• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.581-588
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• 2019

MRI is superior when contrasted to help the organization generate artifacts resolution, but also affect the diagnosis and create a image that can not be read. Metal is inserted into the tooth, it is necessary to often be inhibited in imaging by causing the geometric distortion due to the majority and if the difference between the magnetic susceptibility of a ferromagnetic material or paramagnetic reducing them. The purpose of this study is to conduct a metal artefact in accordance with the analysis using a diamagnetic material. The magnetic material include a wire for the orthodontic bracket and a stainless steel was used as a diamagnetic material was used copper, zinc, bismuth. Testing equipment is sequenced using 1.5T, 3T was used was measured using a SE, TSE, GE, EPI. A self-produced phantom material was used for agarose gel (10%) to a uniform signal artifacts causing materials are stainless steel were tested by placing in the center of the phantom and cover inspection of the positive cube diamagnetic material of 10mm each length.After a measurement artefact artifact zone settings area was calculated using the Wand tool After setting the Low Threshold value of 10 in the image obtained by subtracting images, including magnetic material from a pure tool phantom images using Image J. Metal artifacts occur in stainless steel metal artifact reduction was greatest in the image with the bismuth diamagnetic materials of copper and zinc is slightly reduced, but the difference in degree will not greater. The reason for this is thought to be due to hayeotgi offset most of the susceptibility in bismuth diamagnetic susceptibility of most small ferromagnetic. Most came with less artifacts in image of bismuth in both 1.5T and 3T. Sequence-specific artifact reduction was most reduced artifacts from the TSE 1.5T 3T was reduced in the most artifacts from SE. Signal-to-noise ratio was the lowest SNR is low, appears in the implant, the 1.5T was the Implant + Bi Cu and Zn showed similar results to each other. Therefore, the results of artifacts variation of diamagnetic material, magnetic susceptibility (${\chi}$) is the most this shows the reduced aspect lower than the implant artificial metal artifacts criteria in the video using low bismuth susceptibility to low material the more metal artifacts It was found that the decrease. Therefore, based on the study on the increase, the metal artifacts reduction for the whole, as well as dental prosthesis future orthodontic materials in a way that can even reduce the artifact does not appear which has been pointed out as a disadvantage of the solutions of conventional metal artifact It is considered to be material.

• Journal of Life Science
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• v.29 no.1
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• pp.1-8
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• 2019

In Hanwoo cattle (Korean native cattle), there is a scarcity of comparative analysis papers using highdepth sequencing and haplotype phasing, particularly a comparative analysis of the Truseq Synthetic Long-Read Haplotyping sequencing platform serviced by Illumina (TSLRH) versus the Chromium Genome Sequencing platform serviced by 10X Genomics (10XG). DNA was extracted from the sperm of a Hanwoo breeding bull (ID: TN1505D2184/27214) provided by Hanwoo research canter and used for the generation of sequence data from both the sequencing platforms. We then identified SNVs using an appropriate analysis pipeline tailored for each platform. The TSLRH and 10XG platforms generated a total of 355,208,304 and 1,632,772,004 reads, respectively, corresponding to a Q30 (%) of 89.04% and 88.60%, respectively, of which 351,992,768(99.09%) and 1,526,641,824(93.50%) were successfully mapped. For the TSLRH and 10XG platforms, the mean depth of the sequencing was 13.04X and 74.3X, the longest phase block was 1,982,706 bp and 1,480,081 bp, the N50 phase block was 57,637 bp and 114,394 bp, the total number of SNVs identified was 4,534,989 and 8,496,813, and the total phased rate was 72.29% and 87.67%, respectively. Moreover, for each chromosome, we identified unique and common SNVs using both sequencing platforms. The number of SNVs was directly proportional to the length of the chromosome. Based on our results, we recommend the use of the 10XG platform for haplotype phasing and SNV identification, as it generated a longer N50 phase block, in addition to a higher mean depth, total number of reads, total number of SNVs, and phase rate, than the TSLRH platform.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.49-61
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• 2009

This article provides training exercises for executives into interpreting subroutine maps of executives' thinking in processing business and industrial marketing problems and opportunities. This study builds on premises that Schank proposes about learning and teaching including (1) learning occurs by experiencing and the best instruction offers learners opportunities to distill their knowledge and skills from interactive stories in the form of goal.based scenarios, team projects, and understanding stories from experts. Also, (2) telling does not lead to learning because learning requires action-training environments should emphasize active engagement with stories, cases, and projects. Each training case study includes executive exposure to decision system analysis (DSA). The training case requires the executive to write a "Briefing Report" of a DSA map. Instructions to the executive trainee in writing the briefing report include coverage in the briefing report of (1) details of the essence of the DSA map and (2) a statement of warnings and opportunities that the executive map reader interprets within the DSA map. The length maximum for a briefing report is 500 words-an arbitrary rule that works well in executive training programs. Following this introduction, section two of the article briefly summarizes relevant literature on how humans think within contexts in response to problems and opportunities. Section three illustrates the creation and interpreting of DSA maps using a training exercise in pricing a chemical product to different OEM (original equipment manufacturer) customers. Section four presents a training exercise in pricing decisions by a petroleum manufacturing firm. Section five presents a training exercise in marketing strategies by an office furniture distributer along with buying strategies by business customers. Each of the three training exercises is based on research into information processing and decision making of executives operating in marketing contexts. Section six concludes the article with suggestions for use of this training case and for developing additional training cases for honing executives' decision-making skills. Todd and Gigerenzer propose that humans use simple heuristics because they enable adaptive behavior by exploiting the structure of information in natural decision environments. "Simplicity is a virtue, rather than a curse". Bounded rationality theorists emphasize the centrality of Simon's proposition, "Human rational behavior is shaped by a scissors whose blades are the structure of the task environments and the computational capabilities of the actor". Gigerenzer's view is relevant to Simon's environmental blade and to the environmental structures in the three cases in this article, "The term environment, here, does not refer to a description of the total physical and biological environment, but only to that part important to an organism, given its needs and goals." The present article directs attention to research that combines reports on the structure of task environments with the use of adaptive toolbox heuristics of actors. The DSA mapping approach here concerns the match between strategy and an environment-the development and understanding of ecological rationality theory. Aspiration adaptation theory is central to this approach. Aspiration adaptation theory models decision making as a multi-goal problem without aggregation of the goals into a complete preference order over all decision alternatives. The three case studies in this article permit the learner to apply propositions in aspiration level rules in reaching a decision. Aspiration adaptation takes the form of a sequence of adjustment steps. An adjustment step shifts the current aspiration level to a neighboring point on an aspiration grid by a change in only one goal variable. An upward adjustment step is an increase and a downward adjustment step is a decrease of a goal variable. Creating and using aspiration adaptation levels is integral to bounded rationality theory. The present article increases understanding and expertise of both aspiration adaptation and bounded rationality theories by providing learner experiences and practice in using propositions in both theories. Practice in ranking CTSs and writing TOP gists from DSA maps serves to clarify and deepen Selten's view, "Clearly, aspiration adaptation must enter the picture as an integrated part of the search for a solution." The body of "direct research" by Mintzberg, Gladwin's ethnographic decision tree modeling, and Huff's work on mapping strategic thought are suggestions on where to look for research that considers both the structure of the environment and the computational capabilities of the actors making decisions in these environments. Such research on bounded rationality permits both further development of theory in how and why decisions are made in real life and the development of learning exercises in the use of heuristics occurring in natural environments. The exercises in the present article encourage learning skills and principles of using fast and frugal heuristics in contexts of their intended use. The exercises respond to Schank's wisdom, "In a deep sense, education isn't about knowledge or getting students to know what has happened. It is about getting them to feel what has happened. This is not easy to do. Education, as it is in schools today, is emotionless. This is a huge problem." The three cases and accompanying set of exercise questions adhere to Schank's view, "Processes are best taught by actually engaging in them, which can often mean, for mental processing, active discussion."

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.