• 대한한의학방제학회지
• /
• 제28권2호
• /
• pp.179-187
• /
• 2020

Objectives : This study investigated the hepatoprotective effects effects of Jageum-jung extract on alcohol-induced liver disease mice model. Methods : Alcoholic liver disease was induced by Ethanol in C57/BL6 male mice, which were fed Lieber-DeCarli liquid diet containing ethanol. Jageum-jung (100,200 and 300 mg/kg bw/day) were orally administered daily in the alcoholic fatty liver disease mice for 16 days. Results : The results indicate that Jageum-jung promotes hepatoprotective effects by significantly reducing aspartate transaminase (AST) and alanine transaminase (ALT) levels as indicators of liver damage in the serum. Furthermore, Jageum-jung decreased accumulation of triglyceride and total cholesterol, increased levels of superoxide dismutase (SOD) and glutathione (GSH) in the serum of the alcoholic fatty liver disease mice model. Additionally, it improved the serum alcohol dehydrogenase (ADH) activity. Conclusions : This study confirmed the anti-oxidative and hangover elimination effects of Jageum-jung extract, and suggests the possibility of using Jageum-jung to treat alcholic liver disease.

• International Journal of Computer Science & Network Security
• /
• 제21권9호
• /
• pp.51-62
• /
• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• 응용통계연구
• /
• 제21권4호
• /
• pp.649-658
• /
• 2008

외식산업의 발달과 함께 학교와 기업 등의 단체 급식이 증가하고 있는 오늘날, 관련분야에서는 식중독 예방에 대해 많은 관심을 갖고 연구하고 있다. 우리나라에서는 온도에 따른 세균의 증식속도를 이용하여 식중독지수를 개발하여 식중독 위험에 대한 정보를 제공하고 있는데 그 정보가 식중독 발생상황과는 차이가 있음이 지적되고 있다. 본 연구에서는 최근 3년간($2004{\sim}2006$년)의 일별 식중독 발생과 기상자료를 이용하여 일 최고기온, 습도, 월효과를 설명변수로 하고, 식중독 발생건수를 반응변수로 하는 로그선형모형(Log Linear Model)을 이용하여 식중독 발생의 위험을 예보할 수 있는 사고발생지수를 개발하였다. 개발된 지수와 기존지수를 비교한 결과 개발된 지수가 식중독 발생 상황을 반영함에 있어서 더 나은 설명력이 있음이 나타났다.

• 보건교육건강증진학회지
• /
• 제30권5호
• /
• pp.139-151
• /
• 2013

Objectives: This study investigated how income, duration of illness, social stigma, quality of sleeping, ADL and social participation related to Parkinson's disease(PD) predict depression in a conceptual model based on the International Classification of Functioning(ICF) model. Methods: The sample included 206 adults with idiopathic Parkinson's disease(IPD) attending D university hospital in B Metro-politan City. A structured questionnaire was used and conducted face-to-face interviews. The collected data were analyzed for fitness, using the AMOS 18.0 program. Results: A path analysis showed that the overall model provided empirical evidence for linkages in the ICF model. Depression was manifested by significant direct effects of social stigma(${\beta}=.20$, p<.001), quality of sleeping(${\beta}=-.40$, p<.001), ADL(${\beta}=-.20$, p<.01), and social participation(${\beta}=-.12$, p<.05), indirect effects including income(p<.05), duration of illness(p<.05). These variables explained 45.9% of variance in the prediction model. Conclusions: This model may help nurses to collect and assess information to develop intervention program for depression.

• 한국응용곤충학회지
• /
• 제26권1호
• /
• pp.31-36
• /
• 1987

식물병(植物病) 진전곡선(進展曲線)을 간편하고 융통성있게 기술하는 절편(切片) 1차(次) 회귀(回歸)모델이 본(本) 연구(硏究)에서 제안(提案)되었다. 이 모델은 병진전상황(病進展狀況)을 그 진전형태(進展形態)에 따라 소수(少數)의 1차(次) 회귀식(回歸式)으로 나누고 지표변수(指標變數)를 사용(使用)하여 다시 한개로 묶어 작성(作成)된다. 포장시험(圃場試驗)에서 얻은 12개(個)의 실제병진전상황(實際病進展狀況)에 대(對)한 절편(切片) 1차(次) 회귀(回歸)모델의 통계적(統計的) 적합도(適合度)는 기존(旣存)의 두모델(Logistic모델과 Gompertz모델)에 비(比)하여 증진(增進)되었으며 이 모델이 가진 단순성(單純性), 융통성 및 모수예측(母數豫測)의 용이성(容易性)이 논의(論議)되였다. 그 결과(結果), 절편(切片) 1차(次) 회귀(回歸)모델은 식물병(植物病) 진전(進展)을 기술(記述)하는 한 통계적(統計的) 모델로써 유용(有用)하게 사용(使用)될 수 있으리라 생각된다.

• 지역사회간호학회지
• /
• 제30권4호
• /
• pp.494-507
• /
• 2019

Purpose: The purpose of this study is to provide a clear definition of the health literacy for patients with cardiovascular disease by analyzing the dimensions and properties using Hybrid concept analysis. Methods: The concept of health literacy of patients with cardiovascular disease was analyzed according to the cyclic process of theoretical phase-field work phase-final analysis phase presented in the Hybrid model. We reviewed 26 literatures and conducted in-depth interviews with 13 patients with cardiovascular disease. Results: The concept of health literacy in cardiovascular patients is derived from two dimensions and five attributes. Literacy skills, health information search ability and health information utilization skills were derived as attributes in the individual functional dimension, while active communication with the medical team and utilization of health information support resources were derived at the interrelational dimension. It is defined as the individualized and integrated ability of an individual to explore and utilize the various health information needed to make appropriate health decisions during the chronic course after diagnosis of cardiovascular disease, to communicate proactively with medical staffs and to utilize support resources. Conclusion: This study will contribute to the development and related research of health literacy measurement tools that can be used in cardiovascular nursing practice based on the attributes and indicators of health literacy for patients with cardiovascular disease.

• 한국직업건강간호학회지
• /
• 제15권1호
• /
• pp.50-57
• /
• 2006

Purpose: The purpose of this study was to research the health belief, perception and need of prevention program for musculoskeletal disease of office workers in a public corporation. Method: We surveyed 339 office workers at a industry based in Ahn Yang, Kyunggi Province, with questionnaires, during the period June 3rd - June 18th, 2004. Result: Forty-four percent of the subjects said they had musculoskeletal symptoms, and 10.9% said they had received medical treatment for musculoskeletal disease in the last year. Factors that affected perception of musculoskeletal disease were appeared to be perceived severity, perceived barrier, cue to action, marital status, regular exercise and age, and they explained 23.2% of perception of musculoskeletal disease. Factors that affected need of prevention program appeared to be perceived susceptibility, perceived severity, perceived benefit and PC using hours, and they explained 20.8% of need of prevention program. Conclusion: In conclusion, we suggested that in management the prevention of musculoskeletal disease for office workers, it should be considered nursing intervention strategies to reinforce health belief.

• 보건교육건강증진학회지
• /
• 제26권4호
• /
• pp.117-127
• /
• 2009

Objectives: The primary objective of the study is to analyze the utilization patterns of provincial patients discharged from hospitals located in Seoul area. Methods: For the analysis, the study employed the nationwide data on 'Survey of Injured Patients Discharged from Hospitals' conducted by KCDC (Korea Centers for Disease Control and Prevention). The statistical methodology used in the measurement model is a logistic regression model. Results: The study has three major findings. First, compared to other disease groups, the discharged on both 'neoplasm(cancer)' and 'congenital malformation, deformity and chromosomal abnormalities' disease groups are more likely to utilize hospitals in Seoul area. Second, as for 'neoplasm(cancer)' disease group, patients with 'bones and articular cartilage' areas are more likely to utilize hospitals in Seoul area. Finally, Hospitals with more than 1,000 beds was primary factor in selecting Seoul-based hospitals by the discharged in provincial areas. Conclusion: In sum, the study showed that patients in provincial areas are more likely to utilize hospitals located in Seoul area regardless of the severity of their cases. Local authority, therefore, is required to monitor local hospitals on regular basis, as well as support them to establish specialized medical centers by providing human and physical resources.

• 한국멀티미디어학회논문지
• /
• 제25권11호
• /
• pp.1653-1671
• /
• 2022

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.

• Journal of the Korean Data and Information Science Society
• /
• 제19권1호
• /
• pp.37-51
• /
• 2008

In this article, the aim is at evaluating the knowledge level of the foodbome disease by developing a reasonable survey questionnaire model. Each questions of the questionnaire is made up to check the knowledge covering the several fields of materials related to the foodbome disease. The pilot survey is implemented to evaluate the validity of questionnaire. Each question in questionnaire is scored to get the quantitative measure of the foodbome disease knowledge by converting the total score into 100 points.