• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.441-452
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• 2022

Training using metacognition in a learning environment is one of the topics that have been continuously studied since the 1990s. Metacognition can be broadly divided into declarative metacognitive knowledge and procedural metacognitive knowledge (metacognitive skills). Accordingly, metacognitive training has also been studied focusing on one of the two metacognitive knowledge. The purpose of this study was to examine the role of metacognitive skills training in the context of mathematical problem solving. Specifically, the learner performed the prediction of problem difficulty, estimation of problem solving time, and prediction of accuracy in the context of a test in which problems of various difficulty levels were mixed within a set, and this was repeated 5 times over a total of 5 weeks. As a result of the analysis, we found that there was a significant difference in all three predictive indicators after training than before training, and we revealed that training can help learners in problem-solving strategies. In addition, we analyzed whether there was a difference between the experiment group and control group in the degree of test anxiety and math achievement. As a result, we found that learners in the experiment group showed less emotional and relationship anxiety at 5 weeks. This effect through metacognitive skill training is expected to help learners improve learning strategies needed for test situations.

• Ecology and Resilient Infrastructure
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• v.9 no.2
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• pp.100-106
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• 2022

The relationships between river length and weir density versus fish species observed were analyzed for 210 local rivers in the Seomjin River system (SJR). A nonlinear exponential relationship between river length and number of fish species were observed. Model coefficient was 0.03 and coefficient of determinant (R²) was 0.59, meaning that about 59.0% of total variance was explained by river length variable. Predicted value by model and observed number of species showed a difference. About 110 local rivers (about 52.4%) showed lower value than predictive value. The average index of weir's density (IWD) in the SJR was about 2.7/km, which was significantly higher than that of other river basins. As a result of nonparametric 2-Kimensional Kolmogorov-Smirnov (2-DKS) analysis based on the IWD, the threshold value affecting fish diversity was about 2.5/km (D_max=0.048, p<0.05). Above the threshold value, it means that the number of fish species would be decreased. In fact, the ratio of the expected species to the observed species was lowered to less than 70%, when the IWD is higher than the threshold value. To maintain aquatic ecological connectivity in future, it is necessary to manage IWD below the threshold value.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.71-87
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• 2021

This study aims to reveal how a port affects the living conditions of its neighborhood area with a case study of Pusan New Port and to suggest several implications to port policy. PLS-SEM reflective measurement model satisfies the criteria on reliability and validity, and also structural model meets the criteria in terms of R2, path coefficients' significance and predictive relevance(Q2). The results of PLS-SEM support the hypotheses of this study: The expansion of Pusan New Port contributes to the improvement of living conditions of Gangseo-gu(nearby area) through its significant and sequential effects on the employment and population increase of Gangseo-gu. The originality of this study can be found in enunciating that a port plays a role as a driving force of the betterment of living conditions of its nearby Gu-level area. In terms of policy, central and local governments and port related companies should cooperate with each other to reinforce the acceptability of port policy through the improvement of the living conditions of port neighborhood area. To evaluate comprehensively the influence of a port on its neighborhood area, a further study needs to identify how a port affects the quality of life of the area or what kinds of socio-economic effects a port has on the area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.127-134
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• 2022

Recently, policies and research to prevent increasing construction accidents have been actively conducted in the domestic construction industry. In previous studies, the prediction model developed to prevent construction accidents mainly used only structured data, so various characteristics of construction sites are not sufficiently considered. Therefore, in this study, we developed a machine learning-based construction accident prediction model that enables the characteristics of construction sites to be considered sufficiently by using both structured and text-type unstructured data. In this study, 6,826 cases of construction accident data were collected from the Construction Safety Management Integrated Information (CSI) for machine learning. The Decision forest algorithm and the BERT language model were used to train structured and unstructured data respectively. As a result of analysis using both types of data, it was confirmed that the prediction accuracy was 95.41 %, which is improved by about 20 % compared to the case of using only structured data. Conclusively, the performance of the predictive model was effectively improved by using the unstructured data together, and construction accidents can be expected to be reduced through more accurate prediction.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.765-777
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• 2022

The purpose of this study is to present a digital transformation-based learning model that can be used in universities based on learning digital transformation in order f to be competitive in a rapidly changing environment. Literature review, case study, and focus group interview were conducted and the implications for the learning model from these are as follows. Universities that stand out in related fields are actively using learning analysis to implement dashboards, develop predictive models, and support adaptive learning based on big data, They also have actively introduced advanced edutech to classes. In addition, problems and difficulties faced by other universities and K University when implementing digital transformation were also confirmed. Based on these findings, a digital transformation-based learning model of K University was developed. This model consists of four dimensions: diagnosis, recommendation, learning, and success. It allows students to proceed with learning by diagnosing and recommending various learning processes necessary for individual success, and systematically managing learning outcomes. Finally, academic and practical implications about the research results were discussed.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of The Korean Society of Clinical Toxicology
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• v.19 no.2
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• pp.83-92
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• 2021

Purpose: Glyphosate herbicide (GH) is a widely used herbicide and has been associated with significant mortality as poisoned cases increases. One of the reasons for high toxicity is thought to be toxic effect of its ingredient with glyphosate. This study was designed to determine differences in the clinical course with the salt-type contained in GH. Methods: This was a retrospective study conducted at a single hospital between January 2013 and December 2017. We enrolled GH-poisoned patients visited the emergency department. According to salt-type, patients were divided into 4 groups: isopropylamine (IPA), ammonium (Am), potassium (Po), and mixed salts (Mi) groups. The demographics, laboratory variables, complications, and their mortality were analyzed to determine clinical differences associated with each salt-type. Addtionally, we subdivided patients into survivor and non-survivor groups for investigating predictive factors for the mortality. Results: Total of 348 GH-poisoned patients were divided as follows: IPA 248, Am 41, Po 10, and Mi 49 patients. There was no difference in demographic or underlying disease history, but systolic blood pressure (SBP) was low in Po group. The ratio of intentional ingestion was higher in Po and Mi groups. Metabolic acidosis and relatively high lactate level were presented in Po group. As the primary outcome, the mortality rates were as follows: IPA, 26 (10.5%); Am, 2 (4.9%); Po, 1 (10%); and Mi, 1 (2%). There was no statistically significant difference in the mortality and the incidence of complications. Additionally, age, low SBP, low pH, corrected QT (QTc) prolongation, and respiratory failure requiring mechanical ventilation were analyzed as independent predictors for mortality in a regression analysis. Conclusion: There was no statistical difference in their complications and the mortality across the GH-salt groups in this study.

• Journal of Gastric Cancer
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• v.22 no.2
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• pp.120-134
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• 2022

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.941-953
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• 2022

In this study, monthly water use data were collected for 5 years from the 65 local governments included in the Han-river basin and a typical water usage ratios and patterns were calculated. The difference in water shortage was compared by considering the water usage patterns using the water balance and budget analysis model (MODSIM) and data base. As a result, it was confirmed that the change occurred in the range of -3.120% to +4.322% compared to the monthly constant ratio by period. In addition, when applying the patterns in the water balance model, 17 of the 28 middle watershed showed changes in the quantity of water shortage and the domestic and industrial water shortage would decrease about 8.0% during the maximum drought period. If it is applied in conjunction with predictive research on water usage patterns reflecting climate change, social and regional characteristics in the future, it will be possible to establish a more realistic water supply forecasts and a reliable national water resources plan.

• Korean Journal of Culture and Social Issue
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• v.29 no.1
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• pp.1-23
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• 2023

The purpose of this study was to validate the Suicidal Ideation Attributes Scale(SIDAS) which can measure the severity of suicidal ideation in a sample of 399 Korean adults. For this purpose, an online survey was conducted for two weeks from July 2020 to August 2020. First, there were differences in SIDAS scores among groups divided by gender, residence status, depression and anxiety symptoms, suicidal ideation, suicidal plan, suicidal preparation, and suicidal attempt. Second, correlations between SIDAS, C-SSRS suicidal ideation intensity question(3 items), and the Rosenberg Self-efficacy (RSE) were examined to confirm the validity of SIDAS. As a result, correlations between the SIDAS and suicidal ideation intesity items of C-SSRS were significant in all items, while correlations between the SIDAS and RSE items were negative or insignificant. Third, as a result of the confirmatory factor analysis of SIDAS on all respondents and respondents with suicidal ideation, a single factor structure was appropriate for both groups. Internal consistency of SIDAS was also good. Lastly, as a result of identifying the variables affecting the suicidal plan over the past year, controllability and age were identified as significant predictive variables. SIDAS which was designed to be administered through web, can be appropriately used in Korea. It was confirmed that SIDAS is a reliable and valid suicidal ideation scale which is applicable to adults in Korea.