• Journal of Service Research and Studies
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• v.14 no.3
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• pp.1-18
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• 2024

Recent discussions regarding smart tourism have gained significant momentum in tourism policy and industry; however, knowledge production in this research area remains fragmented and sporadic. This study aims to analyze trends in smart tourism research published in domestic KCI journals up to the end of July 2024 through a systematic literature review, proposing future research tasks to foster academic development. The analysis addresses both the quantitative and qualitative dimensions of smart tourism research, particularly focusing on tourism journals where the terms and concepts are prominent in policy and industry contexts, while also diagnosing the related research paradigms. The findings indicate that the term "smart tourism" began to prominently appear in research titles, topics, keywords, and abstracts as early as 2014. Among the 126 studies analyzed, research related to tourism constituted the largest share, accounting for 30.2%. However, due to the interdisciplinary nature of smart tourism, research has also emerged from various academic fields, including business studies, design, information communication, and computer science. Research on smart tourism has appeared in tourism journals since 2015, predominantly adopting a positivist research paradigm with an emphasis on quantitative methodologies that often utilize surveys. Additionally, the study reveals a pre-paradigm stage within smart tourism research, characterized by insufficient comprehensive conceptual and theoretical development. This stage has also restricted discussions on various ontological, epistemological, methodological, and interpretive issues. The theories mainly employed draw from established behavioral models, such as the Technology Acceptance Model, the Extended Technology Acceptance Model, and the Technology Readiness Model. Based on these findings, the study suggests future research directions for tourism scholars to determine whether smart tourism will solidify as a sustainable research topic or merely be regarded as a transient trend within tourism studies over the next decade.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.43-59
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• 2024

This study focuses on identifying the impacts of the Nowon resource recovery facility in Seoul, Korea, on the real transaction price of apartments in the neighboring areas between 2006 and 2022, and the spatial extent of the impact. Resource recovery facilities, which generate electricity and heating energy while disposing of waste, are typical unwanted facilities that have a negative impact on neighboring property prices. As direct landfilling of household waste is banned in Seoul from 2026 and nationwide from 2030, the demand for the expansion of waste incineration facilities, including resource recovery facilities, is expected to increase rapidly. In addition, social disputes related to the decline in neighboring property prices are expected to increase. This study analyses the impact of the Nowon resource recovery facility on surrounding apartment prices over a 17-year period since 2006 using hedonic price models for apartments, and finds that the Nowon resource recovery facility consistently has a negative impact on nearby apartment prices, the spatial extent of the impact is at least 1,000 meters from the facility, and the intensity of the negative impact weakens as the distance from the facility increases. The results of this study differ from recent studies finding that the spatial extent of the impact of resource recovery facilities in Seoul on surrounding property prices is limited within 500~600 meters, suggesting that a broader approach is needed to systematically manage social conflicts that are expected to increase with the growing social demand for resource recovery facilities.

• Oncology Letters
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• v.42 no.1
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• pp.453-460
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• 2019

The present study aimed to identify novel methylation markers of clear cell renal cell carcinoma (ccRCC) using microarray methylation analysis and evaluate their prognostic relevance in patient samples. To identify cancer-specific methylated biomarkers, microarray profiling of ccRCC samples from our institute (n=12) and The Cancer Genome Atlas (TCGA) database (n=160) were utilized, and the prognostic relevance of candidate genes were investigated in another TCGA dataset (n=153). For validation, pyrosequencing analyses with ccRCC samples from our institute (n=164) and another (n=117) were performed and the potential clinical application of selected biomarkers was examined. We identified 22 CpG island loci that were commonly hypermethylated in ccRCC. Kaplan-Meier analysis of TCGA data indicated that only 4/22 loci were significantly associated with disease progression. In the internal validation set, Kaplan-Meier analysis revealed that hypermethylation of two loci, zinc finger protein 492 (ZNF492) and G protein-coupled receptor 149 (GPR149), was significantly associated with shorter time-to-progression. Multivariate Cox regression models revealed that hypermethylation of ZNF492 [hazard ratio (HR), 5.44; P=0.001] and GPR149 (HR, 7.07; P<0.001) may be independent predictors of tumor progression. Similarly, the methylation status of these two genes was significantly associated with poor outcomes in the independent external validation cohort. Collectively, the present study proposed that the novel methylation markers ZNF492 and GPR149 could be independent prognostic indicators in patients with ccRCC.

• International Journal of Oncology
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• v.54 no.4
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• pp.1327-1336
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• 2019

Endothelial progenitor cells (EPCs) are bone marrow (BM)-derived progenitor cells that can differentiate into mature endothelial cells, contributing to vasculogenesis in the blood vessel formation process. Runt-related transcription factor 3 (RUNX3) belongs to the Runt domain family and is required for the differentiation of specific immune cells and neurons. The tumor suppressive role of RUNX3, via the induction of apoptosis and cell cycle arrest in a variety of cancers, and its deletion or frequent silencing by epigenetic mechanisms have been studied extensively; however, its role in the differentiation of EPCs is yet to be investigated. Therefore, in the present study, adult BM-derived hematopoietic stem cells (HSCs) were isolated from Runx3 heterozygous (Rx3^+/-) or wild-type (WT) mice. The differentiation of EPCs from the BM-derived HSCs of Rx3^+/- mice was found to be significantly increased compared with those of the WT mice, as determined by the number of small or large colony-forming units. The migration and tube formation abilities of Rx3^+/- EPCs were also observed to be significantly increased compared with those of WT EPCs. Furthermore, the number of circulating EPCs, defined as CD34⁺/vascular endothelial growth factor receptor 2 (VEGFR2)⁺ cells, was also significantly increased in Rx3^+/- mice. Hypoxia-inducible factor (HIF)-1α was upregulated in Rx3^+/- EPCs compared with WT EPCs, even under normoxic conditions. Furthermore, in a hindlimb ischemic mouse models, the recovery of blood flow was observed to be highly stimulated in Rx3^+/- mice compared with WT mice. Also, in a Lewis lung carcinoma cell allograft model, the tumor size in Rx3^+/- mice was significantly larger than that in WT mice, and the EPC cell population (CD34⁺/VEGFR2⁺ cells) recruited to the tumor was greater in the Rx3^+/- mice compared with the WT mice. In conclusion, the present study revealed that Runx3 inhibits vasculogenesis via the inhibition of EPC differentiation and functions via the suppression of HIF-1α activity.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.615-626
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• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).

• Journal of Science Education
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• v.48 no.2
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• pp.91-100
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• 2024

The use of smart devices in science classes has brought about positive changes, such as increased student participation and more self-directed learning. Smart devices are increasingly being used in science classes, creating a need to develop lesson models that can stimulate students' interest and encourage active, self-directed learning in scientific inquiry and experimental activities. In smart education, smart devices and applications play a major role. However, in the "Mixture Separation" section of middle school science, chromatography focuses mainly on paper chromatography, which is not currently used in the field of actual research. This approach is not well-suited for students preparing for a new future society, and it is becoming obsolete due to curriculum revisions. Although chromatography can be used as an activity for career exploration, removing it is not convincing. The advantage of using thin-layer chromatography (TLC), which is employed in actual research, is that it is inexpensive and easy to use in classroom settings. In this study, we have developed a new, faster, and simpler analysis method for TLC that uses smart devices for both qualitative and quantitative analysis. We hope this method will enhance student engagement and facilitate small-scale learning by integrating smart devices into learning activities, making it a practical tool for actual school settings.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.585-598
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• 2024

To mitigate natural disasters and efficiently manage water resources, it is essential to enhance hydrologic prediction while reducing model structural uncertainties. This study analyzed the impact of lumped and semi-distributed GR4J model structures on simulation performance and evaluated uncertainties with and without data assimilation techniques. The Ensemble Kalman Filter (EnKF) and Particle Filter (PF) methods were applied to the Namgang Dam basin. Simulation results showed that the Kling-Gupta efficiency (KGE) index was 0.749 for the lumped model and 0.831 for the semi-distributed model, indicating improved performance in semi-distributed modeling by 11.0%. Additionally, the impact of uncertainties in meteorological forcings (precipitation and potential evapotranspiration) on data assimilation performance was analyzed. Optimal uncertainty conditions varied by data assimilation method for the lumped model and by sub-basin for the semi-distributed model. Moreover, reducing the calibration period length during data assimilation led to decreased simulation performance. Overall, the semi-distributed model showed improved flood simulation performance when combined with data assimilation compared to the lumped model. Selecting appropriate hyper-parameters and calibration periods according to the model structure was crucial for achieving optimal performance.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.267-274
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• 2024

Hydrogen embrittlement fracture poses a challenge in ensuring the structural integrity of materials exposed to hydrogen-rich environments. This study advances our comprehension of hydrogen-induced fracture through an integrated numerical modeling approach. In addition, it employs a ductile fracture model named the Gurson-cohesive model (GCM) and hydrogen diffusion analysis. GCM is employed as a fracture model that combines the Gurson model to illustrate the continuum damage evolution and the cohesive zone model to describe crack surface discontinuity and softening behavior. Moreover, porosity and stress triaxiality are considered as crack initiation criteria . A hydrogen diffusion analysis is also integrated with the GCM to account for hydrogen enhanced decohesion (HEDE) mechanisms and their subsequent impacts on crack initiation and propagation. This framework considers the influence of hydrogen on the softening behavior of the traction-separation relationship on the discontinuous crack surface. Parametric studies explore the sensitivity to diffusion properties and hydrogen-induced fracture properties. By combining numerical models of hydrogen diffusion and the ductile fracture model, this study provides an understanding of hydrogen-induced fracture and thereby contributes significantly to the ongoing efforts to design materials that are resilient to hydrogen embrittlement in practical engineering applications.

• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.510-520
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• 2024

Objective : Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases, characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. The treatment of PD aims to alleviate motor symptoms by replacing the reduced endogenous dopamine. Currently, there are no disease-modifying agents for the treatment of PD. Zebrafish (Danio rerio) have emerged as an effective tool for new drug discovery and screening in the age of translational research. The neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is known to cause a similar loss of dopaminergic neurons in the human midbrain, with corresponding Parkinsonian symptoms. L-type calcium channels (LTCCs) have been implicated in the generation of mitochondrial oxidative stress, which underlies the pathogenesis of PD. Therefore, we investigated the neuro-restorative effect of LTCC inhibition in an MPTP-induced zebrafish PD model and suggested a possible drug candidate that might modify the progression of PD. Methods : All experiments were conducted using a line of transgenic zebrafish, Tg(dat:EGFP), in which green fluorescent protein (GFP) is expressed in dopaminergic neurons. The experimental groups were exposed to 500 μmol MPTP from 1 to 3 days post fertilization (dpf). The drug candidates : levodopa 1 mmol, nifedipine 10 μmol, nimodipine 3.5 μmol, diethylstilbestrol 0.3 μmol, luteolin 100 μmol, and calcitriol 0.25 μmol were exposed from 3 to 5 dpf. Locomotor activity was assessed by automated tracking and dopaminergic neurons were visualized in vivo by confocal microscopy. Results : Levodopa, nimodipine, diethylstilbestrol, and calcitriol had significant positive effects on the restoration of motor behavior, which was damaged by MPTP. Nimodipine and calcitriol have significant positive effects on the restoration of dopaminergic neurons, which were reduced by MPTP. Through locomotor analysis and dopaminergic neuron quantification, we identified the neuro-restorative effects of nimodipine and calcitriol in zebrafish MPTP-induced PD model. Conclusion : The present study identified the neuro-restorative effects of nimodipine and calcitriol in an MPTP-induced zebrafish model of PD. They restored dopaminergic neurons which were damaged due to the effects of MPTP and normalized the locomotor activity. LTCCs have potential pathological roles in neurodevelopmental and neurodegenerative disorders. Zebrafish are highly amenable to high-throughput drug screening and might, therefore, be a useful tool to work towards the identification of disease-modifying treatment for PD. Further studies including zebrafish genetic models to elucidate the mechanism of action of the disease-modifying candidate by investigating Ca2+ influx and mitochondrial function in dopaminergic neurons, are needed to reveal the pathogenesis of PD and develop disease-modifying treatments for PD.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.49-60
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• 2024

Ground-penetrating radar (GPR) enables rapid data acquisition over extensive areas, but interpreting the obtained data requires specialized knowledge. Numerous studies have utilized numerical analysis methods to examine GPR signal characteristics under various conditions. To develop more realistic numerical models, the heterogeneous nature of the ground, which causes clutter, must be considered. Clutter refers to signals reflected by objects other than the target. The Peplinski material model and fractal techniques can simulate these heterogeneous characteristics, yet there is a shortage of research on the necessary input parameters. Moreover, methods for quantitatively evaluating the similarity between field and analytical data are not well established. In this study, we calculated the autocorrelation coefficient of field data and determined the correlation length using the autocorrelation function. The correlation length represented the temporal or spatial distance over which data exhibited similarity. By comparing the correlation length of field data with that of the numerical model incorporating fractal weights, we quantitatively evaluated a numerical model for heterogeneous ground. Consequently, the results of this study demonstrated a numerical modeling technique that reflected the clutter characteristics of the field through correlation length.