Purpose: The purpose of this study was to determine the effect of a violent experience and job stress on burnout and to investigate the factors that affect burnout in female 119 emergency medical technicians. Methods: Data from 189 female EMTs were collected using a structured questionnaire. The data were analyzed using SPSS 23.0. The analyses included descriptive statistics, independent t-tests, analysis of variance, Scheffe test, Pearson's correlation coefficient, and multiple linear regression. Results: Burnout was positively correlated with a violent experience in female 119 EMTs. Violent experience (β=.39, p<.001) were a significant predictor of burnout in 15.2% of female 119 EMTs. Conclusion: A violent experience is identified as a significant factor affecting burnout in female 119 EMTs. Therefore, it is necessary to impart education to female 119 EMTs on effectively coping with violence.
Kim, So-Hwa;Kim, Seong-Oh;Choi, Hyung-Jun;Choi, Byung-Jai;Lee, Jae-Ho
Journal of the korean academy of Pediatric Dentistry
/
v.34
no.3
/
pp.430-437
/
2007
The probability table of Moyers and prediction equation of Tanaka and Johnston that have been the most frequently used, cannot produce accurate prediction when used in Korean because they are based on the Caucasian popularity of the Northern European race. The method of Moyers or Tanaka and Johnston predicts sizes of the unerupted canine and premolars on the basis of the sizes of mandibular incisors. However, some of the recent papers raise a question as to whether the mandibular incisors are the best combination to predict the sizes of the unerupted canine and premolars. The purpose of this study is to determine which sum or combination of sums of permanent tooth widths present the best prediction for the unerupted canine and premolars in a Korean sample, to calculate a specific linear regression equation for this population, and to evaluate the clinical significance. A new linear regression equation was calculated based on the data of 178 Korean young adults(70 women, 108 men, mean age 21.63 years) with complete permanent dentitions. Fifty three more children(28 girls, 25 boys, mean age 14.22 years) were used as a validation sample for the application of the multiple linear regression equation. The conclusions were as follows: 1. The combination of the sums of permanent upper central incisors, lower lateral incisors and upper first molars was the best predictor for the unerupted canine and premolars in this sample($r=0.65{\sim}0.80$). 2. The multiple linear regression equation was calculated including sex and arch as additional predictor variables. male, upper: $Y\;=\;0.332{\times}X_0\;+\;6.195$ male, lower: $Y\;=\;0.332{\times}X_0\;+\;5.269$ female, upper: $Y\;=\;0.332{\times}X_0\;+\;5.929$ female, lower: $Y\;=\;0.332{\times}X_0\;+\;5.003$. The determination coefficient of the equation was 64% and a standard error of the estimate was 0.71mm. 3. In about 97% of the validation sample, the estimation of the tooth width sums of unerupted canine and premolars using the new multiple linear regression equation was smaller than 1mm compaired with the actual values.
Greenhouse industry has been growing in many countries due to both the advantage of stable year-round crop production and increased demand for fresh vegetables. In greenhouse cultivation, $CO_2$ concentration plays an essential role in the photosynthesis process of crops. Continuous and accurate monitoring of $CO_2$ level in the greenhouse would improve profitability and reduce environmental impact, through optimum control of greenhouse $CO_2$ enrichment and efficient crop production, as compared with the conventional management practices without monitoring and control of $CO_2$ level. In this study, a mathematical model was developed to estimate the $CO_2$ emission from soil as affected by environmental factors in greenhouses. Among various model types evaluated, a linear regression model provided the best coefficient of determination. Selected predictor variables were solar radiation and relative humidity and exponential transformation of both. As a response variable in the model, the difference between $CO_2$ concentrations at the soil surface and 5-cm depth showed are latively strong relationship with the predictor variables. Segmented regression analysis showed that better models were obtained when the entire daily dataset was divided into segments of shorter time ranges, and best models were obtained for segmented data where more variability in solar radiation and humidity were present (i.e., after sun-rise, before sun-set) than other segments. To consider time delay in the response of $CO_2$ concentration, concept of time lag was implemented in the regression analysis. As a result, there was an improvement in the performance of the models as the coefficients of determination were 0.93 and 0.87 with segmented time frames for sun-rise and sun-set periods, respectively. Validation tests of the models to predict $CO_2$ emission from soil showed that the developed empirical model would be applicable to real-time monitoring and diagnosis of significant factors for $CO_2$ enrichment in a soil-based greenhouse.
Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R2 and adjusted [adj]-R2), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R2 ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH10), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R2 varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH10 as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGBStem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD2H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH30)} AGBBranch = exp {-131.6 + 15.0013 Ln (ρD2H) + 13.176 Ln (CW) + 21.8506 Ln (DSH30)} AGBFoliage = exp {-0.9496 + 0.5282 Ln (DSH30) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGBTotal = exp {-1.8245 + 1.4358 Ln (DSH30) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.
Objective: The purpose of this study was to determine the relationship between center of pressure (CoP) and local stability of the lower joints, which was calculated based on approximate entropy (ApEn) during walking in elderly women. Method: Eighteen elderly women were recruited (age: $66.4{\pm}1.2yrs$; mass: $55.4{\pm}8.3kg$; height: $1.56{\pm}0.04m$) for this study. Before collecting data, reflective marker triads composed of 3 non-collinear spheres were attached to the lateral surface of the thigh and shank near the mid-segment to measure motion of the thigh and shank segments. To measure foot motion, reflective markers were placed on the shoe at the heel, head of the fifth metatarsal, and lateral malleolus, and were also placed on the right anterior-superior iliac spine, left anterior-superior iliac spine, and sacrum to observe pelvic motion. During treadmill walking, kinematic data were recorded using 6 infrared cameras (Oqus 300, Qualisys, Sweden) with a 100 Hz sampling frequency and kinetic data were collected from a treadmill (Instrumented Treadmill, Bertec, USA) for 20 strides. From kinematic data, 3D angles of the lower extremity's joint were calculated using Cardan technique and then ApEn were computed for their angles to evaluate local stability. Range of CoP was determined from the kinetic data. Pearson product-moment and Spearman rank correlation coefficient were applied to find relationship between CoP and ApEn. The level of significance was determined at p<.05. Results: There was a negative linear correlation between CoP and ApEn of hip joint adduction-abduction motion (p<.05), but ApEn of other joint motion did not affect the CoP. Conclusion: It was conjectured that ApEn, local stability index, for adduction/abduction of the hip joint during walking could be useful as a fall predictor.
Background: Fractional exhaled nitric oxide (FeNO) is a non-invasive marker for eosinophilic airway inflammation and a good predictor of response to corticosteroids. There is a need for a reliable and accurate measurement method, as FeNO measurements have been widely used in clinical practice. Our study aimed to compare two FeNO analyzers and derive a conversion equation for FeNO measurements in adults. Methods: We included 99 participants who had chief complaints of chronic cough and difficulty in breathing. The participants underwent concurrent FeNO measurement using NIOX VERO (Circassia AB) and NObreath (Bedfont). We compared the values of the two devices and analyzed their correlation and agreement. We then formulated an equation to convert FeNO values measured by NObreath into those obtained by NIOX VERO. Results: The mean age of the participants was 51.2±17.1 years, with a female predominance (58.6%). Approximately 60% of the participants had asthma. The FeNO level measured by NIOX VERO (median, 27; interquartile range [IQR], 15-45) was significantly lower than that measured by NObreath (median, 38; IQR, 22-58; p<0.001). There was a strong positive correlation between the two devices (r=0.779, p<0.001). Additionally, Bland-Altman plots and intraclass correlation coefficient demonstrated a good agreement. Using linear regression, we derived the following conversion equation: natural log (Ln) (NObreath)=0.728×Ln (NIOX VERO)+1.244. Conclusion: The FeNO values of NIOX VERO and NObreath were in good agreement and had positive correlations. Our proposed conversion equation could help assess the accuracy of the two analyzers.
Polychlorinated biphenyls(PCBs) are halogenated aromatic compounds with the empirical formula $C_{12}$$H_{10-n}$C $l_{n}$(n=1~10), and are a mixture of possible 209 different chlorinated congeners. PCBs were widely used as dielectric fluids for capacitors and transformers, plasticizers, lubricant inks and paint addirives. Once released into the environment, PCBs persist for years because they are so resistant to degradation. In addition to their persistence in the environment, PCBs in ecological food chains undergo biomagnification because of their high degree of lipophilicity. In 1970s, the worldwide production of PCBs was ceased and the import of PCBs was prohibited since 1983 in Korea. In spite of these actions, many PCBs seems to be still in use. The environmental load of PCBs will continue to be recycled through air, land, water, and the biosphere for decades to come. This study was conducted to measure the concentrations of PCBs in the serum samples of 112 women by GC/MSD and GC/ECD. The main results of this study were as follows. 1. PCBs were detected in all samples. The mean $\pm$SD levels of PCBs in the serum were 3.613$\pm$0.759 ppb, and median were 3.828 ppb. 2. The correlation coefficients of the concentrations of 13 PCB congeners were from minimum, 0.7913 to maximum, 0.9985, and all was significant(p=0.0001). The correlation coefficient between the concentrations of PCBs and p,p'-DDE was 0.9641(p=0.0001). 3. There was a positive association between age and PCBs' concentrations (simple linear regression ; $R^2$=0.86, $\beta$=0.08023, p<0.001). 4. There was a positive association between total lipids in the serum and PCBs' concentrations (simple linear regression ; $R^2$=0.7058, $\beta$=0.00486, p<0.001). 5. For possible predictors of PCBs and p,p' -DDE levels in the serum, age adjusted model (Y=$\beta$$_{0}$+$\beta$$_1$age+ $B_2$X) was applied. For BMI, major residential area, wether to eat caught fish by angling, where to eat caught fish by angling(by parents in the past), fish consumption, meat consumption, meat consumption, and dairy consumption, there was no association. For total conception frequency and lactation frequency and lactation period, there was negative association.ion.
Salman Ihsan;Shahab Saqib;Hafiz Muhammad Awais Rashid;Fawad S. Niazi;Mohsin Usman Qureshi
Geomechanics and Engineering
/
v.35
no.2
/
pp.121-133
/
2023
The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.
Kim, Chul-Gyum;Lee, Jeongwoo;Lee, Jeong Eun;Kim, Hyeonjun
Journal of Korea Water Resources Association
/
v.55
no.10
/
pp.723-736
/
2022
In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.
The core questions for determining nitrogen topdress rate (Npi) at panicle initiation stage (PIS) are 'how much nitrogen accumulation during the reproductive stage (PNup) is required for the target rice yield or protein content depending on the growth and nitrogen nutrition status at PIS?' and 'how can we diagnose the growth and nitrogen nutrition status easily at real time basis?'. To address these questions, two years experiments from 2001 to 2002 were done under various rates of basal, tillering, and panicle nitrogen fertilizer by employing a rice cultivar, Hwaseongbyeo. The response of grain yield and milled-rice protein content was quantified in relation to RVIgreen (green ratio vegetation index) and SPAD reading measured around PIS as indirect estimators for growth and nitrogen nutrition status, the regression models were formulated to predict PNup based on the growth and nitrogen nutrition status and Npi at PIS. Grain yield showed quadratic response to PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict grain yield had a high determination coefficient of above 0.95. PNup for the maximum grain yield was estimated to be 9 to 13.5 kgN/10a within the range of RVIgreen around PIS of this experiment. decreasing with increasing RVIgreen and also to be 10 to 11 kgN/10a regardless of SPAD readings around PIS. At these PNup's the protein content of milled rice was estimated to rise above 9% that might degrade eating quality seriously Milled-rice protein content showed curve-linear increase with the increase of PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict protein content had a high determination coefficient of above 0.91. PNup to control the milled-rice protein content below 7% was estimated as 6 to 8 kgN/10a within the range of RVIgreen and SPAD reading of this experiment, showing much lower values than those for the maximum grain yield. The recovery of the Npi applied at PIS ranged from 53 to 83%, increasing with the increased growth amount while decreasing with the increasing Npi. The natural nitrogen supply from PIS to harvest ranged from 2.5 to 4 kg/10a, showing quadratic relationship with the shoot dry weight or shoot nitrogen content at PIS. The regression models to estimate PNup was formulated using Npi and anyone of RVIgreen, shoot dry weight, and shoot nitrogen content at PIS as predictor variables. These models showed good fitness with determination coefficients of 0.86 to 0.95 The prescription method based on the above models predicting grain yield, protein content and PNup and its constraints were discussed.
본 웹사이트에 게시된 이메일 주소가 전자우편 수집 프로그램이나
그 밖의 기술적 장치를 이용하여 무단으로 수집되는 것을 거부하며,
이를 위반시 정보통신망법에 의해 형사 처벌됨을 유념하시기 바랍니다.
[게시일 2004년 10월 1일]
이용약관
제 1 장 총칙
제 1 조 (목적)
이 이용약관은 KoreaScience 홈페이지(이하 “당 사이트”)에서 제공하는 인터넷 서비스(이하 '서비스')의 가입조건 및 이용에 관한 제반 사항과 기타 필요한 사항을 구체적으로 규정함을 목적으로 합니다.
제 2 조 (용어의 정의)
① "이용자"라 함은 당 사이트에 접속하여 이 약관에 따라 당 사이트가 제공하는 서비스를 받는 회원 및 비회원을
말합니다.
② "회원"이라 함은 서비스를 이용하기 위하여 당 사이트에 개인정보를 제공하여 아이디(ID)와 비밀번호를 부여
받은 자를 말합니다.
③ "회원 아이디(ID)"라 함은 회원의 식별 및 서비스 이용을 위하여 자신이 선정한 문자 및 숫자의 조합을
말합니다.
④ "비밀번호(패스워드)"라 함은 회원이 자신의 비밀보호를 위하여 선정한 문자 및 숫자의 조합을 말합니다.
제 3 조 (이용약관의 효력 및 변경)
① 이 약관은 당 사이트에 게시하거나 기타의 방법으로 회원에게 공지함으로써 효력이 발생합니다.
② 당 사이트는 이 약관을 개정할 경우에 적용일자 및 개정사유를 명시하여 현행 약관과 함께 당 사이트의
초기화면에 그 적용일자 7일 이전부터 적용일자 전일까지 공지합니다. 다만, 회원에게 불리하게 약관내용을
변경하는 경우에는 최소한 30일 이상의 사전 유예기간을 두고 공지합니다. 이 경우 당 사이트는 개정 전
내용과 개정 후 내용을 명확하게 비교하여 이용자가 알기 쉽도록 표시합니다.
제 4 조(약관 외 준칙)
① 이 약관은 당 사이트가 제공하는 서비스에 관한 이용안내와 함께 적용됩니다.
② 이 약관에 명시되지 아니한 사항은 관계법령의 규정이 적용됩니다.
제 2 장 이용계약의 체결
제 5 조 (이용계약의 성립 등)
① 이용계약은 이용고객이 당 사이트가 정한 약관에 「동의합니다」를 선택하고, 당 사이트가 정한
온라인신청양식을 작성하여 서비스 이용을 신청한 후, 당 사이트가 이를 승낙함으로써 성립합니다.
② 제1항의 승낙은 당 사이트가 제공하는 과학기술정보검색, 맞춤정보, 서지정보 등 다른 서비스의 이용승낙을
포함합니다.
제 6 조 (회원가입)
서비스를 이용하고자 하는 고객은 당 사이트에서 정한 회원가입양식에 개인정보를 기재하여 가입을 하여야 합니다.
제 7 조 (개인정보의 보호 및 사용)
당 사이트는 관계법령이 정하는 바에 따라 회원 등록정보를 포함한 회원의 개인정보를 보호하기 위해 노력합니다. 회원 개인정보의 보호 및 사용에 대해서는 관련법령 및 당 사이트의 개인정보 보호정책이 적용됩니다.
제 8 조 (이용 신청의 승낙과 제한)
① 당 사이트는 제6조의 규정에 의한 이용신청고객에 대하여 서비스 이용을 승낙합니다.
② 당 사이트는 아래사항에 해당하는 경우에 대해서 승낙하지 아니 합니다.
- 이용계약 신청서의 내용을 허위로 기재한 경우
- 기타 규정한 제반사항을 위반하며 신청하는 경우
제 9 조 (회원 ID 부여 및 변경 등)
① 당 사이트는 이용고객에 대하여 약관에 정하는 바에 따라 자신이 선정한 회원 ID를 부여합니다.
② 회원 ID는 원칙적으로 변경이 불가하며 부득이한 사유로 인하여 변경 하고자 하는 경우에는 해당 ID를
해지하고 재가입해야 합니다.
③ 기타 회원 개인정보 관리 및 변경 등에 관한 사항은 서비스별 안내에 정하는 바에 의합니다.
제 3 장 계약 당사자의 의무
제 10 조 (KISTI의 의무)
① 당 사이트는 이용고객이 희망한 서비스 제공 개시일에 특별한 사정이 없는 한 서비스를 이용할 수 있도록
하여야 합니다.
② 당 사이트는 개인정보 보호를 위해 보안시스템을 구축하며 개인정보 보호정책을 공시하고 준수합니다.
③ 당 사이트는 회원으로부터 제기되는 의견이나 불만이 정당하다고 객관적으로 인정될 경우에는 적절한 절차를
거쳐 즉시 처리하여야 합니다. 다만, 즉시 처리가 곤란한 경우는 회원에게 그 사유와 처리일정을 통보하여야
합니다.
제 11 조 (회원의 의무)
① 이용자는 회원가입 신청 또는 회원정보 변경 시 실명으로 모든 사항을 사실에 근거하여 작성하여야 하며,
허위 또는 타인의 정보를 등록할 경우 일체의 권리를 주장할 수 없습니다.
② 당 사이트가 관계법령 및 개인정보 보호정책에 의거하여 그 책임을 지는 경우를 제외하고 회원에게 부여된
ID의 비밀번호 관리소홀, 부정사용에 의하여 발생하는 모든 결과에 대한 책임은 회원에게 있습니다.
③ 회원은 당 사이트 및 제 3자의 지적 재산권을 침해해서는 안 됩니다.
제 4 장 서비스의 이용
제 12 조 (서비스 이용 시간)
① 서비스 이용은 당 사이트의 업무상 또는 기술상 특별한 지장이 없는 한 연중무휴, 1일 24시간 운영을
원칙으로 합니다. 단, 당 사이트는 시스템 정기점검, 증설 및 교체를 위해 당 사이트가 정한 날이나 시간에
서비스를 일시 중단할 수 있으며, 예정되어 있는 작업으로 인한 서비스 일시중단은 당 사이트 홈페이지를
통해 사전에 공지합니다.
② 당 사이트는 서비스를 특정범위로 분할하여 각 범위별로 이용가능시간을 별도로 지정할 수 있습니다. 다만
이 경우 그 내용을 공지합니다.
제 13 조 (홈페이지 저작권)
① NDSL에서 제공하는 모든 저작물의 저작권은 원저작자에게 있으며, KISTI는 복제/배포/전송권을 확보하고
있습니다.
② NDSL에서 제공하는 콘텐츠를 상업적 및 기타 영리목적으로 복제/배포/전송할 경우 사전에 KISTI의 허락을
받아야 합니다.
③ NDSL에서 제공하는 콘텐츠를 보도, 비평, 교육, 연구 등을 위하여 정당한 범위 안에서 공정한 관행에
합치되게 인용할 수 있습니다.
④ NDSL에서 제공하는 콘텐츠를 무단 복제, 전송, 배포 기타 저작권법에 위반되는 방법으로 이용할 경우
저작권법 제136조에 따라 5년 이하의 징역 또는 5천만 원 이하의 벌금에 처해질 수 있습니다.
제 14 조 (유료서비스)
① 당 사이트 및 협력기관이 정한 유료서비스(원문복사 등)는 별도로 정해진 바에 따르며, 변경사항은 시행 전에
당 사이트 홈페이지를 통하여 회원에게 공지합니다.
② 유료서비스를 이용하려는 회원은 정해진 요금체계에 따라 요금을 납부해야 합니다.
제 5 장 계약 해지 및 이용 제한
제 15 조 (계약 해지)
회원이 이용계약을 해지하고자 하는 때에는 [가입해지] 메뉴를 이용해 직접 해지해야 합니다.
제 16 조 (서비스 이용제한)
① 당 사이트는 회원이 서비스 이용내용에 있어서 본 약관 제 11조 내용을 위반하거나, 다음 각 호에 해당하는
경우 서비스 이용을 제한할 수 있습니다.
- 2년 이상 서비스를 이용한 적이 없는 경우
- 기타 정상적인 서비스 운영에 방해가 될 경우
② 상기 이용제한 규정에 따라 서비스를 이용하는 회원에게 서비스 이용에 대하여 별도 공지 없이 서비스 이용의
일시정지, 이용계약 해지 할 수 있습니다.
제 17 조 (전자우편주소 수집 금지)
회원은 전자우편주소 추출기 등을 이용하여 전자우편주소를 수집 또는 제3자에게 제공할 수 없습니다.
제 6 장 손해배상 및 기타사항
제 18 조 (손해배상)
당 사이트는 무료로 제공되는 서비스와 관련하여 회원에게 어떠한 손해가 발생하더라도 당 사이트가 고의 또는 과실로 인한 손해발생을 제외하고는 이에 대하여 책임을 부담하지 아니합니다.
제 19 조 (관할 법원)
서비스 이용으로 발생한 분쟁에 대해 소송이 제기되는 경우 민사 소송법상의 관할 법원에 제기합니다.
[부 칙]
1. (시행일) 이 약관은 2016년 9월 5일부터 적용되며, 종전 약관은 본 약관으로 대체되며, 개정된 약관의 적용일 이전 가입자도 개정된 약관의 적용을 받습니다.