- P-ISSN1738-6764
- E-ISSN2093-7504
- KCI
ISSN : 1738-6764
Vol.17 No.3
Abstract
Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.
Abstract
The purpose of this paper is to explore how the Confucius Institute Chinese international promotion could better promote the development of China's foreign trade, by analyzing the distribution of the Confucius Institute worldwide, based on the theory of language economics, using SWOT analysis to analyze the advantages and disadvantages of the internal environment, opportunities and challenges of the external environment of Chinese international promotion of Confucius Institute. The following findings were gathered: as a language teaching institution and information exchange platform, Confucius Institute has the ability to share trade information and increase trade opportunities; to improve cultural identity and reduce transaction costs; to promote cultural communication and integration, and drive the development of related industries. The internal disadvantages were mainly reflected in the mismatch between the global regional distribution structure of Confucius Institutes, and the economic and trade structure, such as, the asymmetry between language, culture output, and demand. In addition, the management mechanism was not perfect. External opportunities were mainly new opportunities brought by economic globalization, cultural diversity, and the development of the Belt and Road initiative. External challenges were mainly influenced by the China threat theory and the fierce cultural competition among countries. The corresponding countermeasures were put forward based on the advantages of the platform and grasping the external opportunities: improving the quality of operation and speeding up the localization process; respecting cultural differences and realizing cultural common learning; seeking multilateral cooperation and enhancing the capacity for independent development.
Abstract
This study aimed to determine the effects of simulation learning program using SBAR (Situation, Background, Assessment, Recommendation) techniques on undergraduate nursing students' clinical judgment and communication skills. A quasi-experimental research design (one-group pretest-posttest design) was used in this study. The participants included 88 students from a nursing college. There were statistically significant differences in clinical judgment, communication clarity, and communication confidence between pre-simulation learning using SBAR and post (t=10.32, p<.0001; t=6.05, p=<.0001; t=7.42, p=<.0001). The simulation learning program using SBAR was found to improve nursing students' clinical judgment as well as clarity and confidence in interprofessional communication.
Abstract
The purpose of this study was to analyze the effectiveness of integrated simulations conducted by virtual simulation and in situ simulation among nursing college students during COVID-19. This study was conducted from July 7 to 9, 2020 and the participants included 126 fourth-year nursing college students. Integrated simulation consisted of virtual simulation, teledebriefing, pre-briefing, in situ simulation, and debriefing. The results showed that after the use of various simulation modules and the training of integrated simulations incorporating virtual and in situ simulation training, critical thinking (t=5.20, p=<0.001), clinical judgment (t=6.71, p=<0.001), and simulation effectiveness (t=3.53, p=0.001). These findings could help establish the direction for more diverse forms of simulation-based education and it should be conducted in future nursing simulation during this COVID-19 pandemic era.
Abstract
We proposed a B2B food distribution platform by transforming an established food distribution and management platform based on blockchain technology. Our proposed model introduced a method to bring innovation into the domestic B2B food distribution market and systematically manage and utilize massive data (country of origin, producer, transaction, distributor, final consumer) generated within the food distribution process.
Abstract
Dark matter is barely known because it cannot be explained using the Standard Model. In addition, dark matter has not been detected yet. It is currently being explored through various ways. In this paper, we studied dark matter in an electron-positron collider using MadGraph5. The signal channel is e<sup>+</sup>e<sup>- </sup>→ 𝜇<sup>+</sup>𝜇<sup>-</sup>A' where A' decays to dimuon. We studied the cross-section by increasing the center-of-mass energy. Central processing unit (CPU) time of simulation was compared with that using a local Linux machine and a KISTI-5 supercomputer (Knight Landing and Skylake). Furthermore, one or more cores were used for comparing CPU time among machines. Results of this study will enable the exploration of dark matter in electron-positron experiments. This study also serves as a reference for optimizing high-energy physics simulation toolkits.
Abstract
A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.