• The Korean Journal of Nuclear Medicine Technology
• /
• v.22 no.2
• /
• pp.74-78
• /
• 2018

Purpose $[^{11}C]$acetate has been proved useful in detecting the myocardial oxygen metabolism and various malignancies including prostate cancer, hepatocellular carcinoma, renal cell carcinoma and brain tumors. The purpose of study was to improve the radiosynthesis yield of $[^{11}C]$acetate on a automated radiosynthesis module. Materials and Methods $[^{11}C]$acetate was prepared by carboxylation of grignard reagent, methylmagnesium chloride, with $[^{11}C]$$CO_2$ gas, followed by hydrolysis with 1 mM acetic acid and purification using solid phase extraction cartridges. The effect of the reaction temperature ($0^{\circ}C$, $10^{\circ}C$, $-55^{\circ}C$) and cyclotron beam time (10 min, 15 min, 20 min, 25 min) on the radiosynthesis yield were investigated in the $[^{11}C]$acetate labeling reaction. Results The maximum radiosynthesis yield was obtained at $-10^{\circ}C$ of reaction temperature. The radioactivities of $[^{11}C]$acetate acquired at $-10^{\circ}C$ reaction temperature was 2.4 times higher than those of $[^{11}C]$acetate acquired at $-55^{\circ}C$. Radiosynthesis yield of $[^{11}C]$acetate increased with increasing cyclotron beam time. Conclusion This study shows that radiosynthesis yield of $[^{11}C]$acetate highly dependent on reaction temperature. The best radiosynthesis yield was obtained in reaction of grignard reagent with $[^{11}C]$$CO_2$ at $-10^{\circ}C$. This radiolabeling conditions will be ideal for routine clinical application.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.25 no.2
• /
• pp.29-34
• /
• 2021

Purpose Sample reception environment system in nuclear medicine has not changed much compared to 20 years ago. When preparing sample for in vitro test, there was no significant change because the test was carried out by generating an own specimen from the parent specimen. In this study, We would like to introduce a method that automatically removes the sample cap using the automated decapper equipment and enables automatic reception at the same time. In addition, including a provisional reception system. Materials and Methods In 2019, it was intended to get a device that automatically removes the cap of a patient's blood sample. This equipment is the same as the equipment used in the Department of Laboratory Medicine (Vacuette Ⓡ Unicap Belt Decapper, Greiner bio-one, Austria). However, the purchase was delayed due to differences in tube size, budget, and space. In January 2020, we borrowed domestic automatic decapper equipment and modified it to suit our laboratory environment. After 9 months, we were able to introduce a system that automatically removes the lid of a patient's blood sample and at the same time automatically accepts the test. And, through the provisional reception system, it was possible to know the arrival of the specimen in a short time. Results With the use of an automatic decapper device, the sample cap was automatically removed, and the reception proceeded at the same time. So, it was very efficient at work because it shortened the sample preparation time by about 20 minutes. In addition, it was possible to prevent the examiner's musculoskeletal disorders caused by repeated wrist use. After using the provisional reception system, patients were able to be discharged quickly, and the number of phone calls to confirm the arrival of samples was reduced. Conclusion Most hospitals have about four employees in the nuclear medicine in vitro laboratory. It is effective to use automatic decapper equipment and a provisional reception system for organizations that perform work with the minimum number of personnel.

• Korean Journal of Environmental Agriculture
• /
• v.42 no.1
• /
• pp.52-62
• /
• 2023

This study focused on determining the specific causes and prevention methods of mass fish deaths occurred in five reservoirs (Gagugi, Neupgokgi, Danggokgi, Sagokji, and Hangokji) in Andong-si. For this purpose, a survey of agricultural land and livestock in the upper part of the reservoirs and analysis of water quality in the reservoir irrespective of whether it rains or not were conducted. We attempted to examine the changes in dissolved oxygen (DO) in the surface and bottom layers of reservoirs and changes in DO depending on the amount of livestock compost and time. Based on the above investigations, treatment plans were established to efficiently control the inflow of contaminated water into reservoirs. The rainfall and farmland areas in the upper part of the reservoir were investigated using Google and aviation data provided by the Ministry of Land, Infrastructure, and Transport. The current status of livestock farms distributed around the reservoirs was also examined because compost from these farms can flow into the reservoir when it rains. Various water quality parameters, such as phosphate phosphorus (PO₄-P) and ammonium nitrogen (NH₃-N), were analyzed and compared for each reservoir during the rainy season. Changes in the DO concentration and electrical conductivity (EC) were also observed at the inlet of the reservoir during raining using an automated instrument. In addition, DO was measured until the concentration reached 0 ppm in 10 min by adding livestock compost at various concentrations (0.05%, 0.1%, 0.3%, and 0.5% by wt.), where the concentration of the livestock compost represents the relative weight of rainwater. The DO concentration in the surface layer of reservoirs was 3.7 to 5.3 ppm, which is sufficient for fish survival. However, the fish could not survive at the bottom layer with DO concentration of 0.0-2.1 ppm. When the livestock compost was 0.3%, DO required 10-19 h to reach 0 ppm. Considering these results, it was confirmed that the DO in the bottom layer of the reservoir could easily change to an anaerobic state within 24 h when the livestock compost in the rainwater exceeds 0.3%. The results show that the direct cause of fish mortality is the inflow of excessive livestock compost into reservoirs during the first rainfall in spring. All the surveyed reservoirs had relatively good topographical features for the inflow of compost generated from livestock farms. This keeps the bottom layer of the reservoir free of oxygen. Therefore, to prevent fish death due to insufficient DO in the reservoir, measures should be undertaken to limit the amount of livestock compost flowing into the reservoir within 0.3%, which has been experimentally determined. As a basic countermeasure, minerals such as limestone, dolomite, and magnesia containing calcium and magnesium should be added to the compost of livestock farms around the reservoir. These minerals have excellent pollutant removal capabilities when sprayed onto the compost. In addition, measures should be taken to prevent fish death according to the characteristics of each reservoir.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.23 no.2
• /
• pp.51-58
• /
• 2019

Purpose In in-vitro laboratories of nuclear medicine department, when the reagent lot or reagent lot changes Comparability test or parallel test is performed to determine whether the results between lots are reliable. The most commonly used standard domestic laboratories is to obtain %difference from the difference in results between two lots of reagents, and then many laboratories are set the standard to less than 20% at low concentrations and less than 10% at medium and high concentrations. If the range is deviated from the standard, the test is considered failed and it is repeated until the result falls within the standard range. In this study, several tests are selected that are performed in nuclear medicine in-vitro laboratories to analyze parallel test results and to establish criteria for customized percent difference for each test. Materials and Methods From January to November 2018, the result of parallel test for reagent lot change is analyzed for 7 items including thyroid-stimulating hormone (TSH), free thyroxine (FT4), carcinoembryonic antigen (CEA), CA-125, prostate-specific antigen (PSA), HBs-Ab and Insulin. The RIA-MAT 280 system which adopted the principle of IRMA is used for TSH, FT4, CEA, CA-125 and PSA. TECAN automated dispensing equipment and GAMMA-10 is used to measure insulin test. For the test of HBs-Ab, HAMILTON automated dispensing equipment and Cobra Gamma ray measuring instrument are used. Separate reagent, customized calibrator and quality control materials are used in this experiment. Results 1. TSH [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(low concentration) [14.8 / 4.4 / 3.7 / 0.0 ] C-2(middle concentration) [10.1 / 4.2 / 3.7 / 0.0] 2. FT4 [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(low concentration) [10.0 / 4.2 / 3.9 / 0.0] C-2(high concentration) [9.6 / 3.3 / 3.1 / 0.0 ] 3. CA-125 [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(middle concentration) [9.6 / 4.3 / 4.3 / 0.3] C-2(high concentration) [6.5 / 3.5 / 4.3 / 0.4] 4. CEA [%diffrence Max / Mean / median] (P-value by t-test > 0.05) C-1(low concentration) [9.8 / 4.2 / 3.0 / 0.0] C-2(middle concentration) [8.7 / 3.7 / 2.3 / 0.3] 5. PSA [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(low concentration) [15.4 / 7.6 / 8.2 / 0.0] C-2(middle concentration) [8.8 / 4.5 / 4.8 / 0.9] 6. HBs-Ab [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(middle concentration) [9.6 / 3.7 / 2.7 / 0.2] C-2(high concentration) [8.9 / 4.1 / 3.6 / 0.3] 7. Insulin [%diffrence Max / Mean / Median] (P-value by t-test > 0.05) C-1(middle concentration) [8.7 / 3.1 / 2.4 / 0.9] C-2(high concentration) [8.3 / 3.2 / 1.5 / 0.1] In some low concentration measurements, the percent difference is found above 10 to nearly 15 percent in result of target value calculated at a lower concentration. In addition, when the value is measured after Standard level 6, which is the highest value of reagents in the dispensing sequence, the result would have been affected by a hook effect. Overall, there was no significant difference in lot change of quality control material (p-value>0.05). Conclusion Variations between reagent lots are not large in immunoradiometric assays. It is likely that this is due to the selection of items that have relatively high detection rate in the immunoradiometric method and several remeasurements. In most test results, the difference was less than 10 percent, which was within the standard range. TSH control level 1 and PSA control level 1, which have low concentration target value, exceeded 10 percent more than twice, but it did not result in a value that was near 20 percent. As a result, it is required to perform a longer period of observation for more homogenized average results and to obtain laboratory-specific acceptance criteria for each item. Also, it is advised to study observations considering various variables.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.187-204
• /
• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Asian-Australasian Journal of Animal Sciences
• /
• v.27 no.2
• /
• pp.161-168
• /
• 2014

This study aimed to investigate dietary concentrate:forage ratios (C:F) and undegraded dietary protein (UDP) on nitrogen balance and urinary excretion of purine derivatives (PD) in lambs. Four Dorper${\times}$thin-tailed Han crossbred castrated lambs with $62.3{\pm}1.9$ kg body weight at 10 months of age were randomly assigned to four dietary treatments in a $2{\times}2$ factorial arrangement of two levels of C:F (40:60 and 60:40) and two levels of UDP (35% and 50% of CP), according to a complete $4{\times}4$ Latin-square design. Each experimental period lasted for 19 d. After a 7-d adaptation period, lambs were moved into individual metabolism crates for 12 d including 7 d of adaption and 5 d of metabolism trial. During the metabolism trial, total urine was collected for 24 h and spot urine samples were also collected at different times. Urinary PD was measured using a colorimetric method and creatinine was measured using an automated analyzer. Intake of dry matter (DM) (p<0.01) and organic matter (OM) (p<0.01) increased as the level of UDP decreased. Fecal N was not affected by dietary treatment (p>0.05) while urinary N increased as the level of UDP decreased (p<0.05), but decreased as dietary C:F increased (p<0.05). Nitrogen retention increased as dietary C:F increased (p<0.05). As dietary C:F increased, urinary excretion of PD increased (p<0.05), but was not affected by dietary UDP (p>0.05) or interaction between dietary treatments (p>0.05). Daily excretion of creatinine was not affected by dietary treatments (p<0.05), with an average value of $0.334{\times}0.005$ mmol/kg $BW^{0.75}$. A linear correlation was found between total PD excretion and PDC index ($R^2$ = 0.93). Concentrations of creatinine and PDC index in spot urine were unaffected by sampling time (p>0.05) and a good correlation was found between the PDC index (average value of three times) of spot urine and daily excretion of PD ($R^2$ = 0.88). These results suggest that for animals fed ad libitum, the PDC index in spot urine is effective to predict daily excretion of PD. In order to improve the accuracy of the spot sampling technique, an appropriate lag phase between the time of feeding and sampling should be determined so that the sampling time can coincide with the peak concentration of PD in the urine.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.93-110
• /
• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of the Korea Institute of Building Construction
• /
• v.9 no.5
• /
• pp.79-86
• /
• 2009

In a steel structure, spray-applied fire resistive materials are crucial in preventing structural strength from being weakened in the event of a fire. The quality control of such materials, however, is difficult for manual workers, who can frequently be in short supply. These skilled workers are also very likely to be exposed to environmental hazards. Problems with construction work such as this, which are specifically the difficulty of achieving quality control and the dangerous nature of the work itself, can be solved to some degree by the introduction of automated equipment. It is, however, very difficult to automate the work process, from operation to the selection of a location for the equipment, as the environment of a construction site has not yet been structured to accommodate automation. This is a fundamental study on the possibility of the automation of spray-applied fire resistive coating work. In this study, the linkability of the cutting-edge RTLS to an automation system is reviewed, and a scenario for the automation of spray-applied fire resistive coating work and system composition is presented. The system suggested in this study is still in a conceptual stage, and as such, there are many restrictions still to be resolved. Despite this fact, automation is expected to have good effectiveness in terms of preventing fire from spreading by maintaining a certain level of strength at a high temperature when a fire occurs, as it maintains the thickness of the fire-resistive coating at a specified level, and secures the integrity of the coating with the steel structure, thereby enhancing the fire-resistive performance. It also expected that if future research is conducted in this area in relation to a cutting-edge monitoring TRS, such as the ubiquitous sensor network (USN) and/or building information model (BIM), it will contribute to raising the level of construction automation in Korea, reducing costs through the systematic and efficient management of construction resources, shortening construction periods, and implementing more precise construction

• Korean Journal of Cognitive Science
• /
• v.28 no.4
• /
• pp.315-328
• /
• 2017

Even though assessment using information and communication technology will most likely lead the future of educational assessment, there is little domestic research on this topic. Computerized assessment will not only cut costs but also measure students' performance in ways not possible before. In this context, this study introduces a tool which can overcome the problems of multiple choice tests, which are most widely used type of assessment in current Korean educational setting. Multiple-choice tests, in which options are presented with the questions, are efficient in that grading can be automated; however, they allow for students who don't know the answer, to find the correct answer from the options. Park(2005) has developed a modified multiple-choice testing system (CMMT) using the interactivity of computers, that presents questions first, and options later for a short time when the student requests for them. The present study was conducted to find out if penalizing wrong answers could lower the possibility of students choosing an answer among the options when they don't know the correct answer. 116 students were tested with the directions that they will be penalized for wrong answers, but not for no response. There were 4 experimental conditions: 2 conditions of high or low percentage of penalizing, each in traditional multiple-choice or CMMT format. The results were analyzed using a two-way ANOVA for the number of no response, the test score and self-report score. Analysis showed that the number of no response was significantly higher for the CMMT format and that test scores were significantly lower when the penalizing percentage was high. The possibility of applying CMMT format tests while penalizing wrong answers in actual testing settings was addressed. In addition, the need for further research in the cognitive sciences to develop computerized assessment tools, was discussed.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.113-129
• /
• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.