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- P-ISSN1738-6764
- E-ISSN2093-7504
- KCI
ISSN : 1738-6764
Vol.13 No.2
Abstract
Residents under hazard and disaster conditions should be evacuating to the pre-assigned nearest safety facilities, community hall, local schools, friend's houses, etc. in a safety zone, quickly as soon as possible. The small percentage of evacuees shows serious economy class syndrome after the quake, because those people are forced to a stressful dairy evacuee-life in scattered homes and/or in-vehicle, for instance. Then, we consider on supporting evacuees using heart rate variability, Geospatial Positioning System (GPS) and WiFi functions of a smartphone, and Web-server on the Internet to keep their health in good conditions.
Abstract
It is a well-known fact that Internet addiction adversely affects mental health of adolescents. This study was conducted to determine whether there is a difference in the experience of depression according to the level of Internet addiction. Participants included 73,238 middle and high school students from the Korean Youth Risk Behavior Web-based Survey (KYRBWS) conducted in 2010. The level of Internet addiction and the experience of depression were assessed using self-diagnosis questionnaires. Multiple logistic regression analysis was used to identify the association between Internet addiction and depression. High-risk and potential-risk Internet users were 1.61 times and 1.21 times more likely to experience depression, respectively, than normal Internet users. The increase in depression was more significant in girls students. Acknowledging the connection between Internet addiction and depression, the problem should be tackled from the perspective of school health by providing systematic Internet addiction prevention and treatment programs
Abstract
The purpose of this study was to investigate the association of obesity with medical care use and costs according to overall diseases, cerebrovascular diseases (CVD), ischemic heart disease (IHD), hypertension (HTN) and diabetes mellitus (DM). The final sample was a group of persons who were free of diseases mentioned above and were not underweight. Their baseline screening program data and health insurance contribution data were connected with a 7-year medical claim database. The participants were classified according to their baseline BMI into normal, overweight, obese, and severely obese groups. Given the disease type, the total costs of DM showed the largest difference in each obesity group in both males and females. Also, the pharmacy costs for DM were more relevant than any other type of service to the obesity level. Considering the high prevalence of obesity and the relevantly increased medical care use and costs, there is a need for reduction in medical costs through obesity prevention efforts.
Abstract
Abnormal data in the manufacturing process makes it difficult to find useful information that can be applied in data management for the manufacturing industry. It causes various problems in the daily process of production. An issue from the abnormal data can be handled by our method that uses big data and visualization. Visualization is a new technology that transforms data representation into a two-dimensional representation. Nowadays, many newly developed technologies provide data analysis, algorithm, optimization, and high efficiency, and they meet user requirements. We propose combined production of the data visualization approach that uses integrative visualization of sources of abnormal pattern analysis results. The perceived idea of the proposed approach can solve the problem as it also works for big data. It can also improve the performance and understanding by using visualization and solving issues that occur in the manufacturing process with a calendar heat map.
Abstract
Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.
Abstract
This study sought to measure the influence of HIMs’ work environment changes on job stress, and to explore measures for improving job satisfaction among them. A total of 275 hospital HIMs’ were surveyed using a structured questionnaire. Significant job stress impact variables were sorted out using a simple linear regression analysis. Then, through multiple linear regression analysis, multicollinearity was tested. Significant impact factors were identified from among the control variables, and job stress impact was measured. The survey revealed that in public hospitals where the EMR system has been implemented for a longer period, depression scores in HIMs’ were increased. HIMs’ job stress level was found to be affected by the following factors: computerization of their working environment, experience of depression, unemployment, and manpower reduction, as well as, their lifestyles, including leisure activities. The results of this study suggest that HIMs’ job stress can be reduced through work environment improvement and improvement of their personal lifestyle habits
Abstract
This article presents a concept analysis of the gratitude experience of diseased patients. The Rodgers’ Evolutionary Method was used for conducting the analysis. A search of CINAHL, MEDLINE, PsycARTICLES, and Springer databases was conducted using “gratitude or appreciation or thanks” and “patient or illness” as a key word, 22 final articles were selected. Three critical attributes of gratitude in patients were identified: positive emotions, acceptance of the current status, and a driving force to plant the will of life. In addition, two antecedents of gratitude in patients were identified: interactions with people or the environment, and the perception of a favorable stimuli or help. Two consequences of gratitude in patients were identified: an increased compliance in implemented treatment, and an enhancement of trust relationship. The concept analysis describes diseased patients’ gratitude. This paper will become the basis for future clinical research related to diseased patients’ gratitude.
Abstract
The number of accidents in school zone is decreasing than before with the introduction of strengthening traffic safety policy since January 2011, but the danger still exists. The School zone sign is widely known to have much effect in protecting children from risks of traffic accidents, but design improvement is being demanded to improve a sense of safety and legibility of safety signs in School zone due to the lack of understanding on the safety signs in crosswalk and School zone. This study analyzed differences in shape and color of existing safety signs through a case analysis of traffic developed countries as America, England, Japan, and Germany and suggested improvement plans for drivers to clearly perceive the school zone. For improvement methods, this study suggested the importance of delivering definite and unified warning message for school zone to drivers by using indication sign and caution sign together, and to use yellow, a safety color, and to unify the safety sign into triangle shape that symbolizes warning and caution to conform the international standards. Actual design production and experiment through improvement plans are needed in the future, and it is expected to secure safety of children and to provide international standardization of safety signs in school zone.
Abstract
Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.
Abstract
Patent classification is becoming more critical as patent filings have been increasing over the years. Despite comprehensive studies in the area, there remain several issues in classifying patents on IPC hierarchical levels. Not only structural complexity but also shortage of patents in the lower level of the hierarchy causes the decline in classification performance. Therefore, we propose a new method of classification based on different criteria that are categories defined by the domain’s experts mentioned in trend analysis reports, i.e. Patent Landscape Report (PLR). Several experiments were conducted with the purpose of identifying type of features and weighting methods that lead to the best classification performance using Support Vector Machine (SVM). Two types of features (noun and noun phrases) and five different weighting schemes (TF-idf, TF-rf, TF-icf, TF-icf-based, and TF-idcef-based) were experimented on.