• The Korean Journal of Pain
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• v.20 no.2
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• pp.163-168
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• 2007

Background: Temporomandibular joint disorder (TMD) is a group of musculoskeletal conditions characterized by pain in the pre-auricular area, limitation of jaw movement and palpable muscle tenderness. Thermography is a nonionizing, noninvasive diagnostic alternative for the evaluation of TMD. This study was conducted to evaluate the usefulness of thermography in the assessment of TMD. Methods: Thermography was conducted on the 61 patients who had been diagnosed with TMD, and on the 34 normal symptom-free volunteers. The temperature differences between opposite sides of the temporomandibular joint (${\Delta}T_{TMJ}$) and the masseter muscle (${\Delta}T_{MST}$) were calculated. The sensitivity and specificity of thermography was calculated at the cut off values of 0.2, 0.3, and $0.4^{\circ}C$. Results: In the patient group, the ${\Delta}T_{TMJ}$ was $0.42{\pm}0.38^{\circ}C$ and the ${\Delta}T_{MST}$ was $0.38{\pm}0.33^{\circ}C$, whereas in the control group the ${\Delta}T_{TMJ}$ was $0.10{\pm}0.07^{\circ}C$ and the ${\Delta}T_{MST}\;0.15{\pm}0.10^{\circ}C$. In addition, the patient group demonstrated a significantly lower level of thermal symmetry than the control group (P < 0.001) in both the temporomandibular joints and the masseter muscles. The sensitivity of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 67.2, 49.2, and 42.6% in the temporomandibular joint (TMJ) and 60.7, 49.2 and 37.7% in the masseter muscle, respectively. The specificity of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 88.2, 100, and 100% in the TMJ and 61.8, 91.2 and 100% in the masseter muscles, respectively. The accuracy of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 74.7, 67.4, and 63.2% in TMJ and 61.1, 64.2 and 60.0% in the masseter muscles, respectively. Conclusions: Temperature differences exist between the opposite sides of the TMD and masseter muscles in patients with TMD. Although the sensitivity of thermography in the diagnosis of TMD is low, it has high specificity in the evaluation of TMD, and is therefore applicable to patients with TMD.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.452-458
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• 2019

Coix lacryma-jobi var. ma-yuen (Rom. Caill.) Stapf (CL), which contains riboflavin and coixol, has traditionally been used to treat cancer and arthritis. However, no method for the simultaneous determination of riboflavin and coixol in CL sprouts has been established. In this study, we established and validated a high-performance liquid chromatography-diode array detection (HPLC-DAD) method for the identification and quantification of two reference markers, riboflavin and coixol, in CL sprout extracts. CL sprouts (whole sprouts and leaves) were subjected to extraction with 70% ethanol at room temperature and at 80 ℃ under reflux conditions. The two extractions were validated with respect to specificity, accuracy, precision, and linearity. The content of the two reference markers was highest in leaves extracted under reflux conditions (riboflavin, 8.23 ± 0.32 mg/g; coixol, 5.95 ± 0.04 mg/g). We also investigated the antioxidant activity of the extracts via 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS⁺) scavenging assays. The results indicated that extracts obtained from sprouts under reflux conditions had the strongest antioxidative effects (DPPH half maximal inhibitory concentration [IC₅₀], 68.9 ± 4.1 g/mL; and ABTS, IC₅₀, 34.9 ± 0.1 g/mL). These results can serve as baseline data for the simultaneous determination of the two reference marker compounds, riboflavin and coixol, and development of functional food materials using CL sprouts.

• Analytical Science and Technology
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• v.25 no.2
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• pp.121-126
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• 2012

It is essential to evaluate the quality of Hanji record paper without damaging the record paper by previous destructive methods. The samples were Hanji record paper produced in the 1900s. Near-infrared (NIR) spectrometer was used as a non destructive method for evaluating the quality of record papers. Fourier transform (FT) spectrometer was used with 12,500 to 4,000 $cm^{-1}$ wavenumber range for quantitative analysis and it has high accuracy and good signal-to-noise ratio. The acidity and moisture content of Hanji record paper were measured by integrating sphere as diffuse reflectance type. The acidity (pH) of chemical factors as a quality evaluated factor of Hanji was correlated to NIR spectrum. The NIR spectrum was pretreated to obtain the coefficients of optimum correlation. Multiplicative scatter correction (MSC) and First derivative of Savitzky-Golay were used as pretreated methods. The coefficients of optimum correlation were calculated by PLSR (partial least square regression). The correlation coefficients ($R^2$) of acidity had 0.92 on NIR spectra without pretreatment. Also the standard error of prediction (SEP) of pH was 0.24. And then the NIR spectra with pretreatment would have better correlation coefficient ($R^2$ = 0.98) and 0.19 as SEP on pH. For moisture contents, the linearity correlation without pretreatment was higher than the case with pretreatment (MSC, $1^{st}$ derivative). As the best result, the $R^2$ was 0.99 and SEP was 0.45. This indicates that it is highly proper to evaluate the quality of Hanji record papers speedily with integrated sphere and FT NIR analyzer as a non-destructive method.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• The Korean Journal of Nuclear Medicine
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• v.33 no.4
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• pp.413-421
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• 1999

Purpose: This study was performed to evaluate the usefulness of $^{99m}Tc$-HMPAO-labelled leucocyte scintigraphy for diagnosing prosthetic infection after total knee replacement arthroplasty without the aid of following bone marrow scintigraphy Materials and Methods: The study subjects were 25 prostheses of 17 patients (one man and 16 women, mean age. 65 years) who had total knee replacement arthroplasty. After injection of $^{99m}Tc$-HMPAO-labelled leucocyte, the whole body planar and knee SPECT images were obtained in all patients. The subjects were classified into three groups according to clinical suspicion of prosthetic infection. Group A (n=11) with high suspicion of infection; Group B (n=6) with equivocal suspicion of infection, and Group C (n=8) with asymptomatic contralateral prostheses. Final diagnosis of infection was based on surgical, histological and bacteriological data and clinical follow-up. Results: Infection was confirmed in 13 prostheses (11 in Group A and 2 in Group B). All prostheses in Group A were true positive. There were two true positives, one false positive and three true negatives in Group B, and six true negatives and two false positives in Group C. Overall sensitivity, specificity, and accuracy for diagnosis of the infected knee prosthesis were 100%, 75% and 88%, respectively Conclusion: $^{99m}Tc$-HMPAO-labelled leucocyte scintigraphy is a sensitive method for the diagnosis of infected knee prosthesis. However, false positive uptakes even in asymptomatic prosthesis suggest that bone marrow scintigraphy may be needed to achieve improved specificity.

• Horticultural Science & Technology
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• v.34 no.1
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• pp.172-182
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• 2016

This study was aimed to determine the changes in vitamin $B_5$ and $B_6$ contents compared to fresh materials after parboiling treatment of the main vegetables consumed in Korea. The specificity of accuracy and precision for vitamin $B_5$ and $B_6$ analysis method were validated using high-performance liquid chromatography (HPLC). The recovery rate of standard reference material (SRM) was excellent, and all analysis was under the control line based on the quality control chart for vitamin $B_5$ and $B_6$. The Z-score for vitamin $B_6$ in food analysis performance assessment scheme (FAPAS) proficiency test was -1.0, confirming reliability of analytical performance. The vitamin $B_5$ and $B_6$ contents in a total of 39 fresh materials and parboiled samples were analyzed. The contents of vitamin $B_5$ and $B_6$ ranged from 0.000 to 2.462 and from 0.000 to $0.127mg{\cdot}100g^{-1}$, respectively. The highest contents of vitamin $B_5$ and $B_6$ were $2.462mg{\cdot}100g^{-1}$ in fresh fatsia shoots (stem vegetables), and $0.127mg{\cdot}100g^{-1}$ in fresh spinach beet (leafy vegetables), respectively. Moreover, the vitamin $B_5$ and $B_6$ contents for parboiling treatment in most vegetables were reduced or not detected. In particular, the contents of vitamin $B_5$ in parboiled fatsia shoots and vitamin $B_6$ in parboiled yellow potato and spinach beet were decreased 20- and 4-fold compared with fresh material, respectively. These results can be used as important basic data for utilization and processing of various vegetable crops, information for dietary life, management of school meals, and national health for Koreans.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.1
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• pp.85-88
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• 2015

This study attempted to establish an HPLC analysis method for determination of marker compounds as a part of material standardization for the development of health functional food materials from Perilla frutescens Britton var. acuta Kudo. The quantitative determination method of rosmarinic acid as a marker compound of P. frutescens Britton var. acuta Kudo extract (PFE) was optimized by HPLC analysis using a C18 column ($4.6{\times}150mm$, $5{\mu}m$) with 0.1% acetic acid as the elution gradient and methanol as the mobile phase at a flow rate of 1 mL/min and detection wavelength of 280 nm. The HPLC/UV method was applied successfully to quantification of the marker compound in PFE after validation of the method with linearity, accuracy, and precision. The method showed high linearity in the calibration curve at a coefficient of correlation ($R^2$) of 0.9995, and the limit of detection and limit of quantitation were $0.36{\mu}g/mL$ and $1.2{\mu}g/mL$, respectively. Relative standard deviation (RSD) values of data from intra- and inter-day precision were less than 3.21% and 1.43%, respectively. Recovery rate test at rosmarinic acid concentrations of 12.5, 25 and $50{\mu}g/mL$ scored between 97.04~98.98% with RSD values from 0.25~1.97%. These results indicate that the established HPLC method is very useful for the determination of marker compound in PFE to develop a health functional material.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.383-389
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• 2009

The main objective of the research is a feasibility study on the intertidal zone using a X-band radar satellite, TerraSAR-X. The TerraSAR-X data have been acquired in the west coast of Korea where large tidal flats, Ganghwa and Yeongjong tidal flats, are developed. Investigations include: 1) waterline and backscattering characteristics of the high resolution X-band images in tidal flats; 2) polarimetric signature of halophytes (or salt marsh plants), specifically Suaeda japonica; and 3) phase and coherence of interferometric pairs. Waterlines from TerraSAR-X data satisfy the requirement of horizontal accuracy of 60 m that corresponds to 20 cm in average height difference while current other spaceborne SAR systems could not meet the requirement. HH-polarization was the best for extraction of waterline, and its geometric position is reliable due to the short wavelength and accurate orbit control of the TerraSAR-X. A halophyte or salt marsh plant, Suaeda japonica, is an indicator of local sea level change. From X-band ground radar measurements, a dual polarization of VV/VH-pol. is anticipated to be the best for detection of the plant with about 9 dB difference at 35 degree incidence angle. However, TerraSAR-X HH/TV dual polarization was turned to be more effective for salt marsh monitoring. The HH-HV value was the maximum of about 7.9 dB at 31.6 degree incidence angle, which is fairly consistent with the results of X-band ground radar measurement. The boundary of salt marsh is effectively traceable specifically by TerraSAR-X cross-polarization data. While interferometric phase is not coherent within normal tidal flat, areas of salt marsh where the landization is preceded show coherent interferometric phases regardless of seasons or tide conditions. Although TerraSAR-X interferometry may not be effective to directly measure height or changes in tidal flat surface, TanDEM-X or other future X-band SAR tandem missions within one-day interval would be useful for mapping tidal flat topography.

• Journal of Food Hygiene and Safety
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• v.34 no.5
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• pp.413-420
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• 2019

In this study, a sample preparation method and a simultaneous determination method by ultra-high performance liquid chromatography coupled with tandem mass spectrometry for 9 isomers of chlorogenic acid and arbutin in fruits were developed. The samples were extracted using 90% methanol (pH 3.0), with the solutions being shaken and then sonicated for 10 min each. After centrifugation at 4,000 rpm for 10 min, the extraction was concentrated under a vacuum at $40^{\circ}C$ using a vacuum evaporator. The residue was dissolved in 5 mL of 5% methanol and filtered through a $0.45{\mu}m$ membrane before UHPLC-MS/MS analysis. The separations were performed on a C18 column with gradient elution of water (containing 0.1% formic acid) and methanol (containing 0.1% formic acid). The specificity, linearity, limit of detection, limit of quantification, accuracy, and precision of the proposed methods were also evaluated.