• Restorative Dentistry and Endodontics
• /
• v.24 no.2
• /
• pp.299-309
• /
• 1999

The composite resin, due to its esthetic qualities, is considered the material of choice for restoration of anterior teeth. With respect to shade control, the direct-placement resin composites offer some distinct advantages over indirect restorative procedures. Visible-light-cured (VLC) composites allow dentists to match existing tooth shades or to create new shades and to evaluate them immediately at the time of restoration placement. Optimal intraoral color control can be achieved if optical changes occurring during application are minimized. An ideal VLC composite, then, would be one which is optically stable throughout the polymerization process. The shade guides of the resin composites are generally made of plastic, rather than the actual composite material, and do not accurately depict the true shade, translucency, or opacity of the resin composite after polymerization. So the numerous problems associated with these shade guides lead to varied and sometimes unpredictable results. The aim of this study was to assess the color changes of current resin composite restorative materials which occur as a result of the polymerization process and to compare the color differences between the shade guides provided with the products and the actual resin composites before- and after-polymerization. The results obtained from this investigation should provide the clinician with information which may aid in improved color match of esthetic restoration. Five light activated, resin-based materials (${\AE}$litefil, Amelogen Universal, Spectrum TPH VeridonFil-Photo, and Z100) and shade guides were used in this study. Three specimens of each material and shade combination were made. Each material was condensed inside a 1.5mm thick metal mold with 10mm diameter and pressed between glass plates. Each material was measured immediately before polymerization, and polymerized with Curing Light XL 3000 (3M Dental products, USA) visible light-activation unit for 60 seconds at each side. The specimens were then polished sequentially on wet sandpaper. Shade guides were ground with polishing stones and rubber points (Shofu) to a thickness of approximately 1.5mm. Color characteristics were performed with a spectrophotometer (CM-3500d, Minolta Co., LTD). A computer-controlled spectrophotometer was used to determine CIELAB coordinates ($L^*$, $a^*$ and $b^*$) of each specimen and shade guide. The CIELAB measurements made it possible to evaluate the amount of the color difference values (${\Delta}E{^*}ab$) of resin composites before the polymerization process and shade guides using the post-polishing color of the composite as a control, CIE standard D65 was used as the light source. The results were as follows. 1. Each of the resin composites evaluated showed significant color changes during light-curing process. All the resin composites evaluated except all the tested shades of 2100 showed unacceptable level of color changes (${\Delta}E{^*}ab$ greater than 3.3) between pre-polymerization and post-polishing state. 2. Color differences between most of the resin composites tested and their corresponding shade guides were acceptable but those between C2 shade of ${\AE}$litefil and IE shade of Amelogen Universal and their respective shade guides exceeded what is acceptable. 3. Comparison of the mean ${\Delta}E{^*}ab$ values of materials revealed that Z100 showed the least overall color change between pre-polymerization and post-polishing state followed by ${\AE}$litefil, VeridonFil-Photo, Spectrum TPH, and Amelogen Universal in the order of increasing change and Amelogen Universal. Spectrum TPH, 2100, VeridonFil-Photo and ${\AE}$litefil for the color differences between actual resin and shade guide. 4. In the clinical environment, the shade guide is the better choice than the shade of the actual resin before polymerization when matching colors. But, it is recommended that custom shade guides be made from resin material itself for better color matching.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.1-23
• /
• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• The Korean Journal of Nuclear Medicine
• /
• v.36 no.4
• /
• pp.244-254
• /
• 2002

Purpose: This study investigated the differences between technetium-99m ethyl cysteinate dimer (Tc-99m ECD) and technetium-99m hexamethylpropylene amine oxime (Tc-99m HMPAO) uptake in the normal brain by means of statistical parametric mapping (SPM) analysis. Materials and Methods: We retrospectively analyzed age and sex matched 53 cases of normal brain SPECT. Thirty-two cases were obtained with Tc-99m ECD and 21 cases with Tc-99m HMPAO. There were no abnormal findings on brain MRIs. All of the SPECT images were spatially transformed to standard space, smoothed and globally normalized. The differences between the Tc-99m ECD and Tc-99m HMPAO SPECT images were statistically analyzed using statistical parametric mapping (SPM'99) software. The differences bgetween the two groups were considered significant ant a threshold of corrected P values less than 0.05. Results: SPM analysis revealed significantly different uptakes of Tc-99m ECD and Tc-99m HMPAO in the normal brains. On the Tc-99m ECD SPECT images, relatively higher uptake was observed in the frontal, parietal and occipital lobes, in the basal ganglia and thalamus, and in the superior region of the cerebellum. On the Tc-99m HMPAO SPECT images, relatively higher uptakes was observed in subcortical areas of the frontal region, temporal lobe, and posterior portion of inferior cerebellum. Conclusion: Uptake of Tc-99m ECD and Tc-99m HMPO in the normallooking brain was significantly different on SPM analysis. The selective use of Tc-99m ECD of Tc-99m HMPAO in brain SPECT imaging appears especially valuable for the interpretation of cerebral perfusion. Further investigation is necessary to determine which tracer is more accurate for diagnosing different clinical conditions.