• 제목/요약/키워드: Biological systems

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Contact Tracking Development Trend Using Bibliometric Analysis

  • Li, Chaoqun;Chen, Zhigang;Yu, Tongrui;Song, Xinxia
    • Journal of Information Processing Systems
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    • 제18권3호
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    • pp.359-373
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    • 2022
  • The new crown pneumonia (COVID-19) has become a global epidemic. The disease has spread to most countries and poses a challenge to the healthcare system. Contact tracing technology is an effective way for public health to deal with diseases. Many experts have studied traditional contact tracing and developed digital contact tracking. In order to better understand the field of contact tracking, it is necessary to analyze the development of contact tracking in the field of computer science by bibliometrics. The purpose of this research is to use literature statistics and topic analysis to characterize the research literature of contact tracking in the field of computer science, to gain an in-depth understanding of the literature development status of contact tracking and the trend of hot topics over the past decade. In order to achieve the aforementioned goals, we conducted a bibliometric study in this paper. The study uses data collected from the Scopus database. Which contains more than 10,000 articles, including more than 2,000 in the field of computer science. For popular trends, we use VOSviewer for visual analysis. The number of contact tracking documents published annually in the computer field is increasing. At present, there are 200 to 300 papers published in the field of computer science each year, and the number of uncited papers is relatively small. Through the visual analysis of the paper, we found that the hot topic of contact tracking has changed from the past "mathematical model," "biological model," and "algorithm" to the current "digital contact tracking," "privacy," and "mobile application" and other topics. Contact tracking is currently a hot research topic. By selecting the most cited papers, we can display high-quality literature in contact tracking and characterize the development trend of the entire field through topic analysis. This is useful for students and researchers new to field of contact tracking ai well as for presenting our results to other subjects. Especially when comprehensive research cannot be conducted due to time constraints or lack of precise research questions, our research analysis can provide value for it.

Prospects of omics-driven synthetic biology for sustainable agriculture

  • Soyoung Park;Sung-Dug Oh;Vimalraj Mani;Jin A Kim;Kihun Ha;Soo-Kwon Park;Kijong Lee
    • 농업과학연구
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    • 제49권4호
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    • pp.749-760
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    • 2022
  • Omics-driven synthetic biology is a multidisciplinary research field that creates new artificial life by employing genetic components, biological devices, and engineering technique based on genetic knowledge and technological expertise. It is also utilized to make valuable biomaterials with limited production via current organisms faster, more efficient, and in huge quantities. As the bioeconomic age begins, and the global synthetic biology market becomes more competitive, investment in research and development (R&D) and associated sectors has grown considerably. By overcoming the constraints of present biotechnologies through the merging of big data and artificial intelligence technologies, huge ripple effects are envisaged in the pharmaceutical, chemical, and energy industries. In agriculture, synthetic biology is being used to solve current agricultural problems and develop sustainable agricultural systems by increasing crop productivity, implementing low-carbon agriculture, and developing plant-based, high-value-added bio-materials such as vaccines for diagnosing and preventing livestock diseases. As international regulatory debates on synthetic biology are now underway, discussions should also take place in our country for the growth of bioindustries and the dissemination of research findings. Furthermore, the system must be improved to facilitate practical application and to enhance the risk evaluation technology and management system.

뎁스카메라와 YOLOAddSeg 알고리즘을 이용한 방사선치료환자 미세동작인식 및 실시간 위치보정기술 개발 (Development of Motion Recognition and Real-time Positioning Technology for Radiotherapy Patients Using Depth Camera and YOLOAddSeg Algorithm)

  • 박기용;류규하
    • 대한의용생체공학회:의공학회지
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    • 제44권2호
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    • pp.125-138
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    • 2023
  • The development of AI systems for radiation therapy is important to improve the accuracy, effectiveness, and safety of cancer treatment. The current system has the disadvantage of monitoring patients using CCTV, which can cause errors and mistakes in the treatment process, which can lead to misalignment of radiation. Developed the PMRP system, an AI automation system that uses depth cameras to measure patient's fine movements, segment patient's body into parts, align Z values of depth cameras with Z values, and transmit measured feedback to positioning devices in real time, monitoring errors and treatments. The need for such a system began because the CCTV visual monitoring system could not detect fine movements, Z-direction movements, and body part movements, hindering improvement of radiation therapy performance and increasing the risk of side effects in normal tissues. This study could provide the development of a field of radiotherapy that lags in many parts of the world, along with the economic and social importance of developing an independent platform for radiotherapy devices. This study verified its effectiveness and efficiency with data through phantom experiments, and future studies aim to help improve treatment performance by improving the posture correction mechanism and correcting left and right up and down movements in real time.

위 내시경 이미지 품질에 따른 병변 검출 모델의 성능 비교 연구 (A Performance Comparison Study of Lesion Detection Model according to Gastroscopy Image Quality)

  • 이율희;김영재;김광기
    • 대한의용생체공학회:의공학회지
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    • 제44권2호
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    • pp.118-124
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    • 2023
  • Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gastroscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high quality data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addition, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.

수술 동영상에서의 인공지능을 사용한 출혈 검출 연구 (A Study on the Bleeding Detection Using Artificial Intelligence in Surgery Video)

  • 정시연;김영재;김광기
    • 대한의용생체공학회:의공학회지
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    • 제44권3호
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    • pp.211-217
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    • 2023
  • Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient outcomes.

당귀 당절임 추출음료의 성분 및 약리효능 (Components and Pharmaceutical Effect of Beverage Extracted from Sugar-treated Angelica gigas)

  • 박정자;장광진;서건식;이희선;이건순;박철호;이만휘
    • 현장농수산연구지
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    • 제10권1호
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    • pp.59-65
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    • 2008
  • 본 연구에서는 메밀과 함께 평창의 주요 농산물인 당귀의 새로운 수요를 창출하고 부가가치를 높이기 위하여 당절임 숙성 음료의 효능을 알아보고자 영양성분 조사 및 동물실험을 통하여 혈소판 응집 및 혈액응고에 관한 실험을 수행하였으며 그 결과는 다음과 같다. 영양성분분석에서는 일반적인 성분검사로서 특별한 점이 발견되지는 않았으나 동물실험에서 밝혀진 효능검사는 당귀로 만든 음료가 콜라겐 유도 혈소판 응집반응을 유의성 있게 억제하였다. 일주일간의 짧은 기간 임에도 1%, 10% 및 20%의 음용수로 투여된 실험동물에서 얻어진 혈소판은 콜라겐 유도 혈소판 응집반응을 강력히 억제하였다. 또한 당귀를 음용수에 20%의 용랑을 일주일간의 급성투여로서 혈액응고계에 영향을 미치지 않는 것으로 판단되었다. 결국, 영양성분 및 혈행 개선에 효과가 있다는 것을 알 수 있었다. 이것은 당귀를 이용하여 당절임 추출음료의 개발이 가능하며, 새로운 건강음료가 될 수 있음을 전망하였다. 그러나 제품화하기 위해서는 장기적인 실험 및 다양한 방법의 연구가 추가적으로 이루어져야 한다.

Modeling Soil Temperature of Sloped Surfaces by Using a GIS Technology

  • Yun, Jin I.;Taylor, S. Elwynn
    • 한국작물학회지
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    • 제43권2호
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    • pp.113-119
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    • 1998
  • Spatial patterns of soil temperature on sloping lands are related to the amount of solar irradiance at the surface. Since soil temperature is a critical determinant of many biological processes occurring in the soil, an accurate prediction of soil temperature distribution could be beneficial to agricultural and environmental management. However, at least two problems are identified in soil temperature prediction over natural sloped surfaces. One is the complexity of converting solar irradiances to corresponding soil temperatures, and the other, if the first problem could be solved, is the difficulty in handling large volumes of geo-spatial data. Recent developments in geographic information systems (GIS) provide the opportunity and tools to spatially organize and effectively manage data for modeling. In this paper, a simple model for conversion of solar irradiance to soil temperature is developed within a GIS environment. The irradiance-temperature conversion model is based on a geophysical variable consisting of daily short- and long-wave radiation components calculated for any slope. The short-wave component is scaled to accommodate a simplified surface energy balance expression. Linear regression equations are derived for 10 and 50 cm soil temperatures by using this variable as a single determinant and based on a long term observation data set from a horizontal location. Extendability of these equations to sloped surfaces is tested by comparing the calculated data with the monthly mean soil temperature data observed in Iowa and at 12 locations near the Tennessee - Kentucky border with various slope and aspect factors. Calculated soil temperature variations agreed well with the observed data. Finally, this method is applied to a simulation study of daily mean soil temperatures over sloped corn fields on a 30 m by 30 m resolution. The outputs reveal potential effects of topography including shading by neighboring terrain as well as the slope and aspect of the land itself on the soil temperature.

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Evaluation on the implications of microbial survival to the performance of an urban stormwater tree-box filter

  • Geronimo, Franz Kevin;Reyes, Nash Jett;Choi, Hyeseon;Guerra, Heidi;Jeon, Minsu;Kim, Lee-Hyung
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2021년도 학술발표회
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    • pp.128-128
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    • 2021
  • Most of the studies about stormwater low impact development technologies used generalized observations without fully understanding the mechanisms affecting the whole performance of the systems from catchment to the facility itself. At present, these LID technologies have been treated as black box due to fluctuating flow and environmental conditions affecting its operation and treatment performance. As such, the implications of microbial community to the overall performance of the tree-box filter were investigated in this study. Summer season was found to be the most suitable season for microorganism growth since more microorganism were found during this season. Least microorganism count was found in spring because of the plant growth during this season since plant penology influences the seasonal dynamics of soil microorganisms. Litterfall during fall season might have affected the microorganism count during winter since, during this season, the compositional variety of soil organic matter changes affecting growth of soil microbial communities. Microbial analyses of sediment samples collected in the system revealed that the most dominant microorganism phylum is Proteobacteria in all the seasons in both inlet and outlet comprising 37% to 47% of the total microorganism count. Proteobacteria was followed by Acidobacteria, Actinobacteria and Chloroflexi which comprises 6% to 20%, 9% to 20% and 2% to 27%, respectively of the total microorganism count for each season. These findings were useful in optimizing the design and performance of tree box filters considering physical, chemical and biological pollutant removal mechanisms.

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Modeling the long-term vegetation dynamics of a backbarrier salt marsh in the Danish Wadden Sea

  • Daehyun Kim
    • Journal of Ecology and Environment
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    • 제47권2호
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    • pp.49-62
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    • 2023
  • Background: Over the past three decades, gradual eustatic sea-level rise has been considered a primary exogenous factor in the increased frequency of flooding and biological changes in several salt marshes. Under this paradigm, the potential importance of short-term events, such as ocean storminess, in coastal hydrology and ecology is underrepresented in the literature. In this study, a simulation was developed to evaluate the influence of wind waves driven by atmospheric oscillations on sedimentary and vegetation dynamics at the Skallingen salt marsh in southwestern Denmark. The model was built based on long-term data of mean sea level, sediment accretion, and plant species composition collected at the Skallingen salt marsh from 1933-2006. In the model, the submergence frequency (number yr-1) was estimated as a combined function of wind-driven high water level (HWL) events (> 80 cm Danish Ordnance Datum) affected by the North Atlantic Oscillation (NAO) and changes in surface elevation (cm yr-1). Vegetation dynamics were represented as transitions between successional stages controlled by flooding effects. Two types of simulations were performed: (1) baseline modeling, which assumed no effect of wind-driven sea-level change, and (2) experimental modeling, which considered both normal tidal activity and wind-driven sea-level change. Results: Experimental modeling successfully represented the patterns of vegetation change observed in the field. It realistically simulated a retarded or retrogressive successional state dominated by early- to mid-successional species, despite a continuous increase in surface elevation at Skallingen. This situation is believed to be caused by an increase in extreme HWL events that cannot occur without meteorological ocean storms. In contrast, baseline modeling showed progressive succession towards the predominance of late-successional species, which was not the then-current state in the marsh. Conclusions: These findings support the hypothesis that variations in the NAO index toward its positive phase have increased storminess and wind tides on the North Sea surface (especially since the 1980s). This led to an increased frequency and duration of submergence and delayed ecological succession. Researchers should therefore employ a multitemporal perspective, recognizing the importance of short-term sea-level changes nested within long-term gradual trends.

Recovery of Ammonium Nitrogen and Phosphate from the Piggery Wastewater as Struvite and Its Assessment for the Reduction of Water Pollution Through the Field Test

  • Daeik Kim;Sun Jin Hwang;Su Ho Bae;Keon Sang Ryoo
    • 한국환경농학회지
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    • 제42권2호
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    • pp.83-92
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    • 2023
  • Excess N and P from the livestock manure applied to farmlands, have entered the water systems and poses a serious threat to the natural environment. Consequently, there has been recent awareness towards the management of livestock manure and its related fields. In this study, piggery wastewater was collected from a piggery in Pohang city, Korea. At 800℃, thermal decomposition of a natural stone, magnesite (MgCO3), yielded powered MgO with particle sizes ranging between 10 to 100 ㎛. Furthermore, NH4+-N and PO43--P were recovered as struvite precipitates from the piggery wastewater, by adjusting the pH with MgO and H3PO4. At pH 10, the recovery efficiencies of NH4+-N and PO43--P were found to be 86.1% and 94.1%, respectively. Using an X-ray Diffractometer (XRD), the struvite in the precipitate was confirmed to be consistent with standard pure struvite. Further, the purity of the struvite precipitate was analyzed using an energy dispersive X-ray (EDX) and thermal gravimetry-differential thermal analysis (TG-DTA), and found to be between 79.2% and 93.0%. Additionally, struvite-containing piggery wastewater and sawdust were mixed in a weight ratio of 2.5:1 and processed into a mature compost. The newly manufactured compost passed all quality standards required for first-class graded livestock composts. Moreover, this compost was sprayed directly onto the soil at the test site, and various parameters of the soil's effluent, such as total organic carbon (TOC), total nitrogen (T-N), total phosphorus (T-P), and dissolved oxygen (DO), were analyzed and measured. Based on these results, it is determined that the newly manufactured compost can more significantly reduce water pollution than commercial compost.