• Archives of Plastic Surgery
• /
• 제48권4호
• /
• pp.361-365
• /
• 2021

Background Pain in the postoperative body contouring patient has traditionally been managed with narcotic medication. In an effort to minimize side effects and prevent addiction, plastic surgeons are searching for novel ways to provide adequate analgesia, one of which is nerve blocks. This study was conducted with a meta-analysis that evaluates the efficacy of these blocks for patients who undergo breast surgery. Methods A search of the PubMed/MEDLINE database for articles including the terms "post-operative analgesia" OR "postoperative pain management" AND "in plastic surgery" OR "in cosmetic surgery" OR "in elective surgery" in February 2019 generated five studies on elective breast augmentation and reduction mammoplasty that reported pain scores and quantities of opioids consumed. Independent samples t-tests, one-way analysis of variance, and a random effects model were implemented for evaluation. Results A total of 317 patients were identified as having undergone body contouring of the breast, about half of which received a nerve block. Pain scores on a 1-10 scale and opioid dose-equivalents were calculated. Those who were blocked had an average score of 2.40 compared to 3.64 for those who did not (P<0.001), and required an average of 5.20 less narcotic doses (P<0.001). Pain relief following subpectoral augmentation was best achieved with type-II blocks as opposed to type-I and type-II with serratus plane (P<0.001). Conclusions The opioid epidemic has extended to all surgical specialties. Implementation of a nerve block seems to be an efficacious and cost-effective mechanism to not only help with post-operative pain, but also lower the need for narcotics, especially in subpectoral augmentation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권2호
• /
• pp.381-404
• /
• 2022

Forest fires inflict great losses of human lives and serious damages to ecological systems. Hence, numerous fire detection methods have been proposed, one of which is fire detection based on sensors. However, these methods reveal several limitations when applied in large spaces like forests such as high cost, high level of false alarm, limited battery capacity, and other problems. In this research, we propose a novel forest fire detection method based on image processing and correlation coefficient. Firstly, two fire detection conditions are applied in RGB color space to distinguish between fire pixels and the background. Secondly, the image is converted from RGB to YCbCr color space with two fire detection conditions being applied in this color space. Finally, the correlation coefficient is used to distinguish between fires and objects with fire-like colors. Our proposed algorithm is tested and evaluated on eleven fire and non-fire videos collected from the internet and achieves up to 95.87% and 97.89% of F-score and accuracy respectively in performance evaluation.

• 한국건설관리학회논문집
• /
• 제23권5호
• /
• pp.34-42
• /
• 2022

건설현장배치는 프로젝트 공기와 비용에 큰 영향을 미치므로 이에 대한 최적화 및 자동화 연구가 수행되고 있다. 하지만, 이러한 연구의 목표변수인 건설현장배치의 수준을 평가하는 근거는 매우 제한적이다. 본 연구는 사용성평가 방법을 활용하여 건설현장배치의 수준을 평가하고 향후 건설현장배치 자동화 및 최적화 연구에서 초점을 맞추어야 하는 부분을 확인하고자 하였다. 설문결과 국내건설현장배치 사용성은 효과와 작업환경 항목에서 낮은 점수를 받았으며, 작업자가 관리자에 비해 낮게 평가하였다. 또한 작업환경 항목에서 낮은 점수를 받았으며 현장접근성과 시설 편의성이 낮음을 확인하였다. 이 연구는 건설현장배치 현황을 파악하고 개선방향을 제시함으로써 건설현장배치 자동화 및 최적화 연구의 진행을 위한 지식에 기여한다.

• 한국컴퓨터정보학회논문지
• /
• 제27권11호
• /
• pp.131-138
• /
• 2022

Git의 커밋 메시지는 프로젝트 생명주기와 밀접한 연관성을 지니고 있으며, 이러한 특성에 의해 프로젝트 운영 활동의 위험요소와 프로젝트 현황 등을 파악하여 비용 절감과 작업효율 개선 등에 큰 기여를 할 수 있다. 이와 관련한 분야 중 커밋 메시지를 소프트웨어 유지관리의 유형으로 분류하는 많은 연구가 있으며 연구 중 최대 정확도는 87%다. 본 논문에서는 커밋 분류 모델을 이용한 솔루션 등의 활용을 목적으로 진행 하였고 기존에 발표된 모델들보다 정확도를 높여 모델의 신뢰성을 높이기 위해 여러 모델을 조합한 복합 분류 모델을 설계하고 구현하였다. 본문은 자동화 레이블링 및 소스 변경 내용을 추출하여 데이터셋을 구성하고 디스틸 버트(DistilBERT) 모델을 이용하여 학습시켰다. 검증결과 기존 연구에서 보고된 최대 87%보다 8%가 향상된 95%의 F1 점수 값을 얻어 신뢰성을 확보하였다. 본 연구 결과를 이용하면 모델의 신뢰성을 높이고 이를 이용해 소프트웨어 및 프로젝트관리 등의 솔루션에 적용이 가능할 것으로 기대된다.

• Korean Journal of Acupuncture
• /
• 제40권4호
• /
• pp.194-205
• /
• 2023

Objectives : This study investigated the current utilization status of thermotherapy devices in Korean medicine (KM) institutions and identified areas for improvement and further development, as perceived by KM doctors (KMDs). Methods : An online survey was conducted, targeting KMDs primarily engaged in clinical patient care. The questionnaire included items about respondents' clinical practices, the extent of thermotherapy device usage, their opinions on these devices, and perceived improvement needs. The collected data underwent quantitative analysis. Results : From the 1,025 respondents, data from 862 respondents who provided complete responses were analyzed. On average, respondents utilized thermotherapy treatments for 80% of their patients. Infrared (IR) phototherapy unit, electrical moxibustion apparatus, and heater-based thermotherapy devices were predominantly owned by respondents, with IR being the most frequently used. The average satisfaction score for current thermotherapy devices was 79. A significant concern raised was the necessity for improved health insurance coverage and efficacy evaluation. Conclusions : This research has confirmed that the extensive use of thermotherapy devices by KMDs in treating primarily musculoskeletal and gastrointestinal ailments - common conditions among patients in KM institutions. The main areas identified for improvement encompass safety, cost-effectiveness, and device efficacy. Future enhancements in thermotherapy devices should address these crucial aspects.

• 한국간담췌외과학회지
• /
• 제27권1호
• /
• pp.56-62
• /
• 2023

Backgrounds/Aims: Endoscopic retrograde cholangiopancreatography-guided gallbladder drainage (ERGD) is an alternative to percutaneous cholecystostomy (PTC) for hospitalized acute cholecystitis (AC) patients. Methods: We retrospectively analyzed propensity score matched (PSM) AC hospitalizations using the National Inpatient Sample database between 2016 and 2019 to compare the outcomes of ERGD and PTC. Results: After PSM, there were 3,360 AC hospitalizations, with 48.8% undergoing PTC and 51.2% undergoing ERGD. There was no difference in median length of stay between the PTC and ERGD cohorts (p = 0.110). There was a higher median hospitalization cost in the ERGD cohort, $62,562 (interquartile range [IQR] $40,707-97,978) compared to PTC, $40,413 (IQR $25,244-65,608; p < 0.001). The 30-day inpatient mortality was significantly lower in hospitalizations with ERGD compared to PTC (adjusted hazard ratio 0.16, 95% confidence interval [CI]: 0.1-0.41; p < 0.001). There was no difference in association with blood transfusions, acute renal failure, ileus, small bowel obstruction, and open cholecystectomy conversion (p > 0.05) between hospitalizations with ERGD and PTC. There was lower association of acute hypoxic respiratory failure (adjusted ratio [AOR] 0.46, 95% CI: 0.29-0.72; p = 0.001), hypovolemia (AOR 0.66, 95% CI: 0.49-0.82; p = 0.009) and higher association of lower gastrointestinal bleed (AOR 1.94, 95% CI: 1.48-2.54; p < 0.001) with ERGD compared to PTC. Conclusions: ERGD is a safer alternative to PTC in patients with AC. The risk complications are lower in ERGD compared to PTC but no difference exists based on mortality or conversion to open cholecystectomy.

• Clinical Nutrition Research
• /
• 제12권2호
• /
• pp.138-153
• /
• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• 한국농공학회논문집
• /
• 제66권4호
• /
• pp.27-39
• /
• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• 대한지역사회영양학회지
• /
• 제10권5호
• /
• pp.654-667
• /
• 2005

To survey the different view points about food service programs among parents and teachers, 2 types of questionnaires, which consisted of attitude, perception, satisfaction and demand of the food service program in child education centers, were used. The data was collected from 2450 parents and 450 teachers who attended a child education center in 16 provinces, nationwide. SPSS was used for descriptive analysis and ANOVA test and $X^2-test$. The frinding results were as follows. 1. The average serving size of meal (lunch) were 80 meals per day and 167 meals per day at large institutions. Mean cost of snacks was 14,709 won per month and mean costs of lunch were 29,319 won per month. The mean price was not significantly different according to the scale of institution. The numbers of servings of lunch, morning snack and afternoon snack were 5, 3.4 and 3.5 times per week each. $56.4\%$ of the institutions served meals to children in classrooms, but the national/public institutions, which were attending elementary school, served meals in a dining place in the elementary school. 2. Teacher controlled serving portion size of snacks $(79.6\%)$ and lunch $(88.8\%)\;and\;30.1\%$ of teacher did not allow leaving lunch food. The ratio of knowing about preserved meals of the teacher who worked at a small institution was significantly higher than the teacher who worked at large institutions (p<0.01). 3. Between parents and teachers, several different view points about school lunch programs were detected. Most parents and teachers wanted that the school lunch to be fully cooked and served at the child education institution itself, but $12.2\%$ of parents and $14.4\%$ of teachers wanted a catering service. The teachers group preferred 'lunch box from home' and 'home partially prepared lunch' as an ideal meal serving type than the parent groups (p<0.01). And there were significantly different view points about price factors in school meals, teachers group highly answered that operating expenses must be added in meal prices. 4. The teacher groups' priorities of education activities during meal time were a significantly lower score than parents group in overall education activities. Teacher and parent groups pointed out that individual sanitation activities were most important of the education activities during meal time, but promoting good eating habits was the lowest score in both groups. 5. 'Improving taste and food quality' was most urgent in food service at child education centers, but there were significantly different view points between parent groups $(64.5\%)$ and teacher groups $(43.8\%)\;(p<0.05)$. They answered at a lower percent in 'employee qualified person' and 'cost control' point to improve food service, but there were also different opinions between the two groups (p<0.01). 6. As to the matter of the advantages and disadvantages of catering services, two group answered that the advantages of a catering service were 'convenience' and 'to solve facilities and labor problems', disadvantages were 'lower in food freshness' and 'sanitation problems'. There were also several different view pionts in catering services, the parents groups were more anxious about food sanitation than teachers. This study found several different view points about school food services among parents and teachers. To improve food services at child education institutions, there is a need to adjust the differences between the two groups through interactive communication channels and education and to employ dietitians as taking charge of adjusting roles between the two groups.

• Agribusiness and Information Management
• /
• 제8권1호
• /
• pp.17-26
• /
• 2016

India is largest producer of banana in the world producing 29.72 million tonnes from an area of 0.803 million ha with a productivity of 35.7 MT ha-1 and accounted for 15.48 and 27.01 per cent of the world's area and production respectively (www.nhb.gov.in). In India, Tamil Nadu leads other states both in terms of area and production followed by Maharashtra, Gujarat and Andhra Pradesh. In Rayalaseema region of Andhra Pradesh, Kurnool district had special reputation in the cultivation of banana in an area of 5765 hectares with an annual production of 2.01 lakh tonnes in the year 2012-13 and hence, it was purposively chosen for the study. On $23^{rd}$ November 2003, the Government of Andhra Pradesh has commenced a comprehensive project called 'Andhra Pradesh Micro Irrigation Project (APMIP)', first of its kind in the world so as to promote water use efficiency. APMIP is offering 100 per cent of subsidy in case of SC, ST and 90 per cent in case of other categories of farmers up to 5.0 acres of land. In case of acreage between 5-10 acres, 70 per cent subsidy and acreage above 10, 50 per cent of subsidy is given to the farmer beneficiaries. The sampling frame consists of Kurnool district, two mandals, four villages and 180 sample farmers comprising of 60 farmers each from Marginal (<1ha), Small (1-2ha) and Other (>2ha) categories. A well structured pre-tested schedule was employed to collect the requisite information pertaining to the performance of drip irrigation among the sample farmers and Data Envelopment Analysis (DEA) model was employed to analyze the performance of drip irrigation in banana farms. The performance of drip irrigation was assessed based on the parameters like: Land Development Works (LDW), Fertigation costs (FC), Volume of water supplied (VWS), Annual maintenance costs of drip irrigation (AMC), Economic Status of the farmer (ES), Crop Productivity (CP) etc. The first four parameters are considered as inputs and last two as outputs for DEA modelling purposes. The findings revealed that, the number of farms operating at CRS are more in number in other farms (46.66%) followed by marginal (45%) and small farms (28.33%). Similarly, regarding the number of farmers operating at VRS, the other farms are again more in number with 61.66 per cent followed by marginal (53.33%) and small farms (35%). With reference to scale efficiency, marginal farms dominate the scenario with 57 per cent followed by others (55%) and small farms (50%). At pooled level, 26.11 per cent of the farms are being operated at CRS with an average technical efficiency score of 0.6138 i.e., 47 out of 180 farms. Nearly 40 per cent of the farmers at pooled level are being operated at VRS with an average technical efficiency score of 0.7241. As regards to scale efficiency, nearly 52 per cent of the farmers (94 out of 180 farmers) at pooled level, either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Majority of the farms (39.44%) are operating at IRS and only 29 per cent of the farmers are operating at DRS. This signifies that, more resources should be provided to these farms operating at IRS and the same should be decreased towards the farms operating at DRS. Nearly 32 per cent of the farms are operating at CRS indicating efficient utilization of resources. Log linear regression model was used to analyze the major determinants of input use efficiency in banana farms. The input variables considered under DEA model were again considered as influential factors for the CRS obtained for the three categories of farmers. Volume of water supplied ($X_1$) and fertigation cost ($X_2$) are the major determinants of banana farms across all the farmer categories and even at pooled level. In view of their positive influence on the CRS, it is essential to strengthen modern irrigation infrastructure like drip irrigation and offer more fertilizer subsidies to the farmer to enhance the crop production on cost-effective basis in Kurnool district of Andhra Pradesh, India. This study further suggests that, the present era of Information Technology will help the irrigation management in the context of generating new techniques, extension, adoption and information. It will also guide the farmers in irrigation scheduling and quantifying the irrigation water requirements in accordance with the water availability in a particular season. So, it is high time for the Government of India to pay adequate attention towards the applications of 'Information and Communication Technology (ICT) and its applications in irrigation water management' for facilitating the deployment of Decision Supports Systems (DSSs) at various levels of planning and management of water resources in the country.