Development of a teaching competency diagnostic tool for army military school instructors

The purpose of this study was to develop a teaching competency diagnosis tool for instructors working in army military schools to improve the quality of education and cultivate the competitiveness of the organization. To this end, previous research data related to the development of teaching competency diagnosis tools were collected and analyzed. Subsequently, focus group interviews and behavioral case interviews with excellent instructors at the Army Military School were conducted, and Delphi surveys were conducted with educational experts to organize teaching competency components and diagnostic tools temporarily. Next, to verify the validity of questionnaire items, a preliminary survey and a main survey were conducted with questionnaires administered to excellent instructors and ordinary instructors to confirm the validity and reliability of the teaching competency diagnosis tool. As a result of the study, four competencies (basic, class, organizational, and digital), 10 major competencies (attitude and attitude as educators, relationship formation, etc.), 25 sub-competencies (responsibility as instructors, initiative, etc.), and 65 behavioral indicators were selected. The diagnostic tools derived through this study are the competencies that instructors must have to perform their duties successfully in military schools. Therefore, our results not only provide information necessary for establishing instructor competency development policies but also contribute greatly to developing programs to strengthen teaching competency in military schools

Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF

Evaluating Acupuncture Point and Nonacupuncture Point Stimulation with EEG: A High-Frequency Power Spectrum Analysis

To identify physical and sensory responses to acupuncture point stimulation (APS), nonacupuncture point stimulation (NAPS) and no stimulation (NS), changes in the high-frequency power spectrum before and after stimulation were evaluated with electroencephalography (EEG). A total of 37 healthy subjects received APS at the LI4 point, NAPS, or NS with their eyes closed. Background brain waves were measured before, during, and after stimulation using 8 channels. Changes in the power spectra of gamma waves and high beta waves before, during, and after stimulation were comparatively analyzed. After NAPS, absolute high beta power (AHBP), relative high beta power (RHBP), absolute gamma power (AGP), and relative gamma power (RGP) tended to increase in all channels. But no consistent notable changes were found for APS and NS. NAPS is believed to cause temporary reactions to stress, tension, and sensory responses of the human body, while APS responds stably compared to stimulation of other parts of the body

First Report for the Seasonal and Annual Prevalence of Flea-Borne Bartonella from Rodents and Soricomorphs in the Republic of Korea

Rodents and soricomorphs are animal hosts of fleas and associated zoonotic microbial pathogens. A total of 4,889 small mammals were collected from Gyeonggi and Gangwon Provinces, Republic of Korea, from 2008 through 2010, including: Apodemus agrarius (4,122, 84.3%), followed by Crocidura lasiura (282, 5.8%), Microtus fortis (257, 5.3%), Myodes regulus (77, 1.6%), Micromys minutus (71, 1.5%), Mus musculus (63, 1.3%), and 4 other species (17, 0.3%). A total of 1,099 fleas belonging to 10 species and 7 genera were collected. Ctenophthalmus congeneroides (724, 65.9%) was the most commonly collected flea, followed by Stenoponia sidimi (301, 27.4%), Neopsylla bidentatiformis (29, 2.6%), and Rhadinopsylla insolita (25, 2.3%). The remaining species accounted for only 1.8% (20, range 1-6) of all fleas collected. The 2 dominant flea species, C. congeneroides and S. sidimi, showed an inverse seasonal pattern, with higher populations of C. congeneroides from January-September, whereas S. sidimi was more frequently collected during October-December. The overall flea infestation rates (FIR) and flea indices (FI) were 14.1% and 0.22, respectively, and were highest during April-June (19.7% and 0.30, respectively). A total of 735 of the 1,099 fleas were assayed for the detection of Bartonella spp. by PCR using Bartonella-specific primers, of which 515 were positive for Bartonella, with an overall maximum likelihood estimate (MLE) of 700.7/1,000. The highest MLE values were observed during April-June (899.2) and July-September (936.2) trapping periods and, although lower, were similar for January-March (566.7) and October-December (574.1). C. congeneroides demonstrated high MLEs for all seasons (range 752.5-934.8), while S. sidimi was positive for Bartonella only during January-March (MLE = 342.1) and October-December (MLE = 497.2) collection periods. Continued long-term surveillance of small mammals and associated ectoparasites is needed to improve our understanding of the prevalence of Bartonella spp. in fleas and the role of fleas in the zoonotic maintenance and transmission of Bartonella to humans.OAIID:oai:osos.snu.ac.kr:snu2013-01/102/0000051105/5SEQ:5PERF_CD:SNU2013-01EVAL_ITEM_CD:102USER_ID:0000051105ADJUST_YN:YEMP_ID:A077262DEPT_CD:551CITE_RATE:2.277FILENAME:2013 vbz 13(7)457-467 flea-borne bartonella.pdfDEPT_NM:수의학과SCOPUS_YN:YCONFIRM:

Research on Effective Advertising Types in Virtual Environment

Virtual reality (VR) applies various types of advertisements (ads) to promote brands while collaborating with companies. This study aims to present effective advertisement types by verifying user responses in a VR environment. First, by analyzing the cases of advertisements with immersive content, the types of advertisements in VR were defined as avatar costumes, products, and wall posters. The user response was measured in two categories: gaze response measured by the eye-tracking VR advertisement monitoring system (EVAMS) and the advertisement effect analyzed through surveys. As a result of analyzing the user responses, the avatar costumes among the advertisement types caused the highest visual attention and advertisement effect. In addition, by analyzing the correlation between visual attention and the advertisement effect, it was observed that there was a positive relationship between the number of fixations and advertisement attention, fixation time, and advertisement recall. Thus, it was confirmed that the higher the number of fixations and the longer the fixation time, the more positively an advertisement was affected. In addition, it is expected that the results of this study can be used as a reference for effective advertisement directing in VR content development and advertisement directing and processing

Learning Cultural Spaces: A Collaborative Creation of a Virtual Art Museum Using Roblox

This study proposes education on creating virtual art museums using metaverse technology to broaden the understanding of art museums. It investigates the effect of creating a virtual art museum using Roblox Studio, a metaverse platform, on the acquisition of knowledge about art museums, artists, and artworks and the ability to create metaverse content. This study selected the Moonshin Art Museum (MAM), the art museum to be created in the metaverse space, as its local cultural space. Fifteen students participated in a creation workshop to learn to create virtual spatial content using Roblox Studio and also visited an actual art museum. The students were then assigned to architecture, artwork, avatar, and content teams and collaborated with one another. To evaluate the activities, students filled out pre- and post-questionnaires containing items about the MAM, the sculptor Moonshin, and Moonshin’s artwork, as well as the ability to produce metaverse content. Findings showed that creating a virtual art museum through a metaverse platform facilitates the acquisition of spatial knowledge about art museums as well as information about artists and artworks. In addition, collaboration not only helped in the creation task but also identified and solved technical difficulties and improved creative abilities. These results suggest possibilities for using metaverse technology in delivering education regarding virtual art museums

Anisotropic diffusion noise filtering using region adaptive smoothing strength

This paper presents an improved anisotropic diffusion method using region adaptive smoothing strength. Unlike existing methods, the proposed method uses an adaptive classifier to find a good estimate of the optimal smoothing strength for each iteration to consider the varying noise characteristics. Further, when. training the classifiers, the usefulness of the training data is verified and less useful data are excluded to avoid degraded training results, thereby generating robust and improved denoising performance. For reduction of the computational complexity, this paper also proposes a simple region analysis technique. Consequently, the proposed method is appropriate for the devices that have relatively small computing power. Experimental results confirm that the proposed method outperforms AD-based benchmark methods by increased peak signal-to-noise ratio up to 2.37 dB and structural similarity up to 0.0557 for 10% noise level. (C) 2016 Elsevier Inc. All rights reserved.1122sciescopu

Real-time stereo matching using extended binary weighted aggregation

This paper presents an accurate real-time stereo matching method, which is based on the extended binary weighted aggregation. The accuracy of the proposed stereo matching method was significantly enhanced by extending its binary weighted aggregation so that remote connections of support regions can be allowed for aggregation. The extended binary weighted aggregation is based on the following two new ideas. First, the extended binary weighted aggregation connects distant regions over color boundaries, making them one large support region for a given pixel. This approach induces more aggregation targets, and, thus, makes the aggregation step more robust. Second, it excludes cost outliers in the support region to prevent them from being propagated during the aggregation step, making a quality support region. With the extended binary weighted aggregation, the proposed stereo matching method obtains more accurate disparity maps than existing stereo matching methods using binary weighted aggregation methods, while maintaining the speed advantage of binary weighted aggregation. Experimental results illustrated that the proposed stereo matching method outperformed all existing real-time stereo matching methods in terms of accuracy, providing the average bad pixel rate of 5.12%, for the Middlebury stereo test images. The proposed stereo matching method was implemented on a CUDA platform with a high-end GPU. The implemented system operated at up to 300 fps for the stereo images with 320 x 240 pixel resolution and a disparity range of 32 pixels. (C) 2016 Elsevier Inc. All rights reserved.1133sciescopu