• The Korean Journal of Pesticide Science
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• v.16 no.4
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• pp.350-356
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• 2012

Performance of proficiency testing and the validation of analytical method was included a scheme of quality assurance in analytical chemistry laboratory to monitor a laboratory's performance abilities and produce consistently reliable data. This study was assessed the applicability of proficiency testing scheme proposed for analytical laboratories of pesticide product in domestic. The validation of analytical methods, stability and homogeneity for formulated pesticide products (emulsifiable concentrate) of emamectin benzoate and lufenuron was confirmed for the proficiency testing. The z-score of 33 participation laboratories for emamectin benzoate were that the numbers of outlier were 2 laboratories (6.0%), z-score outside the range from -3 to 3 designated "unaccptable" were 2 laboratories and z-score in the ranges -2 to -3 and 2 to 3 designated "questionable" were 3 laboratories (9.0%). Three laboratories (9.0%) showed the z-score designated "questionable" for lufenuron. The additional proficiency testing for various product types will be needed to establish the scheme of quality control.

• Korean Journal of Medicinal Crop Science
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• v.24 no.4
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• pp.271-276
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• 2016

Background: In the present study, we established an HPLC (high performance liquid chromatography)-analysis method for the determination of marker compounds as a part of the material standardization for the development of health-functional foods from Salvia plebeia R. Br. extract. Methods and Results: The quantitative determination method of hispidulin as a marker compound was optimized by HPLC analysis using a YMC hydrosphere C18 column with a gradient elution system. This method was validated using specificity, linearity, accuracy, and precision tests. It showed a high linearity in the calibration curve with a coefficient of correlation ($r^2$) of 0.999995. The method was fully validated, and was sensitive, with the limit of detection (LOD) at $0.09{\mu}g{\cdot}m{\ell}^{-1}$ and limit of quantification (LOQ) at $0.27{\mu}g{\cdot}m{\ell}^{-1}$. The relative standard deviation (RSD) values of the data from intra- and inter-day precision were 0.05 - 0.22% and 0.32 - 0.42%, respectively, and the intra- and inter-day accuracy of hispidulin were 99.5 - 102.3% and 98.8 - 101.5%, respectively. The average content of hispidulin in Salvia plebeia R. Br. extract was $3.945mg{\cdot}g^{-1}$ (0.39%). Conclusions: These results suggest that the developed HPLC method is very efficient, and that it could contribute to the quality control of Salvia plebeia R. Br. extracts as a functional ingredient in health functional foods.

• Journal of Korean Academy of Nursing
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• v.42 no.3
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• pp.385-395
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• 2012

Purpose: This purpose of this study was to develop and validate a Self-Efficacy Scale for Self-Management of Breast Cancer (SESSM-B). Methods: The SESSM-B was developed and validated as follows: Item generation, pilot study, and tests of validity and reliability. Twenty-one items were developed through evaluation by 10 experts and 13 items were finally confirmed through item analysis and factor analysis. Psychometric testing was performed with a convenience sample of 303 women with breast cancer. Data were analyzed using factor analysis, Pearson correlation coefficients, and Cronbach's alpha. Results: Five factors evolved from the factor analysis, which explained 69.8% of the total variance. The first factor 'coping with psycho-informational demand' explained 17.2%, 2nd factor 'maintenance of healthy lifestyle' 14.5%. 3rd factor 'management of side-effects' 13.3%, 4th factor 'therapeutic compliance' 12.8%, and 5th factor 'sexual life' 11.9%. SESSM-B also demonstrated a concurrent validity with health-related quality of life scale, EORTC QLQ-C30 & BR23. The internal consistency, Cronbach's alpha, was .78, and reliability of the subscales ranged from .61 to .79. Conclusion: The results of this study suggest that the SESSM-B is an easy, reliable, and valid instrument to measure self-efficacy for self-management of breast cancer.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.421-428
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• 2014

Like Alzheimer's disease, Parkinson's Disease(PD) is one of the most common neurodegenerative brain disorders. PD results from the deterioration of dopaminergic neurons in the brain region called the substantia nigra. Currently there is no cure for PD, but diagnosing in its early stage is important to provide treatments for relieving the symptoms and maintaining quality of life. Unlike many diagnosis methods of PD which use a single biomarker, we developed a diagnosis method that uses both biochemical biomarkers and imaging biomarkers. Our method uses ${\alpha}$-synuclein protein levels in the cerebrospinal fluid and diffusion tensor images(DTI). It achieved an accuracy over 91.3% in the 10-fold cross validation, and the best accuracy of 72% in an independent testing, which suggests a possibility for early detection of PD. We also analyzed the characteristics of the brain fiber pathways of Parkinson's disease patients and normal elderly people.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.257-263
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• 2024

Purpose: The purpose of this study was to classify mandibular molar furcation involvement (FI) in periapical radiographs using a deep learning algorithm. Materials and Methods: Full mouth series taken at East Carolina University School of Dental Medicine from 2011-2023 were screened. Diagnostic-quality mandibular premolar and molar periapical radiographs with healthy or FI mandibular molars were included. The radiographs were cropped into individual molar images, annotated as "healthy" or "FI," and divided into training, validation, and testing datasets. The images were preprocessed by PyTorch transformations. ResNet-18, a convolutional neural network model, was refined using the PyTorch deep learning framework for the specific imaging classification task. CrossEntropyLoss and the AdamW optimizer were employed for loss function training and optimizing the learning rate, respectively. The images were loaded by PyTorch DataLoader for efficiency. The performance of ResNet-18 algorithm was evaluated with multiple metrics, including training and validation losses, confusion matrix, accuracy, sensitivity, specificity, the receiver operating characteristic (ROC) curve, and the area under the ROC curve. Results: After adequate training, ResNet-18 classified healthy vs. FI molars in the testing set with an accuracy of 96.47%, indicating its suitability for image classification. Conclusion: The deep learning algorithm developed in this study was shown to be promising for classifying mandibular molar FI. It could serve as a valuable supplemental tool for detecting and managing periodontal diseases.

• Analytical Science and Technology
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• v.37 no.5
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• pp.280-294
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• 2024

This study introduces a novel analytical method for the assessment of pralsetinib impurities and degradation products (DPs), addressing critical gaps in existing methodologies. This research aims to develop a robust HPLC method for impurity analysis, characterize degradation products using LC-MS, and evaluate the environmental impact of the method. The study began by optimizing HPLC conditions with various columns and buffers, ultimately achieving successful separation using an XBridge^® RP-C18 column with ethanol as solvent A and 50 mM formic acid at pH 2.9. This setup provided excellent peak resolution and symmetry, essential for reliable stability studies. The developed HPLC method was then adapted for HPLC-MS/MS, enhancing sensitivity and detection efficiency of DPs. Stress degradation studies of pralsetinib under different conditions (acidic, basic, oxidative, thermal, and photolytic) revealed significant degradation under acidic (29.3 %) and basic (21.5 %) conditions, with several DPs identified. Oxidative stress resulted in 19.8 % degradation, while thermal and photolytic conditions caused minimal degradation. HPLC-MS/MS analysis identified structures of five degradation products, providing detailed insights into pralsetinib's stability and degradation pathways. Method validation followed ICH guidelines Q2(R1), confirming method's specificity, selectivity, sensitivity, linearity, accuracy, precision, and robustness. The method exhibited strong linearity with a coefficient of determination (r²) greater than 0.999 for pralsetinib and its impurities. This method advances impurity detection and DPs characterization, ensuring the quality and safety of pralsetinib. Additionally, method's environmental impact was assessed, aligning with sustainable analytical practices. These findings provide essential data on pralsetinib's stability, guiding storage conditions and ensuring its efficacy and safety in pharmaceutical applications.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.216-222
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• 2002

The ergonomic human model can be considered as a tool for the evaluation of ergonomic factors in vehicle design process. The proper anthropometric data on driver's postures are needed in order to apply a human model to vehicle design. Although studies on driver's posture have been carried out for the last few decades, there are still some problems for the posture data to be applied directly to the human model due to the lack of fitness because such studies were not carried out under the conditions for the human model application. In the traditional researches, the joint angles were evaluated by the categorized data, which are not appropriate for the human model application because it is so extensive that it can not explain the posture evaluation data in detail. And the human models require whole-body posture evaluation data rather than joint evaluation data. In this study a postural evaluation function was developed not by category data but by the concept of the loss function in quality engineering. The loss was defined as the discomfort in driver's posture and measured by the magnitude estimation technique in the experiment using a seating buck. Four loss functions for the each joint - knee, hip, shoulder, and elbow were developed and a whole-body postural evaluation function was constructed by the regression analysis using these loss functions as independent factors. The developed postural evaluation function shows a good prediction power for the driver's posture discomfort in validation test. It is expected that the driver's postural evaluation function based on the loss function can be used in the human model application to the vehicle design process.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.159-161
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• 2007

Geostationary Ocean Color Imager (GOCI), the world-first ocean remote sensing instrument on geostationary Communication, Ocean, Meteorological Satellite (COMS), will be able to take a picture of a large region several times a day (almost with every one hour interval). We, KORDI, are in charge for developing the GOCI data processing system (GDPS) which is the basic software for processing the data from GOCI. The GDPS will be based on windows operating system to produce the GOCI level 2 data products (useful for oceanographic environmental analysis) automatically in real-time mode. Also, the GDPS will be a user-interactive program by well-organized graphical user interfaces for data processing and visualization. Its products will be the chlorophyll concentration, amount of total suspended sediments (TSS), colored dissolved organic matters (CDOM) and red tide from water leaving radiance or remote sensing reflectance. In addition, the GDPS will be able to produce daily products such as water current vector, primary productivity, water quality categorization, vegetation index, using individual observation data composed from several subscenes provided by GOCI for each slit within the target area. The resulting GOCI level 2 data will be disseminated through LRIT using satellite dissemination system and through online request and download systems. This software is carefully designed and implemented, and will be tested by sub-contractual company until the end of this year. It will need to be updated in effect with respect to new/improved algorithms and the calibration/validation activities.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.289-299
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• 2015

The objective of the present study is to develop statistical quantitative forecast model for PM10 concentration over Seoul. We used three types of data (weather observation data in Korea, the China's weather observation data collected by GTS, and air quality numerical model forecasts). To apply the daily forecast system, hourly data are converted to daily data and then lagging was performed. The potential predictors were selected based on correlation analysis and multicollinearity check. Model validation has been performed for checking model stability. We applied two models (multiple regression model and threshold regression model) separately. The two models were compared based on the scatter plot of forecasts and observations, time series plots, RMSE, skill scores. As a result, a threshold regression model performs better than multiple regression model in high PM10 concentration cases.