• PNF and Movement
• /
• v.20 no.2
• /
• pp.189-201
• /
• 2022

Purpose: The purpose of this study was to investigate the effect of McConnell taping and Kinesio taping on pain and lower extremity joint angles when patients with patellofemoral pain syndrome (PFPS) ascend stairs. Methods: Fifty young adults who were experiencing anterior knee pain due to PFPS were selected as participants. Then, 25 patients were randomly assigned to the McConnell taping group and 25 to the Kinesio taping group. Pain and lower extremity joint angle were measured while ascending stairs before and after the intervention. A paired t-test was performed to evaluate the amount of change in the parameter values after the intervention within the groups, and an independent t-test was used to compare the results of the groups. Results: In the within-group comparisons, a statistically significant difference was found in both groups between the anterior knee pain scale scores recorded before and after the intervention (p < 0.05). A statistically significant difference was also found between the groups (p < 0.05). Comparison of the lower extremity joint angles at initial contact, loading response, terminal stance, and pre-swing within the groups showed that there were statistically significant differences in the hip, knee flexion, abduction, lateral rotation, and dorsiflexion angles in both the McConnell and Kinesio taping groups (p < 0.05). There was also a statistically significant difference in all angles between the groups during the following events (p < 0.05): (1) at initial contact, (2) at loading response (except hip flexion angle), (3) at terminal stance (except hip flexion and lateral rotation angles), and (4) at pre-swing (except hip, knee abduction, and inversion angles). Conclusion: McConnell taping and Kinesio taping both effectively improved the occurrence of knee pain and the lower extremity joint angles during stair ascent in patients with PFPS. However, McConnell taping had a significant impact on pain reduction and lower extremity joint angles compared to Kinesio taping.

• Journal of Periodontal and Implant Science
• /
• v.52 no.4
• /
• pp.338-350
• /
• 2022

Purpose: Various studies have investigated 3-dimensional (3D)-printed implants using Ti6Al-4V powder; however, multi-root 3D-printed implants have not been fully investigated. The purpose of this study was to explore the stability of multirooted 3D-printed implants with lattice and solid structures. The secondary outcomes were comparisons between the 2 types of 3D-printed implants in micro-computed tomographic and histological analyses. Methods: Lattice- and solid-type 3D-printed implants for the left and right mandibular third premolars in beagle dogs were fabricated. Four implants in each group were placed immediately following tooth extraction. Implant stability measurement and periapical X-rays were performed every 2 weeks for 12 weeks. Peri-implant bone volume/tissue volume (BV/TV) and bone mineral density (BMD) were measured by micro-computed tomography. Bone-to-implant contact (BIC) and bone area fraction occupancy (BAFO) were measured in histomorphometric analyses. Results: All 4 lattice-type 3D-printed implants survived. Three solid-type 3D-printed implants were removed before the planned sacrifice date due to implant mobility. A slight, gradual increase in implant stability values from implant surgery to 4 weeks after surgery was observed in the lattice-type 3D-printed implants. The marginal bone change of the surviving solid-type 3D-printed implant was approximately 5 mm, whereas the value was approximately 2 mm in the lattice-type 3D-printed implants. BV/TV and BMD in the lattice type 3D-printed implants were similar to those in the surviving solid-type implant. However, BIC and BAFO were lower in the surviving solid-type 3D-printed implant than in the lattice-type 3D-printed implants. Conclusions: Within the limits of this preclinical study, 3D-printed implants of double-rooted teeth showed high primary stability. However, 3D-printed implants with interlocking structures such as lattices might provide high secondary stability and successful osseointegration.

• Journal of The Korean Society of Integrative Medicine
• /
• v.10 no.3
• /
• pp.85-94
• /
• 2022

Purpose : This study aimed to investigate the effects of 12 weeks of resistance exercise on body composition, physical strength, blood lipids, and insulin. Methods : The study was conducted on 24 elderly women divided into two groups: 12 subjects in an exercise group and 12 subjects in a control group. Resistance exercise was performed for 50 minutes a day, three times a week, for the duration of 12 weeks, and body composition, physical strength, blood lipids, and insulin were measured before and after the subjects completed the program. For the statistical analysis, the mean and standard deviation (M±SD) of each variable were calculated using SPSS version 20, and a paired t-test and two-way repeated ANOVA were conducted to test for the differences before and after the resistance exercise. All significant levels were set to α=.05 as a result of the experiment. Results : Changes in body composition after the 12-week resistance exercise program did not show any significant difference based on the comparison between the groups, but when noting the values for body fat percentage and body in the control group before and after, a significant difference was shown in fat mass (p<.05). As for changes in physical fitness, significant differences appeared in flexibility, muscle strength, and stenotic force (p<.01) when the groups were compared. Regarding pre- and post-values within each group concerning flexibility within the exercise group, significant differences were shown in gender (p<.001), muscle strength (p<.05), (p<.01), muscle earth strength, equilibrium (p<.01), stenosis force, and cardiopulmonary earth force (p<.001). Also, comparisons between populations in changes in blood lipids the values before and after in each group, significant differences in glucose (p<.05) and insulin (p<.05) were shown in the exercise group. When comparing the values before and after in each population, a significant difference was shown in the control group (p<.05). Conclusion : When all the results were integrated, the 12-week resistance exercise program was found to enhance physical strength (flexibility, muscle strength, and coordination) and improve the blood sugar levels of elderly women. In particular, resistance exercise is believed to lower the prevalence of obesity, type 2 diabetes mellitus, and metabolic diseases by having a positive effect on insulin. Further studies are suggested to verify the effect on body composition and blood lipids by setting up a variety of exercise treatment methods (including subjects, exercise periods, exercise plans, and exercise intensity focuses).

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.39 no.5
• /
• pp.489-494
• /
• 1994

Near Infrared Reflectance Spectroscopy(NIRS) has been used as a tool for the rapid, accurate, protein assay of malting barley. NIRS used in this study was filter type instruments, Neotec 102. The objective of this study was to obtain the best calibration equation, for the rapid, ease and accurate protein content analysis of malting barley using NIRS system. The optimum wavelength for protein content analysis used NIRS were 2095nm, 2095/1941nm, 2095/1941/2282nm, 2905/1941/2282/2086nm, respectively. Mean protein content with this calibration equation in NIRS analysis was 10.59%, while 10.60% in Micro-Kjeldahl one. The range of protein content in Micro-Kjeldahl was 8.66~12.66% and that in NIRS was 8.80~12.35%. When 18 other varieties produced in 1992 were analysed with 2095nm, 2095/1941nm, 2095/1941/2282nm, 2095/1941/2282/2086nm equation, standard deviation of difference (SDD)and standard error of performence(SEP) and $R^2$ values were 0.47, 0.43, 0.95, respectively. Both the mean protein content by Micro-Kjeldahl and by NIRS was 10.25%. With this equation, analysied 31 varities produced in 1993, SDD and SEP and r values were 0.69, 0.67, 0.91, respectively, and that bias value was 0.65. In this analysis, mean protein content by Micro-Kjeldahl was 10.17% and by NIRS was 10.81%. The range of protein content in Micro-Kjeldahl was 7.58~14.29%, What that in NIRS was 8.63~13.93%. After adjusted bias in the best calibration equation, mean protein content of Micro-Kjeldahl was 10.17% and that of NIRS was 10.09%, without variance of SDD, SEP and r values.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.6
• /
• pp.132-145
• /
• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• The Korean Journal of Applied Statistics
• /
• v.36 no.2
• /
• pp.141-166
• /
• 2023

This paper investigates synthetic data generation methods and their evaluation measures. There have been increasing demands for releasing various types of data to the public for different purposes. At the same time, there are also unavoidable concerns about leaking critical or sensitive information. Many synthetic data generation methods have been proposed over the years in order to address these concerns and implemented in some countries, including Korea. The current study aims to introduce and compare three representative synthetic data generation approaches: Sequential regression, nonparametric Bayesian multiple imputations, and deep generative models. Several evaluation metrics that measure the utility and disclosure risk of synthetic data are also reviewed. We provide empirical comparisons of the three synthetic data generation approaches with respect to various evaluation measures. The findings of this work will help practitioners to have a better understanding of the advantages and disadvantages of those synthetic data methods.

• Journal of Technology Innovation
• /
• v.31 no.2
• /
• pp.111-133
• /
• 2023

Innopolis start-up has become a representative model and path for commercialization of public technology. Along with the quantitative growth of innopolis start-up, the importance of innopolis start-up in national policies and institutional strategies related to public technology commercialization has also increased. However, over the past 16 years, innopolis start-up's establishment and growth have taken place in different ways at different times. This study aims to compare and analyze changes in innopolis start-up over the past 16 years, focusing on comparisons before and after 2014, when the establishment of innopolis start-up began to increase rapidly. Main findings are as follows. First, in the early stage of the quantitative expansion period, policy changes related to innopolis start-up were the main factors for the increase in innopolis start-ups. In addition, the rapid increase in the establishment of innopolis start-up after 2016 was largely influenced by changes in the start-up environment and institutional changes related to innopolis start-up. Second, the time of registration and size of the capital of innopolis start-up had a statistically significant effect on the sales for 3 years after registration. This result shows that with the rapid increase in innopolis start-ups, the need to build a customized support system for innopolis start-ups by size or growth stage has increased.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.26 no.3
• /
• pp.12-36
• /
• 2023

Graph databases, which store different types of data and their relationships modeled as a graph, can be effective in managing and analyzing real estate spatial data linked by complex relationships. However, they are not widely used due to the limited spatial functionalities of graph databases. In this study, we propose a uniform grid-based real estate spatial data management approach using a graph database to respond to various real estate-related spatial questions. By analyzing the real estate community to identify relevant data and utilizing national point numbers as unit grids, we construct a graph schema that linking diverse real estate data, and create a test database. After building a test database, we tested basic topological relationships and spatial functions using the Jackpine benchmark, and further conducted query tests based on various scenarios to verify the appropriateness of the proposed method. The results show that the proposed method successfully executed 25 out of 29 spatial topological relationships and spatial functions, and achieved about 97% accuracy for the 25 functions and 15 scenarios. The significance of this study lies in proposing an efficient data integration method that can respond to real estate-related spatial questions, considering the limited spatial operation capabilities of graph databases. However, there are limitations such as the creation of incorrect spatial topological relationships due to the use of grid-based indexes and inefficiency of queries due to list comparisons, which need to be improved in follow-up studies.

• Journal of the Korea Society for Simulation
• /
• v.32 no.3
• /
• pp.93-108
• /
• 2023

In manufacturing productivity analysis, understanding the intricate interplay among factors like facility performance, layout design, and workforce allocation within the production line is imperative. This paper introduces a simulation-based methodology tailored to food manufacturing, progressively expanding scenarios to analyze production enhancement. The target system is a food processing plant, encompassing production processes, including warehousing, processing, subdivision, packaging, inspection, loading, and storage. First, we analyze the target system and design a simulation model according to the actual layout arrangement of equipment and workers. Then, we validate the developed model reflecting the real data obtained from the target system, such as the workers' working time and the equipment's processing time. The proposed model aims to identify optimal factor values for productivity gains through incremental scenario comparisons. To this end, three stages of simulation experiments were conducted by extending the equipment and worker models of the subdivision and packaging processes. The simulation experiments have shown that productivity depends on the placement of skilled workers and the performance of the packaging machine. The proposed method in this study will offer combinations of factors for the specific production requirements and support optimal decision-making in the real-world field.

• Geophysics and Geophysical Exploration
• /
• v.26 no.3
• /
• pp.114-125
• /
• 2023

The physics-informed neural network (PINN) has been proposed to overcome the limitations of various numerical methods used to solve partial differential equations (PDEs) and the drawbacks of purely data-driven machine learning. The PINN directly applies PDEs to the construction of the loss function, introducing physical constraints to machine learning training. This technique can also be applied to wave equation modeling. However, to solve the wave equation using the PINN, second-order differentiations with respect to input data must be performed during neural network training, and the resulting wavefields contain complex dynamical phenomena, requiring careful strategies. This tutorial elucidates the fundamental concepts of the PINN and discusses considerations for wave equation modeling using the PINN approach. These considerations include spatial coordinate normalization, the selection of activation functions, and strategies for incorporating physics loss. Our experimental results demonstrated that normalizing the spatial coordinates of the training data leads to a more accurate reflection of initial conditions in neural network training for wave equation modeling. Furthermore, the characteristics of various functions were compared to select an appropriate activation function for wavefield prediction using neural networks. These comparisons focused on their differentiation with respect to input data and their convergence properties. Finally, the results of two scenarios for incorporating physics loss into the loss function during neural network training were compared. Through numerical experiments, a curriculum-based learning strategy, applying physics loss after the initial training steps, was more effective than utilizing physics loss from the early training steps. In addition, the effectiveness of the PINN technique was confirmed by comparing these results with those of training without any use of physics loss.