EFFECT OF APPLICATION METHODS OF A SELF-ETCHING PRIMER ADHESIVE SYSTEM ON ENAMEL BOND STRENGTH (자가부식 프라이머 접착제의 적용방식이 법랑질의 결합강도에 미치는 영향)
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- Restorative Dentistry and Endodontics
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- v.33 no.2
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- pp.90-97
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- 2008
The purpose of this study was to evaluate the effect of passive or active application of primer and coat times of bond on the shear bond strength when a self-etching primer adhesive (Clearfil SE Bond) was applied to enamel surface. Crowns of sixteen human molars were selected. Buccal and lingual enamels of crowns were partially exposed and slabs of 1.2 mm thick were made. They were divided into one of four equal groups (n = 8). Group 1: passive application of Primer and 1 coat of Bond, Group 2: active application of Primer and 1 coat of Bond, Group 3: passive application of Primer and 2 coats of Bond, Group 4: active application of Primer and 2 coats of Bond. Clearfil AP-X was bonded to enamel suface of each group using Tygon tubes. The bonded specimens were subjected to microshear bond strength (uSBS) testing with a crosshead speed of 1 mm/min. The results of this study were as follows; 1. The uSBS of Group 1 was the lowest among groups and the uSBS of Group 4 was the highest. 2. There was not statistically significant interaction between enamel uSBS by application method of Primer and coat time of Bond (p > 0.05). 3. There was not statistically significant difference between enamel uSBS by passive and active application of Primer (p > 0.05). 4. There was statistically significant difference between enamel uSBS by one- and two-coat of Bond (p < 0.05).
Purpose: The purpose of this study is to compare the effect of two fiber post systems and one metal cast post system on the fracture strength and fracture pattern of crowned, endodontically treated teeth with 2 mm-height of the reamining tooth structure. Materials and methods: A total of 36 recently extracted sound human mandibular premolars were selected Each tooth structure of the crown portion except 2mm-height of the one above the cementoenamel junction was removed. After being endodontically treated, they were randomly distributed into 3 groups: group 1, restored with quarts fiber post(D.T. Light-Post), group 2, with glass fiber post(FRC Postec), and group 3, metal cast post and core. All teeth were fully covered with nonprecious metal crowns. Each specimen was embedded in an acrylic resin block and then secured in a universal load-testing machine. A compressive load was applied at a 130 degree angle to the long axis of the tooth until fractured, at a crosshead speed 20mm/min. The highest fracture loads were measured and recorded as the fracture strength of each specimen. Fracture areas were measured on the mid-buccal and mid-lingual point from the crown margins. One-way analysis of variance and Turkey test were used to determine the statistic significance of the different fracture loads and areas among the groups (p<0.05). Results: The mean fracture loads were
The purpose of this in vitro study was to evaluate the efficacy of self-etching primer which was developed to simplify the bonding procedures by measuring the shear bond strength and observing the interfacial morphology. 90 flat dentinal surfaces were prepared by grinding the buccal and lingual areas of caries-free human deciduous molars. After bonding of composite resin to sample surfaces according to the manufacturer's direction and thermocycling, shear bond strengths were measured using Universal testing machine(Instron). Another groups of specimens were treated by hydrochloric acid to secure the resin only and those tags were evaluated under SEM for their length and forms and the morphology of the bonding sites were also observed. The result as follows. 1. Group III showed higher shear bond strength than group I and II but no statistically significant difference was founded between group I and II(p>.05). 2. Adhesive failure was predominant in group II whereas dentin detachment was the main failure pattern in group I and III. 3. Relating long resin tags of
Purpose: This study measured and analyzed the PTO (power take off) torque of a transplanter according to the planting conditions during field operation. Methods: A torque measurement system was constructed with torque sensors to measure the torque of a PTO shaft, a measurement device to acquire sensor signals, and a power controller to provide power for a laptop computer. The field operation was conducted at four planting distances (26, 35, 43, and 80 cm) and two planting depths using the transplanter on a field with similar soil conditions. One-way ANOVA with planting distance and Duncan's multiple range test at a significance level of 0.05 were used to analyze the PTO torque. The torque ratio was calculated based on the minimum torque using the average PTO torque measured under each planting condition. Results: The average torques on the PTO shaft for planting distances of 26, 35, 43, and 80 cm at a low planting depth were 11.05, 9.07, 7.04, and 3.75 Nm, respectively; the same for planting distances of 26, 35, 43, and 80 cm at a middle planting depth were 12.20, 9.86, 7.94, and 4.32 Nm, respectively. When the planting distance decreased by 43, 35, and 26 cm, the torque ratio at a low planting depth increased by 88, 142, and 195%, respectively. When the planting distance decreased by 43, 35, and 26 cm, the torque ratio at the middle planting depth increased by 84, 128, and 182%, respectively. Conclusions: PTO torque fluctuated by planting distance and depth. Moreover, the PTO torque increased for short planting distances. Therefore, farmers should determine the planting conditions of the transplanter by considering the load and durability of the machine. The results of this study provide useful information pertaining to the optimum PTO design of the transplanter considering the field load.
Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R2, RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R2, RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM2.5.
Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.
From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.
With the breakthrough of speech recognition technology, conversational agents have become pervasive through smartphones and smart speakers. The recognition accuracy of speech recognition technology has developed to the level of human beings, but it still shows limitations on understanding the underlying meaning or intention of words, or understanding long conversation. Accordingly, the users experience various errors when interacting with the conversational agents, which may negatively affect the user experience. In addition, in the case of smart speakers with a voice as the main interface, the lack of feedback on system and transparency was reported as the main issue when the users using. Therefore, there is a strong need for research on how users can better understand the capability of the conversational agents and mitigate negative emotions in error situations. In this study, we applied social strategies, "forewarning" and "apology", to conversational agent and investigated how these strategies affect users' perceptions of the agent in breakdown situations. For the study, we created a series of demo videos of a user interacting with a conversational agent. After watching the demo videos, the participants were asked to evaluate how they liked and trusted the agent through an online survey. A total of 104 respondents were analyzed and found to be contrary to our expectation based on the literature study. The result showed that forewarning gave a negative impression to the user, especially the reliability of the agent. Also, apology in a breakdown situation did not affect the users' perceptions. In the following in-depth interviews, participants explained that they perceived the smart speaker as a machine rather than a human-like object, and for this reason, the social strategies did not work. These results show that the social strategies should be applied according to the perceptions that user has toward agents.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70