0{\pm}0.014\;g/cm^2$, %CV=0.54, r=0.998 in Group 4. Conclusion: Both LUNAR ASP CV% and HOLOGIC Spine Phantom are included in the normal range of error of ${\pm}2%$ defined in ISCD. BMD measurement keeps a relatively constant value, so showing excellent repeatability. The Phantom has homogeneous characteristics, but it has limitations to reflect the clinical part including variations in patient's body weight or body fat. As a result, it is believed that quality control using Phantom will be useful to check mis-calibration of the equipment used. A value measured a patient two times with one equipment, and that of double-crossed two equipment are all included within 2SD Value in the Bland - Altman Graph compared results of Group 3 with Group 4. The r value of 0.99 or higher in Linear regression analysis(Regression Analysis) indicated high precision and correlation. Therefore, it revealed that two compatible equipment did not affect in tracking the patients. Regular testing equipment and capabilities of a tester, then appropriate calibration will have to be achieved in order to calculate confidential BMD.

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