• The Journal of Korean Academy of Prosthodontics
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• v.53 no.3
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• pp.207-214
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• 2015

Due to the limitations of conventional removable partial denture prostheses to treat a cleft lip & palate patient who shows scar tissue on upper lip, excessive absorption of the maxillary residual alveolar ridge, and class III malocclusion with narrow palate and undergrowth of the maxilla, 4 implants were placed on the maxillary edentulous region and a maxillary removable implant-supported partial denture was planned using a CAD/CAM milled titanium bar. Unlike metal or gold casting technique which has shrinkage after the molding, CAD/CAM milled titanium bar is highly-precise, economical and lightweight. In practice, however, it is very hard to obtain accurate friction-fit from the milled bar and reduction in retention can occur due to repetitive insertion and removal of the denture. Various auxiliary retention systems (e.g. $ERA^{(R)}$, $CEKA^{(R)}$, magnetics, $Locator^{(R)}$ attachment), in order to deal with these problems, can be used to obtain additional retention, cost-effectiveness and ease of replacement. Out of diverse auxiliary attachments, $Locator^{(R)}$ has characteristics that are dual retentive, minimal in vertical height and convenient of attachment replacement. Drill and tapping method is simple and the replacement of the metal female part of $Locator^{(R)}$ attachment is convenient. In this case, the $Locator^{(R)}$ attachment is connected to the milled titanium bar fabricated by CAD/CAM, using the drill and tapping technique. Afterward, screw holes were formed and 3 $Locator^{(R)}$ attachments were secured with 20 Ncm holding force for additional retention. Following this procedure, satisfactory results were obtained in terms of aesthetic facial form, masticatory function and denture retention, and I hereby report this case.

• Tuberculosis and Respiratory Diseases
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• v.44 no.2
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• pp.298-308
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• 1997

Background : Portable devices for measuring peak expiratory flow(PEF) are now of proved value in the diagnosis and management of asthma and many lightweight PEF meters have become available. However, it is necessary to determine whether peak expiratory flow rate(PEFR) measurements measured with peak flowmeters is accurate and reproducible for clinical application. The aim of the present study is to define accuracy, agreement, and precision of mini-Wright peak flow meter(MPFM) against standard pneumotachygraph. Methods : The lung function tests by standard pneumotachygraph and PEFR measurement by MPFM were performed in a random order for 2 hours in 22 normal and 17 asthmatic subjects and also were performed for 3 successive days in 22 normals. Results : The PEFR measured with MPFM was significantly related to the PEFR and $FEV_1$ measured with standard pneumotachygraph in normal and asthmatics(for PEFR, r = 0.92 ; p < 0.001 ; for $FEV_1$, r = 0.78 ; p < 0.001). The accuracy of MPFM was within 100(limits of accuracy recommeded by NAEP) in all the subjects or 22 normal, mean difference from standard pneumotachygraph being 16.5L/min(percentage of difference being 2.90%) or 10.6L/min(percentage of difference being 1.75%), respectively. According to the method proposed by Bland and Altman, the 95% limits of the distribution of differences between MPFM and standard pneumotachygraph after correction of PEFR using our regression equation were +38.2 and -71.5L/min in all the subjects or 20.49~+9.49L/min in 22 normal and was similar to the intraindividual agreements for 3 successive days in normal. There was no statistically significant difference of PEFR measured with MPFM and standard pneumotachygraph among three days(p > 0.05) and the coefficient of variation($2.4{\pm}1.2%$) of PEFR measured with MPFM was significantly lower than that($5.2{\pm}3.5%$) with standard pneumotachygraph in normal (p < 0.05). Conclusion : This results suggest that the MPFM was as accurate and reproducible as standard pneumotachygraph for monitoring of PEFR in the asthmatic subjects.

• Journal of the Korean Society of Radiology
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• v.13 no.2
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• pp.271-281
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• 2019

This study investigated the shielding efficiency of various types of shielding materials and measured the dose by organ using the phantom. Results of Shielding Efficiency Measurement Using Personal Radiation Meter. Among the various shielding materials, 1.1 mm RNS-TX composed of nano tungsten showed the highest shielding efficiency and 0.2 mm lead shielding showed the lowest shielding efficiency. 99mTc 30 mCi was exposed to the phantom for 120 minutes and the result of the measurement of the organs. 20.53 mSv without radiation protective clothing, 8.75 mSv when wearing 0.25 mm Pb protective clothing, 6.03 mSv when wearing 0.5 mm Pb protective clothing. 131I 2 mCi mCi was exposed to the phantom for 120 minutes and the result of the measurement of the organs. 7.71 mSv without radiation protective clothing, 4.88 mSv when wearing 0.25 mm Pb protective clothing, 2.79 mSv when wearing 0.5 mm Pb protective clothing. 18F 5 mCi was exposed to the phantom for 120 minutes and the result of the measurement of the organs. 16.39 mSv without radiation protective clothing, 15.84 mSv when wearing 0.25 mm Pb protective clothing, 12.52 mSv when wearing 0.5 mm Pb protective clothing. None of the radiation workers working in the nuclear medicine department exceeded the dose limit. However, when compared with other workers in the hospital, they showed a relatively high dose. Therefore, it is necessary to prepare measures to reduce and manage the dose of radiation workers in the nuclear medicine department through the wearing of radiation protective clothing made of lightweight, shielding material with good shielding efficiency, circulation task, task sharing, and substitution equipment such as auto dispenser.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.