• Safety and Health at Work
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• v.10 no.1
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• pp.95-102
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• 2019

Background: In a cluster-randomized trial, the Safety and Health Involvement For Truck drivers intervention produced statistically significant and medically meaningful weight loss at 6 months (-3.31 kg between-group difference). The current manuscript evaluates the relative impact of intervention components on study outcomes among participants in the intervention condition who reported for a post-intervention health assessment (n = 134) to encourage the adoption of effective tactics and inform future replications, tailoring, and enhancements. Methods: The Safety and Health Involvement For Truck drivers intervention was implemented in a Web-based computer and smartphone-accessible format and included a group weight loss competition and body weight and behavioral self-monitoring with feedback, computer-based training, and motivational interviewing. Indices were calculated to reflect engagement patterns for these components, and generalized linear models quantified predictive relationships between participation in intervention components and outcomes. Results: Participants who completed the full program-defined dose of the intervention had significantly greater weight loss than those who did not. Behavioral self-monitoring, computer-based training, and health coaching were significant predictors of dietary changes, whereas behavioral and body weight self-monitoring was the only significant predictor of changes in physical activity. Behavioral and body weight self-monitoring was the strongest predictor of weight loss. Conclusion: Web-based self-monitoring of body weight and health behaviors was a particularly impactful tactic in our mobile health intervention. Findings advance the science of behavior change in mobile health intervention delivery and inform the development of health programs for dispersed populations.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.27-35
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• 2021

In this paper, we propose a new stacking ensemble framework for deep learning models which reflects the distribution of label embeddings. Our ensemble framework consists of two phases: training the baseline deep learning classifier, and training the sub-classifiers based on the clustering results of label embeddings. Our framework aims to divide a multi-class classification problem into small sub-problems based on the clustering results. The clustering is conducted on the label embeddings obtained from the weight of the last layer of the baseline classifier. After clustering, sub-classifiers are constructed to classify the sub-classes in each cluster. From the experimental results, we found that the label embeddings well reflect the relationships between classification labels, and our ensemble framework can improve the classification performance on a CIFAR 100 dataset.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.537-544
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• 2022

As the penetration of electric vehicles and development of new models, user experience factors are getting more important in designing connectivity systems for car infotainment services. The primary object of this study is to identify commonalities and differences by comparing user experience factors in the Korean and US electric vehicle markets. This study derived connectivity keywords by text mining the vehicle introduction on the market in each country, and performed centrality, cluster analysis and visualization mapping using the semantic network analysis. As a result, the Korean new electric vehicle connectivity service mainly focused on driving functions such as driving, parking assistance, and charging, while US focused on device connection, convenience function control, app use, entertainment viewing. Based on the analysis, this study presented the practical implications in marketing, system design, and HMI design.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.387-393
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• 2022

The article reflects short-term perspectives on the use of information and communication technologies in the training of teachers for higher education. Education is characterized by conservatism, so aspects of systematic development of the industry are relevant to this cluster of social activity. Therefore, forecasting the introduction of innovative elements of ICT training is in demand for the educational environment. Forecasting educational trends are most relevant exactly in the issues of training future teachers of higher education because these specialists are actually the first to implement the acquired professional skills in pedagogical activities. The article aims to consider the existing potential of ICT-based learning, its implementation in the coming years, and promising innovative educational elements that may become relevant for the educational space in the future. The tasks of scientific exploration are to show the optimal formats of synergy between traditional and innovative models of learning. Based on already existing experience, extrapolation of conditions of educational process organization with modeling realities of using information and communication technologies in various learning dimensions should be carried out. Educational trends for the next 3 years are a rather tentative forecast because, as demonstrated by the events associated with the COVID-19 pandemic, the socio-cultural space is very changeable. Consequently, the dynamism of the educational environment dictates the need for a value-based awareness of the information society and the practical use of technological advances. Thus, information and communication technologies are a manifestation of innovative educational strategies of today and become an important component along with traditional aspects of educational process organization. Future higher education teachers should develop a training strategy taking into account the expediency of the ICT component.

• Earthquakes and Structures
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• v.22 no.2
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• pp.185-201
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• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• Journal of Veterinary Science
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• v.21 no.1
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• pp.9.1-9.15
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• 2020

Regenerative therapy holds great promise in the development of cures of some untreatable diseases such as cardiovascular diseases, and pluripotent stem cells (PSCs) including induced PSCs (iPSCs) are the most important regenerative seed cells. Recently, differentiation of human PSCs into functional tissues and cells in vitro has been widely reported. However, although porcine reports are rare they are quite essential, as the pig is an important animal model for the in vitro generation of human organs. In this study, we reprogramed porcine embryonic fibroblasts into porcine iPSCs (piPSCs), and differentiated them into cluster of differentiation 31 (CD31)-positive endothelial cells (ECs) (piPSC-derived ECs, piPS-ECs) using an optimized single-layer culture method. During differentiation, we observed that a combination of GSK3β inhibitor (CHIR99021) and bone morphogenetic protein 4 (BMP4) promoted mesodermal differentiation, resulting in higher proportions of CD31-positive cells than those from separate CHIR99021 or BMP4 treatment. Importantly, the piPS-ECs showed comparable morphological and functional properties to immortalized porcine aortic ECs, which are capable of taking up low-density lipoprotein and forming network structures on Matrigel. Our study, which is the first trial on a species other than human and mouse, has provided an optimized single-layer culture method for obtaining ECs from porcine PSCs. Our approach can be beneficial when evaluating autologous EC transplantation in pig models.

• Biomolecules & Therapeutics
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• v.31 no.2
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• pp.161-167
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• 2023

Despite the various medications used in clinics, the efforts to develop more effective treatments for depression continue to increase in the past decades mainly because of the treatment-resistant population, and the testing of several hypotheses- and target-based treatments. Undesirable side effects and unresponsiveness to current medications fuel the drive to solve this top global health problem. In this study, we focused on neuroinflammatory response-mediated depression which represents a cluster of depression etiology both in animal models and humans. Several meta-analyses reported that proinflammatory cytokines such as interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) were increased in major depressive disorder patients. Inflammatory mediators implicated in depression include type-I interferon and inflammasome pathways. To elucidate the molecular mechanisms of neuroinflammatory cascades underlying the pathophysiology of depression, we introduced hycanthone, an antischistosomal drug, to check whether it can counteract depressive-like behaviors in vivo and normalize the inflammation-induced changes in vitro. Lipopolysaccharide (LPS) treatment increased proinflammatory cytokine expression in the murine microglial cells as well as the stimulation of type I interferon-related pathways that are directly or indirectly regulated by Janus kinase-signal transducer and activator of transcription (JAK-STAT) activation. Hycanthone treatment attenuated those changes possibly by inhibiting the JAK-STAT pathway and inflammasome activation. Hycanthone also ameliorated depressive-like behaviors by LPS. Taken together, we suggest that the inhibitory action of hycanthone against the interferon pathway leading to attenuation of depressive-like behaviors can be a novel therapeutic mechanism for treating depression.

• Mycobiology
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• v.50 no.4
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• pp.254-257
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• 2022

Wolfiporia cocos is a wood-decay brown rot fungus belonging to the family Polyporaceae. While the fungus grows, the sclerotium body of the strain, dubbed Bokryeong in Korean, is formed around the roots of conifer trees. The dried sclerotium has been widely used as a key component of many medicinal recipes in East Asia. Wolfiporia cocos strain KMCC03342 is the reference strain registered and maintained by the Korea Seed and Variety Service for commercial uses. Here, we present the first draft genome sequence of W. cocos KMCC03342 using a hybrid assembly technique combining both short- and long-read sequences. The genome has a total length of 55.5 Mb comprised of 343 contigs with N50 of 332 kb and 95.8% BUSCO completeness. The GC ratio was 52.2%. We predicted 14,296 protein-coding gene models based on ab initio gene prediction and evidence-based annotation procedure using RNAseq data. The annotated genome was predicted to have 19 terpene biosynthesis gene clusters, which was the same number as the previously sequenced W. cocos strain MD-104 genome but higher than Chinese W. cocos strains. The genome sequence and the predicted gene clusters allow us to study biosynthetic pathways for the active ingredients of W. cocos.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.337-357
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• 2023

Universities and local governments in Korea are simultaneously experiencing the difficulties of a decrease in the youth population and a decrease in the school-age population, and close cooperation between universities and local governments is urgently needed to solve this problem. Representative methods of such cooperation include the establishment of a regional innovation system and the theories of innovation clusters and triple helix models. In addition to these theories, the aim is to derive sustainable conditions for the local government-university cooperation model by examining various cases of cooperation at home and abroad. This is the purpose of this paper. In particular, through case studies of cooperation between local governments and universities at home and abroad, three types of models (job, education, and housing) were analyzed, and common conditions and requirements for sustainable cooperation were proposed. In order for cooperation between local governments and universities to continue and produce successful results, mutual benefit creation, infrastructure construction and operation appropriate for capabilities, flexibility, and mutual responsibility are necessary. Furthermore, a model that suits the capabilities of local governments and universities must be found, and in this process, the university's research capabilities and commercialization capabilities of research results are especially important. In addition, it is essential to establish a new cooperation system between local governments, universities, and the central government.