The rapidly changing social structure of the digital environment is having a significant impact on economic activities. That is also an important issue for Individuals who want to sustain economic activities and countries that support policies. Non-face-to-face industries have been revitalized due to the problem of human capital utilization attributed to aging population, the real economic recession caused by Corona 19, contraction of face-to-face economic activities, reduction of employment, and job instability. Accordingly, digital media contents based economic activities have become commonplace, and the government's main policy issue is to use human capital effectively for media contents based economic activities. Adaptation to the digital environment has become a necessity, not a choice, for those who wish to continue to be in employment. Therefore, this study analyzed the effects of digital and individual abilities on intention to sustain the economic activity and verified the modulation effect of the role model. In order to achieve the purpose of this research, an online survey was conducted on men and women aged 20 to 80 nationwide, and 382 of the 385 collected questionaires were analyzed. The SPSS 23.0 program was used to analyze this study, and the questionaire questions were measured using the Likert 5-point scale. As a result of the analysis, first, the ability to utilize media contents in digital capacity has a positive impact on the intention to sustain economic activity, and that the higher the ability to utilize the latest digital media contents such as SNS, the more likely the intention to sustain economic activity. Secondly, it was found that the financial strength of individuals' abilities was affected by the negative impact, and that the experiences were affected by positive(+) impact on the intention to sustain economic activity. Thirdly, the social environment has no significant effect on the intention to sustain economic activity. Fourth, it was found that family support amongst social support has a positive impact on the intention to sustain economic activity, and that various emotional support for families has increased intention to sustain economic activity. Fifth, the role model was found to have a positive(+) impact on economic sustainability, while the ability to utilize media content and family support played a modulating role on economic sustainability. Therefore, as a result of this research, the government's policy support for employment and entrepreneurship is required in accordance with digital media content based digital education and human structure in order to sustain economic activities.
Ken Wilber(1949~) tries to approach integration of philosophy, emphasising the advent of the perennial philosophy in the exchange of scholarships at the present day. He suggests All Quadrant(四象限) for the first step to reach his integrated approach. In addition, his approach concretizes for AQAL(All Quadrant, All Level). His AQAL approach which can be divide into inside/outside, singular/plural, or interobjectivity/intersubjectivity is applied to a lot of fileds such as ecology, management, criminology, medical treatment and its boundary is getting bigger. The merits of Quadrant is that it can solve the problem of the plain(平原), which resolves the strength of subjective interior(cultural contexts) into the external existence(social system). From view of Dongmu Lee Je-ma(1837~1900), a former philosopher, Sa-Sang theory['Affairs-Mind-body-Objects(事心身物)', 'Heaven-Human-nature-Order(天人性命)] is in accord with Wilber in content. In Dongmu's writing, Quadrant(Affairs-Mind-body-Objects') called Sa-Sang theory(四象學) or Sasang Constitional Medicine(四象醫學) is basic component in order to explain the structure of all nature. 'Heaven-Human-nature-Order'; That is, 'Affairs-Mind-body-Objects' results in 'Heaven-Human-nature-Order' focused on human being(human body) in the universe. In other words, Sa-Sang theory is the same as the Wilver's inclusive perspectives from the universe and human being. Dongmu's Sa-Sang theory is compared with C. G. Jung(1875~1961)'s psychology and it helps both fields confirm the foundation and extend the province of application. Comparision of Dongmu's Quadrant with Wilber's is not just analogical reasoning. In comparison, Dongmu's Sa-Sang theory is more delicated and crystallized than Wilber's in medical prospective. Dongmu regards Quadrant not as diseases, but as origin of Physiology and Pathology. And he explains all of their courses as Quadrant.
The purpose of this study is to validate the failure attribution factors affecting emotional changes after a failed service by local restaurant users, and the relapse effects of the perceived failure of a customer's brand relationship. In this study, the implications of this study can be divided into the null theory and the homogenous theory, in which the study of the relationship between individual belief that influences the null theory and the post-gender emotional response is minimal. The independence of the crash response (angerous VS compassion) has been equally validated as building a belief-gathering-emotion three-step model. First, emotional BRQ (intimate and love) has a reduction effect on controllable geeks, and behavioral BRQ (relative existence) has an extended effect on controllable geeks. From a management perspective, restaurant managers should be less aware of the repeatability of a customer's service failure and call for customer sympathy. Integratedly, restaurant managers must control the customer's perception of service failure and restore the impact of the customer's BRQ on emotional reactions. A variety of service recovery measures should be established and the cerumen should be controlled. In addition, since BRQs have different effects on anger and sympathy (extended VS), different service failure recovery plans should be presented depending on the characteristics of the customer BRQ. For example, measures such as monetary compensation or fair dealing, emotional distribution to close and loving customers, and persuasion of reciprocal benefits to interdependent customers should be developed according to circumstances. This study explored the effectiveness of the geeks after a service failure and has limitations that do not take into account the various regulatory factors in the BRQ-return-Empression process. Thus, in further studies, the effects of adjusting service failure strength should be considered and a more complete model should be built.
Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.
In this study, silicon sludge from semiconductor dicing process is recycled to fabricate silica nanoparticles, which are applied as dispersing materials for electro-responsive (ER) smart fluid. In specific, metal impurities are removed from silicon sludge by acid washing to obtain the high-purity silicon powder. And then, silica nanoparticles are synthesized by facile hydrothermal method employing the silicon powder as reactant material. To control the size of silica nanoparticles, the reaction time of hydrothermal method is varied as 8, 15, 20, and 30 hours are applied to control the size of silica nanoparticles. Sizes of silica nanoparticles are increased proportionally to the reaction time owing to the increased numbers of hydrolysis and condensation reactions. As-synthesized silica nanoparticles are prepared as electro-responsive smart fluids by dispersing into silicon oil. Silica nanoparticles synthesized by 30 hours of hydrothermal reaction (SiO2-H30) exhibit the highest shear stress of 21.4 Pa under an applied electric field strength of 3.0kV mm-1. Such enhancement in ER performance of SiO2-H30 among various silica nanoparticles are attribute to the reinforcing effect originated from the mixed particle size, which allowing the formation of rigid chain-like structures. Accordingly, this study successfully propose a recycling method of silicon sludge to synthesize silica nanoparticles and their derived ER fluids, which may suggest new possibility to ESG management emphasizing the eco-friendliness.
Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.
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