• Title/Summary/Keyword: Threat score

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Relationship between Cognitive Appraisal and Cardiac Risk Reduction Behavior Following Coronary Angioplasty (PTCA 시술 환자의 인지적 평가와 위험요인수정행위)

  • Hahn, Sook-Won;Lee, Myung-Sun
    • Korean Journal of Adult Nursing
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    • v.16 no.4
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    • pp.556-565
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    • 2004
  • Purpose: According to Lazarus & Folkman (1984), appraising a stressor as a threat is associated with negative psychological and physical adjustment, whereas appraising a stressor as a challenge is positive psychological and physical adjustment. This study examined how cognitive appraisal of PTCA(heart disease threat and treatment appraisal) related to the cardiac risk reduction behaviors(smoking cessation, low salt and low cholesterol diet, regular exercise and stress management) 6 weeks following discharge. Method: Data were collected from 50 subjects with successful primary PTCA. Result: Heart disease threat was negative related to treatment appraisal (r=-0.240, p=0.046). Psychological well-being was negative related to heart disease threat (r=-0.317, p=0.012) and positive related to treatment appraisal(r=0.402, p=0.002). The cardiac risk reduction behaviors score was negative related to heart disease threat(r= -0.296, p=0.018) and positive related to treatment appraisal(r=-0.291, p=0.020). Conclusion: More negative appraisal was related to lower the cardiac risk reduction behaviors score. But more positive appraisal was related to higher the cardiac risk reduction behaviors score. So, there is a need to develop the cognitive-behavioral intevention that increase the coping strategy to replace with positive appraisal.

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Evaluation of the Combat Aircraft Susceptibility Against Surface-Based Threat Using the Weighted Score Algorithm

  • Kim, Joo-Young;Kim, Jin-Young;Lee, Kyung-Tae
    • International Journal of Aeronautical and Space Sciences
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    • v.12 no.4
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    • pp.396-402
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    • 2011
  • Aircraft combat survivability is an essential factor in the design of combat aircrafts that operate in an enemy air defense area. The combat aircrafts will be confronted with anti-aircraft artillery and/or surface-to-air missiles (SAM) from the ground, and their survivability can be divided into two categories: susceptibility and vulnerability. This article studies the prediction of susceptibility in the case of a one-on-one engagement between the combat aircraft and a surface-based threat. The weighted score method is suggested for the prediction of susceptibility parameters, and Monte Carlo simulations are carried out to draw qualitative interpretation of the susceptibility characteristics of combat aircraft systems, such as the F-16 C/D, and the hypersonic aircraft, which is under development in the United States, versus ground threat from the SAM SA-10.

Fuzzy Rule-Based Method for Air Threat Evaluation (적기의 위협 평가 자동화를 위한 퍼지 규칙 방법론)

  • Choi, Byeong Ju;Kim, Ji Eun;Kim, Jin Soo;Kim, Chang Ouk
    • Journal of the Korea Institute of Military Science and Technology
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    • v.19 no.1
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    • pp.57-65
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    • 2016
  • Threat evaluation is a process to estimate the threat score which enemy aerial threat poses to defended assets. The objective of threat evaluation is concerned with making an engagement priority list for optimal weapon allocation. Traditionally, the threat evaluation of massive air threats has been carried out by air defence experts, but the human decision making is less effective in real aerial attack situations with massive enemy fighters. Therefore, automation to enhance the speed and efficiency of the human operation is required. The automatic threat evaluation by air defense experts who will perform multi-variable judgment needs formal models to accurately quantify their linguistic evaluation of threat level. In this paper we propose a threat evaluation model by using a fuzzy rule-based inference method. Fuzzy inference is an appropriate method for quantifying threat level and integrating various threat attribute information. The performance of the model has been tested with a simulation that reflected real air threat situation and it has been verified that the proposed model was better than two conventional threat evaluation models.

Retrieval of Rain-Rate Using the Advanced Microwave Sounding Unit(AMSU)

  • Byon, Jae-Young;Ahn, Myoung-Hwan;Sohn, Eun-Ha;Nam, Jae-Cheol
    • Proceedings of the KSRS Conference
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    • 2002.10a
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    • pp.361-365
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    • 2002
  • Rain-rate retrieval using the NOAA/AMSU (Advanced Microwave Sounding Unit) (Zaho et al., 2001) has been implemented at METRI/KMA since 2001. Here, we present the results of the AMSU derived rain-rate and validation result, especially for the rainfall associated with the tropical cyclone for 2001. For the validation, we use rain-rate derived from the ground based radar and/or rainfall observation from the rain gauge in Korea. We estimate the bias score, threat score, bias, RMSE and correlation coefficient for total of 16 tropical cyclone cases. Bias score shows around 1.3 and it increases with the increasing threshold value of rain-rate, while the threat score extends from 0.4 to 0.6 with the increasing threshold value of precipitation. The averaged rain-rate for at all 16 cases is 3.96mm/hr and 1.41mm/hr for the retrieved from AMSU and the ground observation, respectively. On the other hand, AMSU rain-rate shows a much better agreement with the ground based observation over inner part of tropical cyclone than over the outer part (Correlation coefficient for convective region is about 0.7, while it is only about 0.3 over the stratiform region). The larger discrepancy of tile correlation coefficient with the different part of the tropical cyclone is partly due to the time difference in between ice water path and surface rainfall. This results indicates that it might be better to develop the algorithm for different rain classes such as convective and stratiform.

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The Effect of Violent Experience on Burnout among Some Dental Hygienists

  • Jeon, Eun-Jeong;Han, Mi Ah;Park, Jong;Choi, Seong Woo
    • Journal of dental hygiene science
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    • v.17 no.5
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    • pp.413-422
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    • 2017
  • This study investigated the effect of violent experience on burnout among some dental hygienists. The study subjects were 242 dental hygienists working at dental clinics. Data were collected by self-administered questionnaire including information such as demographics, work-related characteristics, working environment, experience of violence, and burnout. The violence was classified as verbal violence, physical threat, or physical violence committed by dentists, patients, or caregivers. Descriptive analysis, t-test, ANOVA, correlation, and multiple linear regression analysis were performed to examine the factors associated with burnout. The levels of verbal violence, physical threat, and physical violence by dentists were $0.53{\pm}1.26$, $1.12{\pm}2.70$, and $0.04{\pm}0.42$, respectively. The levels of verbal violence, physical threat, and physical violence by patients and caregivers were $1.50{\pm}1.89$, $1.41{\pm}2.24$, and $0.24{\pm}1.38$, respectively. The score of burnout was $3.13{\pm}0.43$. Total violence, verbal violence, and physical violence by dentists were positively correlated with burnout. Total violence, verbal violence, and physical threat by patients and caregivers were positively correlated with burnout. In multiple linear regression analysis, the level of physical violence by dentists was positively associated with burnout of dental hygienists (${\beta}=0.95$, p=0.032). The levels of total physical violence (${\beta}=0.28$, p=0.002), verbal violence (${\beta}=0.15$, p<0.001), and physical threat (${\beta}=0.19$, p=0.009) by the patients or caregivers were positively associated with burnout of dental hygienists. This study examined the association between violence and burnout among dental hygienists. The level of violence showed positive correlation with burnout. Environment improvement to protect employees from violence and for management of employees who experienced workplace violence are needed to reduce the burnout.

Nurses' Calling, Perceived Risk, Performance on Standard Precautions, and Burnout in the COVID-19 Pandemic (COVID-19 팬데믹 상황에서 간호사의 소명의식, 지각된 위험, 표준주의지침 수행 및 소진)

  • Hyun Jeong;Younghye Go;Mihyun Lee;Miri Jeong
    • Journal of Industrial Convergence
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    • v.21 no.3
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    • pp.65-74
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    • 2023
  • This study aimed to identify the effect of occupational calling, compliance with standard precautions and perceived threat for COVID-19 on the COVID-19 burnout of hospital nurses in the convergence society. The participants were 212 nurses; data were analyzed using t-test, ANOVA, Pearson correlation, multiple regression. This study found that: hospital nurses showed higher score for perceived threat of COVID-19, higher scores for COVID-19 burnout. The main factors influencing COVID-19 burnout were perceived threat of COVID-19 (𝛽=.233), and working unit (𝛽=.154). They explained about 6.7% of the COVID-19 burnout. Therefore, systematic support and nursing education is needed to reduce the perceived threat of COVID-19 among nurses.

Improving Probability of Precipitation of Meso-scale NWP Using Precipitable Water and Artificial Neural Network (가강수량과 인공신경망을 이용한 중규모수치예보의 강수확률예측 개선기법)

  • Kang, Boo-Sik;Lee, Bong-Ki
    • Proceedings of the Korea Water Resources Association Conference
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    • 2008.05a
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    • pp.1027-1031
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    • 2008
  • 본 연구는 한반도 영역을 대상으로 2001년 7, 8월과 2002년 6월로 홍수기를 대상으로 RDAPS 모형, AWS, 상층기상관측(upper-air sounding)의 자료를 이용하였다. 또한 수치예보자료를 범주적 예측확률로 변환하고 인공신경망기법(ANN)을 이용하여 강수발생확률의 예측정확성을 향상시키는데 있다. 신경망의 예측인자로 사용된 대기변수는 500/ 750/ 1000hpa에서의 지위고도, 500-1000hpa에서의 층후(thickness), 500hpa에서의 X와 Y의 바람성분, 750hpa에서의 X와 Y의 바람성분, 표면풍속, 500/ 750hpa/ 표면에서의 온도, 평균해면기압, 3시간 누적 강수, AWS관측소에서 관측된 RDAPS모형 실행전의 6시간과 12시간동안의 누적강수, 가강수량, 상대습도이며, 예측변수로는 강수발생확률로 선택하였다. 강우는 다양한 대기변수들의 비선형 조합으로 발생되기 때문에 예측인자와 예측변수 사이의 복잡한 비선형성을 고려하는데 유용한 인공신경망을 사용하였다. 신경망의 구조는 전방향 다층퍼셉트론으로 구성하였으며 역전파알고리즘을 학습방법으로 사용하였다. 강수예측성과의 질을 평가하기 위해서 $2{\times}2$ 분할표를 이용하여 Hit rate, Threat score, Probability of detection, Kuipers Skill Score를 사용하였으며, 신경망 학습후의 강수발생확률은 학습전의 강수발생확률에 비하여 한반도영역에서 평균적으로 Kuipers Skill Score가 0.2231에서 0.4293로 92.39% 상승하였다.

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Enhancing E-commerce Security: A Comprehensive Approach to Real-Time Fraud Detection

  • Sara Alqethami;Badriah Almutanni;Walla Aleidarousr
    • International Journal of Computer Science & Network Security
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    • v.24 no.4
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    • pp.1-10
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    • 2024
  • In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

A Study on Heavy Rainfall Guidance Realized with the Aid of Neuro-Fuzzy and SVR Algorithm Using AWS Data (AWS자료 기반 SVR과 뉴로-퍼지 알고리즘 구현 호우주의보 가이던스 연구)

  • Kim, Hyun-Myung;Oh, Sung-Kwun;Kim, Yong-Hyuk;Lee, Yong-Hee
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.4
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    • pp.526-533
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    • 2014
  • In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

Sensitivity Analysis of Numerical Weather Prediction Model with Topographic Effect in the Radiative Transfer Process (복사전달과정에서 지형효과에 따른 기상수치모델의 민감도 분석)

  • Jee, Joon-Bum;Min, Jae-Sik;Jang, Min;Kim, Bu-Yo;Zo, Il-Sung;Lee, Kyu-Tae
    • Atmosphere
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    • v.27 no.4
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    • pp.385-398
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    • 2017
  • Numerical weather prediction experiments were carried out by applying topographic effects to reduce or enhance the solar radiation by terrain. In this study, x and ${\kappa}({\phi}_o,\;{\theta}_o)$ are precalculated for topographic effect on high resolution numerical weather prediction (NWP) with 1 km spatial resolution, and meteorological variables are analyzed through the numerical experiments. For the numerical simulations, cases were selected in winter (CASE 1) and summer (CASE 2). In the CASE 2, topographic effect was observed on the southward surface to enhance the solar energy reaching the surface, and enhance surface temperature and temperature at 2 m. Especially, the surface temperature is changed sensitively due to the change of the solar energy on the surface, but the change of the precipitation is difficult to match of topographic effect. As a result of the verification using Korea Meteorological Administration (KMA) Automated Weather System (AWS) data on Seoul metropolitan area, the topographic effect is very weak in the winter case. In the CASE 1, the improvement of accuracy was numerically confirmed by decreasing the bias and RMSE (Root mean square error) of temperature at 2 m, wind speed at 10 m and relative humidity. However, the accuracy of rainfall prediction (Threat score (TS), BIAS, equitable threat score (ETS)) with topographic effect is decreased compared to without topographic effect. It is analyzed that the topographic effect improves the solar radiation on surface and affect the enhancements of surface temperature, 2 meter temperature, wind speed, and PBL height.