• Radiation Oncology Journal
• /
• v.20 no.1
• /
• pp.41-52
• /
• 2002

Purpose : 3D conformal radiotherapy, the optimum dose delivered to the tumor and provided the risk of normal tissue unless marginal miss, was restricted by organ motion. For tumors in the thorax and abdomen, the planning target volume (PTV) is decided including the margin for movement of tumor volumes during treatment due to patients breathing. We designed the respiratory gating radiotherapy device (RGRD) for using during CT simulation, dose planning and beam delivery at identical breathing period conditions. Using RGRD, reducing the treatment margin for organ (thorax or abdomen) motion due to breathing and improve dose distribution for 3D conformal radiotherapy. Materials and Methods : The internal organ motion data for lung cancer patients were obtained by examining the diaphragm in the supine position to find the position dependency. We made a respiratory gating radiotherapy device (RGRD) that is composed of a strip band, drug sensor, micro switch, and a connected on-off switch in a LINAC control box. During same breathing period by RGRD, spiral CT scan, virtual simulation, and 3D dose planing for lung cancer patients were peformed, without an extended PTV margin for free breathing, and then the dose was delivered at the same positions. We calculated effective volumes and normal tissue complication probabilities (NTCP) using dose volume histograms for normal lung, and analyzed changes in doses associated with selected NTCP levels and tumor control probabilities (TCP) at these new dose levels. The effects of 3D conformal radiotherapy by RGRD were evaluated with DVH (Dose Volume Histogram), TCP, NTCP and dose statistics. Results : The average movement of a diaphragm was 1.5 cm in the supine position when patients breathed freely. Depending on the location of the tumor, the magnitude of the PTV margin needs to be extended from 1 cm to 3 cm, which can greatly increase normal tissue irradiation, and hence, results in increase of the normal tissue complications probabiliy. Simple and precise RGRD is very easy to setup on patients and is sensitive to length variation (+2 mm), it also delivers on-off information to patients and the LINAC machine. We evaluated the treatment plans of patients who had received conformal partial organ lung irradiation for the treatment of thorax malignancies. Using RGRD, the PTV margin by free breathing can be reduced about 2 cm for moving organs by breathing. TCP values are almost the same values $(4\~5\%\;increased)$ for lung cancer regardless of increasing the PTV margin to 2.0 cm but NTCP values are rapidly increased $(50\~70\%\;increased)$ for upon extending PTV margins by 2.0 cm. Conclusion : Internal organ motion due to breathing can be reduced effectively using our simple RGRD. This method can be used in clinical treatments to reduce organ motion induced margin, thereby reducing normal tissue irradiation. Using treatment planning software, the dose to normal tissues was analyzed by comparing dose statistics with and without RGRD. Potential benefits of radiotherapy derived from reduction or elimination of planning target volume (PTV) margins associated with patient breathing through the evaluation of the lung cancer patients treated with 3D conformal radiotherapy.

• Radiation Oncology Journal
• /
• v.21 no.1
• /
• pp.82-93
• /
• 2003

Purpose : To reduce the Irradiation dose to the lungs and heart in the case of chest wail irradiation using an oppositional electron beam, we used an Individualized custom bolus, which was precisely designed to compensate for the differences In chest wall thickness. The benefits were evaluated by comparing the normal tissue complication probablilties (NTCPS) and dose statistics both with and without boluses. Materials and Methods : Boluses were made, and their effects evaluated in ten patients treated using the reverse hockey-stick technique. The electron beam energy was determined so as to administer 80% of the irradiation prescription dose to the deepest lung-chest wall border, which was usually located at the internal mammary lymph node chain. An individualized custom bolus was prepared to compensate for a chest wall thinner than the prescription depth by meticulously measuring the chest wall thickness at 1 emf intervals on the planning CT Images. A second planning CT was obtained overlying the individuailzed custom bolus for each patient's chest wall. 3-D treatment planning was peformed using ADAC-Pinnacle$^{3}$ for all patients with and without bolus. NTCPS based on 'the Lyman-Kutcher' model were analyzed and the mean, maximum, minimum doses, V$_{50}$ and V$_{95}$ for 4he heari and lungs were computed. Results .The average NTCPS in the ipsliateral lung showed a statistically significant reduction (p<0.01), from 80.2${\pm}$3.43% to 47.7${\pm}$4.61%, with the use of the individualized custom boluses. The mean lung irradiation dose to the ipsilateral iung was also significantly reduced by about 430 cGy, Trom 2757 cGy to 2,327 cGy (p<0.01). The V$_{50}$ and V$_{95}$ in the ipsilateral lung markedly decreased from the averages of 54.5 and 17.4% to 45.3 and 11.0%, respectively. The V$_{50}$ and V$_{95}$ In the heart also decreased from the averages of 16.8 and 6.1% to 9.8% and 2.2%, respectively. The NTCP In the contralateral lung and the heart were 0%, even for the cases with no bolus because of the small effective mean radiation volume values of 4.4 and 7.1%, respectively Conclusion : The use of an Individualized custom bolus in the radiotherapy of postrnastectorny chest wall reduced the NTCP of the ipsilateral lung by about 24.5 to 40.5%, which can improve the complication free cure probability of breast cancer patients.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.5
• /
• pp.1699-1705
• /
• 2015

Background: Medication errors in oncology may cause severe clinical problems due to low therapeutic indices and high toxicity of chemotherapeutic agents. We aimed to investigate unintentional medication errors and underlying factors during chemotherapy preparation and administration based on a systematic survey conducted to reflect oncology nurses experience. Materials and Methods: This study was conducted in 18 adult chemotherapy units with volunteer participation of 206 nurses. A survey developed by primary investigators and medication errors (MAEs) defined preventable errors during prescription of medication, ordering, preparation or administration. The survey consisted of 4 parts: demographic features of nurses; workload of chemotherapy units; errors and their estimated monthly number during chemotherapy preparation and administration; and evaluation of the possible factors responsible from ME. The survey was conducted by face to face interview and data analyses were performed with descriptive statistics. Chi-square or Fisher exact tests were used for a comparative analysis of categorical data. Results: Some 83.4% of the 210 nurses reported one or more than one error during chemotherapy preparation and administration. Prescribing or ordering wrong doses by physicians (65.7%) and noncompliance with administration sequences during chemotherapy administration (50.5%) were the most common errors. The most common estimated average monthly error was not following the administration sequence of the chemotherapeutic agents (4.1 times/month, range 1-20). The most important underlying reasons for medication errors were heavy workload (49.7%) and insufficient number of staff (36.5%). Conclusions: Our findings suggest that the probability of medication error is very high during chemotherapy preparation and administration, the most common involving prescribing and ordering errors. Further studies must address the strategies to minimize medication error in chemotherapy receiving patients, determine sufficient protective measures and establishing multistep control mechanisms.

• Applied Chemistry for Engineering
• /
• v.33 no.6
• /
• pp.563-573
• /
• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

• Korean Journal of Radiology
• /
• v.24 no.9
• /
• pp.860-870
• /
• 2023

Objective: The intra-parotid facial nerve (FN) can be visualized using three-dimensional double-echo steady-state water-excitation sequence magnetic resonance imaging (3D-DESS-WE-MRI). However, the clinical impact of FN imaging using 3D-DESS-WE-MRI before parotidectomy has not yet been explored. We compared the clinical outcomes of parotidectomy in patients with and without preoperative 3D-DESS-WE-MRI. Materials and Methods: This prospective, non-randomized, single-institution study included 296 adult patients who underwent parotidectomy for parotid tumors, excluding superficial and mobile tumors. Preoperative evaluation with 3D-DESS-WE-MRI was performed in 122 patients, and not performed in 174 patients. FN visibility and tumor location relative to FN on 3D-DESS-WE-MRI were evaluated in 120 patients. Rates of FN palsy (FNP) and operation times were compared between patients with and without 3D-DESS-WE-MRI; propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to adjust for surgical and tumor factors. Results: The main trunk, temporofacial branch, and cervicofacial branch of the intra-parotid FN were identified using 3D-DESS-WE-MRI in approximately 97.5% (117/120), 44.2% (53/120), and 25.0% (30/120) of cases, respectively. The tumor location relative to FN, as assessed on magnetic resonance imaging, concurred with surgical findings in 90.8% (109/120) of cases. Rates of temporary and permanent FNP did not vary between patients with and without 3D-DESS-WE-MRI according to PSM (odds ratio, 2.29 [95% confidence interval {CI} 0.64-8.25] and 2.02 [95% CI: 0.32-12.90], respectively) and IPTW (odds ratio, 1.76 [95% CI: 0.19-16.75] and 1.94 [95% CI: 0.20-18.49], respectively). Conversely, operation time for surgical identification of FN was significantly shorter with 3D-DESS-WE-MRI (median, 25 vs. 35 min for PSM and 25 vs. 30 min for IPTW, P < 0.001). Conclusion: Preoperative FN imaging with 3D-DESS-WE-MRI facilitated anatomical identification of FN and its relationship to the tumor during parotidectomy. This modality reduced operation time for FN identification, but did not significantly affect postoperative FNP rates.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.5
• /
• pp.417-425
• /
• 2018

This paper proposes a safety management method that extracts ETA (event tree analysis) based scenario and combines ICT technology to reduce serious disasters occurring workplace for shipbuilding and offshore plant. The statistics of Safety and Health Agency and (previous)Ministry of Public Safety and Security show that the most frequent accident among the serious disasters related to shipbuilding and offshore plant is falling. The main cause of accidents is absence of a safety belt and safety belt ring. To solve these problems, we create ETA based scenarios to derive results based on safety considerations. Based on these results, we propose a solution by applying ICT technology for accident prevention. Deriving ETA based scenarios and ICT technology, the proposed solutions include a system for detecting the wearing of safety belts and safety helmets, a system for detecting whether or not the safety belts are connected, and a hook system for measuring safety distances. These safety related systems can reduce the probability of death of workers. By preventing accidents using the proposed method, we can reduce serious disasters in shipbuilding and offshore plant and establish systematic safety management.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.4
• /
• pp.87-96
• /
• 2021

Determining design rainfall is the first step to plan an agricultural drainage facility. The objective of this study is to evaluate whether the current method for parameter estimation is reasonable for computing the design rainfall. The current Gumbel-Kendall (G-K) method was compared with two other methods which are Gumbel-Chow (G-C) method and Probability weighted moment (PWM). Hourly rainfall data were acquired from the 60 ASOS (Automated Synoptic Observing System) stations across the nation. For the goodness-of-fit test, this study used chi-squared (𝛘²) and Kolmogorov-Smirnov (K-S) test. When using G-K method, 𝛘² statistics of 18 stations exceeded the critical value (𝑥²_a=0.05,df=4=9.4877) and 10, 3 stations for G-C method, PWM method respectively. For K-S test, none of the stations exceeded the critical value (D^a=0.05_n=0.19838). However, G-K method showed the worst performances in both tests compared to other methods. Subsequently, this study computed design rainfall of 48-hour duration in 60 ASOS stations. G-K method showed 5.6 and 6.4% higher average design rainfall and 15.2 and 24.6% higher variance compared to G-C and PWM methods. In short, G-K showed the worst performance in goodness-of-fit tests and showed higher design rainfall with the least robustness. Likewise, considering the basic assumptions of the design rainfall estimation, G-K is not an appropriate method for the practical use. This study can be referenced and helpful when revising the agricultural drainage standards.

• The Journal of Engineering Geology
• /
• v.14 no.4 s.41
• /
• pp.443-450
• /
• 2004

This study developed a prediction model of debris flow to predict a landslide probability on natural terrain composed of the Tertiary sedimentary and volcanic rocks using a logistic regression analysis. The landslides data were collected around Pohang, Gyeongbuk province where more than 100 landslides were occurred in 1998. Considered with basic characteristics of the logistic regression analysis, field survey and laboratory soil tests were performed for both slided points and not-slided points. The final iufluential factors on landslides were selected as six factors by the logistic regression analysis. The six factors are composed of two topographic factors and four geologic factors. The developed landslide prediction model has more than $90\%$ of prediction accuracy. Therefore, it is possible to make probabilistic and quantitative prediction of landslide occurrence using the developed model in this study area as well as the previously developed model for metamorphic and granitic rocks.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.57-73
• /
• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.9
• /
• pp.161-169
• /
• 2020

This study investigated the effects and relationships of various stress, school life adjustment and happiness factors among middle school and high school adolescents. The structural model group effects between boy and girl groups were also investigated. Data was employed from the 'Korean Children and Youth Well-Being Index Survey, 2018' while subjects of the analysis included 5,144 students. Based on the education statistics data, the sample was collected by probability proportional to size. Results utilizing structural equation modeling and multigroup analysis showed that first, school stress, friend stress, and look stress had significant effects on adolescent happiness. School life adjustment had positive significant effects on adolescents' happiness. Parent stress, money stress and school stress had significant effects on school life adjustment. Second, the mediating effect of school life adjustment between stress and happiness was significant with parent, school stress, and financial stress. Third, results of the multi-group analysis by level and gender showed that the structure was similar between boys and girls, with the only differences among mediating effects. Boys had significant mediating effects by school stress, and financial stress, while girls had significant mediating effects by school stress, financial stress, and parent stress. Finally, based on the results of the study, practice and policy implications were suggested to support the promotion of adolescents' happiness.