Controlling Factors on the Development and Connectivity of Fracture Network: An Example from the Baekildo Fault in the Goheung Area (단열계의 발달 및 연결성 제어요소: 고흥지역 백일도단층의 예)
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- Economic and Environmental Geology
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- v.54 no.6
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- pp.615-627
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- 2021
The Baekildo fault, a dextral strike-slip fault developed in Baekil Island, Goheung-gun, controls the distribution of tuffaceous sandstone and lapilli tuff and shows a complex fracture system around it. In this study, we examined the spatial variation in the geometry and connectivity of the fracture system by using circular sampling and topological analysis based on a detailed fracture trace map. As a result, both intensity and connectivity of the fracture system are higher in tuffaceous sandstone than in lapilli tuff. Furthermore, the degree of the orientation dispersion, intensity, and average length of fracture sets vary depending on the along-strike variation in structural position in the tuffaceous sandstone. Notably, curved fractures abutting the fault at a high angle occur at a fault bend. Based on the detailed observation and analyses of the fracture system, we conclude as follows: (1) the high intensity of the fracture system in the tuffaceous sandstone is caused by the higher content of brittle minerals such as quartz and feldspar. (2) the connectivity of the fracture system gets higher with the increase in the diversity and average length of the fracture sets. Finally, (3) the fault bend with geometric irregularity is interpreted to concentrate and disturb the local stress leading to the curved fractures abutting the fault at a high angle. This contribution will provide important insight into various geologic and structural factors that control the development of fracture systems around faults.
The Chinese three northeastern three provinces - Heilongjiang, Liaoning, and Jilin - hold significant geographical, geopolitical, and commercial importance due to their location allowing for cross-border trade and transportation with North Korea. These provinces are crucial for establishing a complex Eurasian logistics network in line with South Korea's new northern policy. The Chinese three northeastern three provinces, as this region is known, boasts excellent maritime transportation links between South Korea, China, and North Korea, making it an logistics hub for transporting goods to Eurasia and Europe through multimodal transport. This study highlights the importance of securing a logistics hub area by fostering cooperation and friendly relations with China's three northeastern three provinces, which are crucial to the success of the New Northern Policy. In particular, the study aims to analyze current status of trade with these region and freight volume transported by ships and recommend political advice for securing logistics hub and revitalizing maritime transport. As the policy suggestion, this study is to establish a logistics hub by implementing joint port operations, constructing port infrastructure jointly, and operating shipping companies together. Additionally, we propose ways to revitalize the maritime passenger transport business between Korea and China, which involves expanding cultural exchanges and developing content.
With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.
The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.
As global challenges, particularly climate change, become more pressing, there is a growing global awareness of Environmental, Social, and Governance (ESG) management. Given the crucial role played by the logistics industry in the complex network of the global supply chain, various societal stakeholders are emphasizing the necessity for logistics entities to practice ESG management. Despite the comprehensive ESG guidelines established by Korea for all enterprises, a notable limitation arises from its inadequate consideration of the distinctive features inherent to logistics enterprises, especially those of a smaller and medium scale. Accordingly, this study conducts a thorough examination of existing ESG guidelines, sustainable management approaches in large-scale logistics enterprises, and prior research to identify potential ESG management diagnostic criteria relevant to small and medium-sized logistics enterprises, including aspects such as Public(P), Environmental(E), Social(S), and Governance(G). To streamline the diagnostic criteria, taking into account the unique characteristics of small and medium-sized logistics enterprises, this study conducts a survey involving 60 logistics company personnel and experts from academic and research domains. The collected data undergoes Principal Component Analysis (PCA), revealing that the four dimensions of information disclosure can be consolidated into a single dimension. Additionally, environmental criteria reduce from 16 to 3 items, societal considerations decrease from 22 to 7 items, and governance structures distill from 20 to 5 items. This empirical endeavor is deemed significant in presenting tailored ESG management diagnostic criteria aligned with the specificities of small and medium-sized logistics enterprises. The findings of this study are expected to serve as a foundational resource for the development of guidelines by relevant entities, promoting the wider adoption of ESG management practices in the sphere of small and medium-sized logistics enterprises in the near future. population coming from areas other than Gwangyang, where Gwangyang Port is located.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.
The purpose of this study is to propose effective scenarios for green areas in apartment complexes that can improve the connection between green spaces considering wind flow, thermal comfort, and mitigation of the urban heat island effect. The study site was an apartment complex in Godeok-dong, Gangdong-gu, Seoul, Korea. The site selection was based on comparing temperatures and discomfort index data collected from June to August 2020. Initially, the thermal and wind environment of the current site was analyzed. Based on the findings, three scenarios were proposed, taking into account both green patches and corridor elements: Scenario 1 (green patch), Scenario 2 (green corridor), and Scenario 3 (green patch & corridor). Subsequently, each scenario's wind speed, wind flow, and thermal comfort were analyzed using ENVI-met to compare their effectiveness in mitigating the urban heat island effect. The study results demonstrated that green patches contributed to increased wind speed and improved wind flow, leading to a reduction of 31..20% in the predicted mean vote (PMV) and 68.59% in the predicted percentage of dissatisfied (PET). On the other hand, green corridors facilitated the connection of wind paths and further increased wind speed compared to green patches. They proved to be more effective than green patches in mitigating the urban heat island, resulting in a reduction of 92.47% in PMV and 90.14% in PET. The combination of green patches and green corridors demonstrated the greatest increase in wind speed and strong connectivity within the apartment complex, resulting in a reduction of 95.75% in PMV and 95.35% in PET. However, patches in narrow areas were found to be more effective in improving thermal comfort than green corridors. Therefore, to effectively mitigate the urban heat island effect, enhancing green areas by incorporating green corridors in conjunction with green patches is recommended. This study can serve as fundamental data for planning green areas to mitigate future urban heat island effects in apartment complexes. Additionally, it can be considered a method to improve urban resilience in response to the challenges posed by the urban heat island effect.
The purpose of this study was to analyze the examples presented in the "Separation of Mixtures" section of the 2015 revised science authorized textbook introduced in elementary schools in 2022 and to see how the teachers and students understand the concept. To do that, 96 keywords were extracted through three cleansing processes to separate the elements of the mixture presented in the textbook. In order to analyze the teachers' perceptions, 32 teachers at elementary schools in Gyeonggi-do received responses to a survey, and a survey of 92 fourth graders who learned the separation of the mixture with an authorized textbook in 2022 was used for the analysis. As a result, as for the solids, 54 out of 96 separations (56.3%) showed the highest ratio, and the largest number of cases were presented according to the characteristics of the development stage of students. It was followed by living things, liquids, other objects and substances, and gasses. By analyzing the mixture, the structure and the interrelationships between the 96 extracted keywords were systematized through the network analysis, and the connection between the keywords, which were a part of the mixture was analyzed. The teachers partially responded to the separation of the complex mixture presented in the textbook, but most of the students did not recognize it. Because the analysis of the teachers' and students' perceptions of the seven separate categories presented in the survey was not based on a clear conceptual perception of the separation of the mixture, but rather they tended to respond differently for each characteristic of each individual category, it was decided that it was necessary to present clearer examples of the separation of the mixture, so that the students could better understand the concept of separation of mixtures that could be somewhat abstract.