• Animal Bioscience
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• 제34권4호
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• Journal of Ecology and Environment
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• 제45권4호
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• pp.313-327
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• 2021

Background: Citizen science is becoming a mainstream approach of baseline data collection to monitor biodiversity and climate change. Dragonflies (Odonata) have been ranked as the highest priority group in biodiversity monitoring for global warming. Ischnura senegalensis Rambur has been designated a biological indicator of climate change and is being monitored by the citizen science project "Korean Biodiversity Observation Network." This study has been performed to understand changes in the distribution range of I. senegalensis in response to climate change using citizen science data in South Korea. Results: We constructed a dataset of 397 distribution records for I. senegalensis, ranging from 1980 to 2020. The number of records sharply increased over time and space, and in particular, citizen science monitoring data accounted for the greatest proportion (58.7%) and covered the widest geographical range. This species was only distributed in the southern provinces until 2010 but was recorded in the higher latitudes such as Gangwon-do, Incheon, Seoul, and Gyeonggi-do (max. Paju-si, 37.70° latitude) by 2020. A species distribution model showed that the annual mean temperature (Bio1; 63.2%) and the maximum temperature of the warmest month (Bio5; 16.7%) were the most critical factors influencing its distribution. Future climate change scenarios have predicted an increase in suitable habitats for this species. Conclusions: This study is the first to show the northward expansion in the distribution range of I. senegalensis in response to climate warming in South Korea over the past 40 years. In particular, citizen science was crucial in supplying critical baseline data to detect the distribution change toward higher latitudes. Our results provide new insights on the value of citizen science as a tool for detecting the impact of climate change on ecosystems in South Korea.

• Animal Bioscience
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• 제36권5호
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• pp.692-703
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• 2023

Objective: The main goals of this investigation were to i) assess the population structure and genetic diversity and ii) determine the efficiency of the ongoing breeding program in a closed flock of Angora rabbits through pedigree analysis. Methods: The pedigree records of 6,145 animals, born between 1996 to 2020 at NTRS, ICAR-CSWRI, Garsa were analyzed using ENDOG version 4.8 software package. The genealogical information, genetic conservation index and parameters based on gene origin probabilities were estimated. Results: Analysis revealed that, 99.09% of the kits had both parents recorded in the whole dataset. The completeness levels for the whole pedigree were 99.12%, 97.12%, 90.66%, 82.49%, and 74.11% for the 1st, 2nd, 3rd, 4th, and 5th generations, respectively, reflecting well-maintained pedigree records. The maximum inbreeding, average inbreeding and relatedness were 36.96%, 8.07%, and 15.82%, respectively. The mean maximum, mean equivalent and mean completed generations were 10.28, 7.91, and 5.51 with 0.85%, 1.19%, and 1.85% increase in inbreeding, respectively. The effective population size estimated from maximum, equivalent and complete generations were 58.50, 27.05, and 42.08, respectively. Only 1.51% of total mating was highly inbred. The effective population size computed via the individual increase in inbreeding was 42.83. The effective numbers of founders (f_e), ancestors (f_a), founder genomes (f_g) and non-founder genomes (f_ng) were 18, 16, 6.22, and 9.50, respectively. The fe/fa ratio was 1.12, indicating occasional bottlenecks had occurred in the population. The six most influential ancestors explained 50% of genes contributed to the gene pool. The average generation interval was 1.51 years and was longer for the sire-offspring pathway. The population lost 8% genetic diversity over time, however, considerable genetic variability still existed in the closed Angora population. Conclusion: This study provides important and practical insights to manage and maintain the genetic variability within the individual flock and the entire population.

• The Korean Journal of Pain
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• 제37권1호
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• pp.41-50
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• 2024

Background: Recognizing the seriousness of the misuse and abuse of medical narcotics, the South Korean government introduced the world's first narcotic management system, the Narcotics Information Management System (NIMS). This study aimed to explore the recent one-year opioid prescribing patterns in South Korea using the NIMS database. Methods: This study analyzed opioid prescription records in South Korea for the year 2022, utilizing the dispensing/administration dataset provided by NIMS. Public data from the Korean Statistical Information Service were also utilized to explore prescription trends over the past four years. The examination covered 16 different opioid analgesics, assessed by the total number of units prescribed based on routes of administration, type of institutions, and patients' sex and age group. Additionally, the disposal rate for each ingredient was computed. Results: In total, 206,941 records of 87,792,968 opioid analgesic units were analyzed. Recently, the overall quantity of prescribed opioid analgesic units has remained relatively stable. The most prescribed ingredient was oral oxycodone, followed by tapentadol and sublingual fentanyl. Tertiary hospitals had the highest number of dispensed units (49.4%), followed by community pharmacies (40.2%). The highest number of prescribed units was attributed to male patients in their 60s. The disposal rates of the oral and transdermal formulations were less than 0.1%. Conclusions: Opioid prescription in South Korea features a high proportion of oral formulations, tertiary hospital administration, pharmacy dispensing, and elderly patients. Sustained education and surveillance of patients and healthcare providers is required.

• 한국경영과학회지
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• 제39권3호
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• pp.63-82
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• 2014

The main objective of this research is to investigate whether the RFM (recency-frequency-monetary value) information of a customer's redemption behavior of loyalty points can improve the prediction of future value of the customer. The conventional measurement of customer value has been primarily based on purchase transactions behavior although a customer's future behavior can be also influenced by other interactions between the customer and the firm such as redemption of rewards in a loyalty program. We theorize why a customer's redemption behavior can influence her future purchases and thereby the customer's total value based on operant learning theory, goal gradient hypothesis, and lock-in effect. Using a dataset from a major book store in Korea spanning three years between 2008 and 2010, we analyze both purchase transactions and redemption records of over 10,000 customers. The results show that the redemption-based RFM information does improve the prediction accuracy of the customer's future purchases. Based on this result, we also propose an improved estimate of customer lifetime value (CLV) by combining purchase transactions and loyalty points redemption data. Managerial implications will be also discussed for firms managing loyalty programs to maximize the total value customers.

• 대기
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• 제16권3호
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• pp.215-223
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• 2006

Among observational, local-environmental, and large-scale factors causing significant changes in climate records, the site relocations and the replacement of the instruments are well-known nonclimatic factors for the analysis of climatic trends, climatic variability, and for the detection of anthropogenic climate change such as heat-island effect and global warming. Using dataset that were contaminated by these nonclimatic factors can affect seriously the assessment of climatic trends and variability, and the detection of the climatic change signal. In this paper, the inhomogeneities, which have been caused by relocation of the observation site, in the climate data of Korea Meteorological Administration (KMA) were examined using two-phase regression model. The observations of pan evaporation and wind speed are more sensitive to site relocations than those of other meteorological elements, such as daily mean, maximum and minimum temperatures, with regardless to region.

• 한국방사선학회논문지
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• 제10권8호
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• pp.637-643
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• 2016

최근 인공지능에 대중의 관심으로 인해, 인공신경망을 사용한 의료영상 처리가 학계와 산업계에서 관심이 커져가고 있다. 딥러닝을 이용한 컨볼루션 신경망은 영상을 효과적으로 표현할 수 있는 것으로 증명되었다. 그러나 학습을 위해서는 고성능 H/W 플랫폼이 요구된다. 따라서 고차원의 많은 학습 샘플을 저사양 H/W 플랫폼에서 학습하는 것은 매우 도전적인 문제이다. 본 논문에서는 온라인 인공 신경망을 사용해 라즈베리파이에서 동작할 수 있는 실시간 신경망 알고리즘을 제안하고자 한다. 다양한 실험 결과를 통해 제안된 방법은 실시간 학습이 가능함을 보여주었다.

• 한국정보통신학회논문지
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• 제18권11호
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• pp.2691-2696
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• 2014

SNOMED CT는 전자의무기록(EMR) 및 전자건강기록(EHR) 시스템에서 표준화된 용어를 사용하여 진로기록을 작성하고 관리하기 위한 표준 의학용어 체계이다. 이 용어체계는 용어의 방대함 및 설계 구조로 인해 용어 체계가 매우 복잡한 특징이 있다. SNOMED CT에서 제공하는 의학 용어를 검색하기 위해 진료 과정에서 사용하는 기존의 브라우저들은 용어체계의 복잡성을 반영하지 못하여 진료기록의 작성 단계에서 그 효용성이 떨어지는 문제가 있다. 본 연구에서는 SNOMED CT 브라우저에 내재된 문제점을 제시하고 용어체계의 분석을 통해 의학 용어를 빠르고 효율적으로 검색하기 위한 검색 브라우저의 요건을 분석하고 개선안을 제시한다.

• Earthquakes and Structures
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• 제15권6호
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• pp.605-616
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• 2018

In this paper, an extended Cloud analysis method is developed for seismic fragility assessment of existing highway bridges in the southeast Queensland region. This method extends the original Cloud analysis dataset by performing scaled Cloud analyses. The original and scaled Cloud datasets are then paired to generate seismic fragility curves. The seismic hazard in this region is critically reviewed, and the ground motion records are selected for the time-history analysis based on various record selection criteria. A parametric highway bridge model is developed in the OpenSees analysis software, and a sampling technique is employed to quantify the uncertainties of highway bridges ubiquitous in this region. Technical recommendations are also given for the seismic performance evaluation of highway bridges in such low-to-moderate seismic zones. Finally, a probabilistic fragility study is conducted by performing a total of 8000 time-history analyses and representative bridge fragility curves are generated. It is illustrated that the seismic fragility curves generated by the proposed extended Cloud analysis method are in close agreement with those which are obtained by the rigorous incremental dynamic analysis method. Also, it reveals that more than 50% of highway bridges existing in southeast Queensland will be damaged subject to a peak ground acceleration of 0.14 g.

• 문화기술의 융합
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• 제7권1호
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• pp.582-587
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• 2021

도서 취향을 고려하여 도서를 추천해주는 도서 추천 시스템은 사용자의 독서 경험과 독서에 대한 인식 개선에 효과적이다. 축적된 사용자 평점 기록이 상대적으로 적은 도서의 경우 추천 정확도에 한계가 나타난다. 본 연구에서는 상대적으로 풍부한 사용자 평점 데이터를 가진 영화 평점 정보를 이용하여 사용자에게 맞춤형 도서를 추천하는 추천 시스템을 제안한다. 제안하는 방법을 통해 도서 추천의 정확도를 높이고 보다 다양한 종류의 추천을 수행하는데 효과적임을 보였다. 영화 평점 데이터를 활용한 도서추천 시스템은 도서 분야 외 타 미디어 플랫폼의 데이터를 도서추천에 활용하는 의미 있는 시도가 될 것으로 예상한다.