• The korean journal of orthodontics
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• v.49 no.3
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• pp.170-180
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• 2019

Objective: The purpose of this study was to investigate the relationship between the facial skeletal patterns and the shape of the mandibular symphysis in adults with malocclusion by using a structural equation model (SEM). Methods: Ninety adults who had malocclusion and had records of facial skeletal measurements performed using cone-beam computed tomography were selected for this study. The skeletal measurements were classified into three groups (vertical, anteroposterior, and transverse). Cross-sectional images of the mandibular symphysis were analyzed using generalized Procrustes and principal component (PC) analyses. A SEM was constructed after the factors were extracted via factor analysis. Results: Two factors were extracted from the transverse, vertical, and anteroposterior skeletal measurements. Latent variables were extracted for each factor. PC1, PC2, and PC3 were selected to analyze the variations of the mandibular symphyseal shape. The SEM was constructed using the skeletal variables, PCs, and latent variables. The SEM showed that the vertical latent variable exerted the most influence on the mandibular symphyseal shape. Conclusions: The relationship between the skeletal pattern and the mandibular symphysis was analyzed using a SEM, which showed that the vertical facial skeletal pattern had the highest effect on the shape of the mandibular symphysis.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.1
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• pp.63-72
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• 2018

Social entrepreneurship is increasingly gaining interest in developing countries for the great benefits of society. In Vietnam, the concept of social entrepreneurship is still quite new. Entrepreneurial intention is regarded as a useful and practial approach for understanding actual entrepreneurial behavior. The purpose of this paper is to develop an integrated model based on planned behavior to examine the direct and indirect effect of perceived access to finance on social entrepreneurial intention. The confirm factor analysis to study the latent constructs underlying determinants of planned behavioral theory, perceived access to finance and social entrepreneurial intention. Then, it applies the technique of structural equation modeling to explore relationships among latent constructs. There is no direct relationship between perceived access to finance and social entrepreneurial intention. Perceived access to finance only indirectly increases entrepreneurial intention through attitude towards behavior and perceived behavioral. This study focuses on the perceptual factor of financial access that affects entrepreneurial intentions. The study does not cover other in-depth issues of social entrepreneurship such as decision making, leadership, personality traits, social capital, and human capital. To establish an environment with a strong social entrepreneurial intention, a focus on developing perceived access to finance is an extremely important factor. This study also suggests that attitude towards behavior and perceived behavioral have a strong impact to social entrepreneurship.

• Korean Journal of Childcare and Education
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• v.18 no.4
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• pp.1-16
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• 2022

Objective: The purpose of this study is to test the psychometric properties of the Maternal Emotion Coaching Questionnaire (MECQ, Lim et al., 2018) in order to measure emotion coaching in mothers of preschoolers. Methods: A total of 316 preschoolers and their mothers participated in this study. Maternal emotion coaching was assessed by self-report and child emotion regulation ability was evaluated by the teacher. Data were analyzed with chi-square tests, reliability analysis, confirmatory factor analysis, latent profile analysis, and F-test. Results: Each item of the MECQ showed proper discriminative power. The MECQ and each subscale demonstrated adequate internal consistency and split-half reliability. Evidence of construct validity was provided by confirmatory factor analysis. The five-factor model including maternal attention, awareness, acceptance, empathy, and guidance showed a good fit. Results of the latent profile analysis revealed three profiles of emotion coaching: excellent, good, and poor. Preschoolers with mothers in the poor coaching profile showed significantly lower emotion regulation ability compared to those in the excellent or good coaching profiles, which suggested discriminative validity of the MECQ. Conclusion/Implications: The MECQ presents a reliable and valid tool to assess emotion coaching in mothers of preschool children and can thus be effectively used for mothers of preschoolers.

• Journal of Environmental Science International
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• v.3 no.1
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• pp.17-29
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• 1994

An one dimensional atmosphere-vegetation interaction model is developed to discuss of the effect of vegetation on heat flux in mesoscale planetary boundary layer. The canopy model was a coupled system of three balance equations of energy, moisture at ground surface and energy state of canopy with three independent variables of $T_f$(foliage temperature), $T_g$(ground temperature) and $q_g$(ground specific humidity). The model was verified by comparative study with OSUID(Oregon State University One Dimensional Model) proved in HYPEX-MOBHLY experiment. As the result, both vegetation and soil characteristics can be emphasized as an important factor iii the analysis of heat flux in the boundary layer. From the numerical experiments, following heat flux characteristics are clearly founded simulation. The larger shielding factor(vegetation) increase of $T_f$ while decrease $T_g$. because vegetation cut solar radiation to ground. Vegetation, the increase of roughness and resistance, increase of sensible heat flux in foliage while decrease the latent heat flux in the foliage.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.45-68
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• 2023

The purpose of this study is to longitudinally analyze impact factors on library use. One of library use indicators, the number of circulated books was statistically analyzed with latent growth model and growth mixture model. Library data from 2014 to 2019 were collected from the National Library Statistics System, and 846 public libraries were analyzed. As results, the number of circulated books were decreased, but it was tempered. Next, with controlling the factor affecting the dependent variable, the size of collection and the number of participants in reading programs provided by public libraries were statistically significant. Lastly, 5 classes were identified by applying the growth mixture model, and the number of librarians was significantly associated with trajectory class membership.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.19-28
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• 2017

Collaborative filtering (CF) is a system that interprets the relationship between a user and a product and recommends the product to a specific user. The CF model is advantageous in that it can recommend products to users with only rating data without any additional information such as contents. However, there are many cases where a user does not give a rating even after consuming the product as well as consuming only a small portion of the total product. This means that the number of ratings observed is very small and the user rating matrix is very sparse. The sparsity of this rating data poses a problem in raising CF performance. In this paper, we concentrate on raising the performance of latent factor model (especially SVD). We propose a new model that includes product similarity information and co occurrence information in SVD. The similarity and concurrence information obtained from the rating data increased the expressiveness of the latent space in terms of latent factors. Thus, Recall increased by 16% and Precision and NDCG increased by 8% and 7%, respectively. The proposed method of the paper will show better performance than the existing method when combined with other recommender systems in the future.

• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.19-37
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• 2015

The purpose of this study is to confirm a structural relationship on factors affecting ratio of public transportation spending to a car-owning household's total transportation expenditure. For this purpose, informations of household's attributes and activities were gathered using the 13th Korean Labor and Income Panel Study (KLIPS), and information of land-use and transportation conditions on their residential locations was collected and processed. A structural equation model (SEM) on determinants affecting ratio of public transportation expenditure was constructed, based on an execution result of factor analysis using the analyzing database. The latent variables were derived as land-use/transportation characteristic, household's attribute and household's activity. In the analyzing result of the SEM, the entire latent variables were significant. And, the first two latent variables had positive influences, and the last latent variable had a negative impact. To promote public transportation use of the car-owning households, this study suggests that the policies such as enhancement of convenience in public transportation use for the household's activities and improvement of the land-use/transport conditions are required.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.589-603
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• 2023

Deep generative models target to infer the underlying true data distribution, and it leads to a huge success in generating fake-but-realistic data. Regarding such a perspective, the data attributes can be a crucial factor in the data generation process since non-existent counterfactual samples can be generated by altering certain factors. For example, we can generate new portrait images by flipping the gender attribute or altering the hair color attributes. This paper proposes counterfactual disentangled variational autoencoder generative adversarial networks (CDVAE-GAN), specialized for data attribute level counterfactual data generation. The structure of the proposed CDVAE-GAN consists of variational autoencoders and generative adversarial networks. Specifically, we adopt a Gaussian variational autoencoder to extract low-dimensional disentangled data features and auxiliary Bernoulli latent variables to model the data attributes separately. Also, we utilize a generative adversarial network to generate data with high fidelity. By enjoying the benefits of the variational autoencoder with the additional Bernoulli latent variables and the generative adversarial network, the proposed CDVAE-GAN can control the data attributes, and it enables producing counterfactual data. Our experimental result on the CelebA dataset qualitatively shows that the generated samples from CDVAE-GAN are realistic. Also, the quantitative results support that the proposed model can produce data that can deceive other machine learning classifiers with the altered data attributes.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.1
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• pp.15-20
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• 2017

The summer climate is very hot and humid in Korea. The humidity is an important factor in determining thermal comfort. Recently, the research for dehumidification device development has been attempted to save energy that is required for the operation of the current dehumidifiers on the market. Existing dehumidification systems have disadvantages such as wasting energy to drive a compressor. Meanwhile, dehumidification systems with membranes can dehumidify humid air without increasing the dry bulb temperature so it doesn't have to consume cooling energy. In this paper, the cooling energy savings was studied when a dehumidification system was applied in a model building instead of a chiller. The sensible heat load was almost the same result, but the latent heat load was decreased by 38.9% and the total heat load was decreased by 8.5%. As a result, electric energy used to drive the compressor in a chiller was saved by applying a membrane air-conditioning system instead.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.