• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.12
• /
• pp.5295-5302
• /
• 2016

Objectives: A physically active lifestyle is important for cancer survivors. Therefore, this study was conducted to 1) provide population-based estimates of the prevalence of physical activity and sitting time, and 2) their correlates in Korean cancer survivors. Materials and Methods: This study analyzed a cancer survivor subsample (N=1,482) from 2008-2013 Korea National Health and Nutrition Examination Survey (KNHANES), data selected with a complex sampling design. Overall and subgroup-specific prevalences of physical activity and sitting time were estimated. Correlates of moderate- to vigorous-intensity physical activity ( MVPA) and sitting time were tested using age-group-specific hierarchical multiple regression models. Results: Overall adherence rate to physical activity guidelines was 34.9% (95% CI=31.5-38.4). Age-group-specific adherence rates were 41.1% (95% CI=36.3-45.9) in adults (30-64 years old), and 25.3% (95% CI=21.0-25.3) in older adults (65 years or older). Adults spent 213.33 minutes (95% CI=172.4-254.3) per week on MVPA and 55.3 minutes (95% CI=36.4-64.6) on sitting time per day. In adults, sitting time was significantly associated with employed status (B=28.0, p=0.046), smoking (B=-47.4, p=0.020), and number of comorbidity conditions (B=-13, p=.037). MVPA was significantly associated with marital status (B=134.9, p<0.001), employment status (B=98.12, p=.046), and years since cancer diagnosis (B=104.7, p=0.015). Older adults spent 162.2 minutes (95% CI=119.5-204.8) per week on MVPA and 63.0 minutes (95% CI=45.0-89.5) on sitting time per day. Their significant correlates were sex (B= -45.2, p=0.014), smoking (B=-70.14, p<0.001), and years since cancer diagnosis (B=37.0, p=0.024). Age (B=5.8, p=0.042) and marital status (B=83.8, p=0.033) were also significantly associated with MVPA in older adults. Conclusion: A majority of Korean cancer survivors do not sufficiently participate in physical activity. In general, older, unhealthier, non-working, and being unmarried were risk factors for physical inactivity. While this study informs public health policy makers and practitioners about physical activity intervention demand for cancer survivors, future investigations should address psychosocial mediators to better inform intervention programs.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.10
• /
• pp.1808-1814
• /
• 2008

In addition to RBL function, which is used to applying for spam e-mail filtering, as an effective way to deal with the recently widespread spam types, this paper proposes how to extract URL that was comprised in the original e-mail, apply it to RBL, and expand it. The BotNet, which is used to using for sending spam mails these days, has a problem that it is not able to solve with the distributed addresses of sent mails in spam e-mails. In general, as these spam e-mails are sent from the infected Zombi PC of individual user, the sent address itself is not efficient and is meaningless to use in RBL. As an effective way to filter spam e-mail sent by BotNet, this paper analyzes URLs that contained in the original spam e-mail and proposes how to effectively improve filter rate, based on the distribution data of URL site tempting users. This paper proposes the sending mechanism of spam e-mails from BotNet and the methods to realize those types of spam e-mails. In order to gather analyzable spam e-mails, this paper also carries out an experiment by configuring trap system of spam e-mail. By analyzing spam e-mails, which have been received during the certain period of experiment, this paper shows that the expanded RBL method, using URLs that contained in spam e-mails, is effective way to improve the filter distribution of spam e-mail.

• Korean Journal of Construction Engineering and Management
• /
• v.20 no.6
• /
• pp.89-97
• /
• 2019

Recently, BIM has been actively introduced in construction projects. In particular, the introduction of BIM in cost estimating process is expected to improve the accuracy and efficiency of the cost estimate. However, the quantity calculation and BoQ documents preparation process still require manual work. Although the BIM model may support quantification process, it is still problematic that the level of detail of the model must be very detailed to meet the items in BoQ. To address this, it is necessary to analyse the LOD criteria and to analyze the extent to which quantity can be computed according to the level of detail in the BIM model. For the analysis of LOD-based work items, the work item grades were divided into A,B, and C. In this study, the ratio and cost of each item that can be calculated at the LOD level in the detailed design phase are reviewed for each type of work, and the method for improving the quantity calculation using BIM is proposed. In the LOD 300 stage(Detailed design stage), the largest number of items in the class B, the major improvement class, are window and glass work. In addition, the most expensive type of work was analyzed by reinforced concrete work. In the future, it is necessary to suggest appropriate improvement way for items with high item ratios and items with high cost ratios. The results of this study are expected to be used as a BIM-based cost estimation or as basic data for improving the current BoQ system.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.6
• /
• pp.483-494
• /
• 2022

Owing to the saturation of ground spaces in downtown areas, underground spaces are being developed increasingly. Underground spaces are utilized for transportation, water supply and sewerage, communication zones, electric power zones, and various cultural complexes. In Korea, for excavating underground spaces, blasting methods using gunpowder such as the New Austrian Tunneling Method (NATM) are mainly used. However, the blasting method causes vibration and noise during tunnel excavation, generating many complaints from residents in the vicinity of the excavation site. To address this problem, various methods have been developed, and recently, vibration and noise have been reduced using deep excavation. This study predicts blast vibration changes according to the depth, under the same blasting and tunnel conditions, using numerical analysis based on the blast vibration measurement data of the GTX-A route, the tunnel cross-section drawings, and ground investigation reports. Furthermore, the necessary separation distance from densely populated areas such as residential areas is suggested by analyzing the trend of decreasing blast vibration according to the distance from ground surface directly above the blasting location.

• The Journal of Society for e-Business Studies
• /
• v.26 no.3
• /
• pp.97-117
• /
• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• The Journal of the Korea Contents Association
• /
• v.21 no.9
• /
• pp.431-445
• /
• 2021

The purpose of this study was to provide implications for social work practice by examining the influence of career barrier on smart-phone addiction through self-esteem and depression among out-of-school adolescents. Using data from the Panel Survey of School Dropouts conducted by the National Youth Policy Institute, this study examined the research questions by analyzing the direct and indirect influences of career barrier on smart-phone addiction with the PROCESS macro method. First, the results of this study showed that out-of-school adolescents' career barrier statistically significantly increased smart-phone addiction. Also, out-of-school adolescents' depression mediated the influence of career barrier on smart-phone addiction, but out-of-school adolescents' self-esteem did not mediate the influence. However, out-of-school adolescents' self-esteem and depression dual-mediated the influence of career barrier on smart-phone addiction. The level of career barrier decreased the level of self-esteem, and in turn increased the level of depression, and finally increased the level of smart-phone addiction. Based on the findings, practical strategies to address the smart-phone addiction of out-of-school adolescents were discussed.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.77-87
• /
• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of Veterinary Science
• /
• v.22 no.5
• /
• pp.68.1-68.15
• /
• 2021

Background: Colistin and carbapenem-resistant bacteria have emerged and become a serious public health concern, but their epidemiological data is still limited. Objectives: This study examined colistin and carbapenem resistance in Escherichia coli and Salmonella from pigs, pig carcasses, and pork in Thailand, Lao PDR, and Cambodia border provinces. Methods: The phenotypic and genotypic resistance to colistin and meropenem was determined in E. coli and Salmonella obtained from pigs, pig carcasses, and pork (n = 1,619). A conjugative experiment was performed in all isolates carrying the mcr gene (s) (n = 68). The plasmid replicon type was determined in the isolates carrying a conjugative plasmid with mcr by PCR-based replicon typing (n = 7). The genetic relatedness of mcr-positive Salmonella (n = 11) was investigated by multi-locus sequence typing. Results: Colistin resistance was more common in E. coli (8%) than Salmonella (1%). The highest resistance rate was found in E. coli (17.8%) and Salmonella (1.7%) from Cambodia. Colistin-resistance genes, mcr-1, mcr-3, and mcr-5, were identified, of which mcr-1 and mcr-3 were predominant in E. coli (5.8%) and Salmonella (1.7%), respectively. The mcr-5 gene was observed in E. coli from pork in Cambodia. Two colistin-susceptible pig isolates from Thailand carried both mcr-1 and mcr-3. Seven E. coli and Salmonella isolates contained mcr-1 or mcr-3 associated with the IncF and IncI plasmids. The mcr-positive Salmonella from Thailand and Cambodia were categorized into two clusters with 94%-97% similarity. None of these clusters was meropenem resistant. Conclusions: Colistin-resistant E. coli and Salmonella were distributed in pigs, pig carcasses, and pork in the border areas. Undivided-One Health collaboration is needed to address the issue.

• Journal of Climate Change Research
• /
• v.9 no.4
• /
• pp.303-312
• /
• 2018

In Ethiopia, climate change and deforestation are major issues hindering sustainable development. Local Ethiopian communities commonly perceive an increase in temperature and a decrease in rainfall. Meteorological data shows that rainfall has declined in southern Ethiopia, and spring droughts have occurred more frequently during the last 10-15 years. The frequently occurring droughts have seriously affected the agriculture-dominated Ethiopian economy. Forests can play an important role in coping with climate change. However, deforestation is alarmingly high in Ethiopia, and this is attributed mainly to agricultural expansion and fuel wood extraction. Deforestation has led to a decrease in various benefits from forest ecosystem services, and increased ecological and environmental problems including loss of biodiversity. To resolve the issues effectively, it is crucial to enhance climate change resilience through reforestation and various international collaborations are urgently needed. To continue collaboration activities for resolving these issues, it is first necessary to address fundamental questions on the nature of collaboration: does collaboration aim for a support-benefit or a mutual benefit situation; dividing the workload or sharing the workload; an advanced technology or an appropriate technology; and short-term and intensive or long-term and extensive?. Potential collaboration activities were identified by sectors: in the governmental sector, advancing governmental structure and policy, enhancing international collaborations and negotiations, and capacity building for forest restoration and management; in the research and education sector, identifying and filling gaps in forestry and climate change education, capacity building for reforestation and climate change resilience research, and developing bioenergy and feed stocks; and in the business and industry sector, supporting conservation based forestry businesses and industries, while promoting collaboration with the research and education sectors. It is envisaged that international collaboration for enhancing climate change resilience through reforestation will provide a strong platform for resolving climate change and deforestation issues, and achieving sustainable development in Ethiopia.

• Journal of Navigation and Port Research
• /
• v.42 no.6
• /
• pp.539-545
• /
• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.