• Asian Pacific Journal of Cancer Prevention
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• v.16 no.13
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• pp.5377-5383
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• 2015

Background: Participation is one of the major factors affecting the long-term success of population-based prostate cancer screening programs. The aim of this study was to explore strong factors linked to participation in prostate cancer screening among older Jordanian adults using the Health Belief Model (HBM). Materials and Methods: Data were obtained from Jordanian older adults, aged 40 years and over, who visited a comprehensive health care center within the Ministry of Health. A pilot test was conducted to investigate the internal consistency of the the Champion Health Belief Model Scale for prostate cancer screening and the clarity of survey questions. Sample characteristics and rates of participation in prostate cancer screening were examined using means and frequencies. Important factors associated with participation in prostate cancer screening were examined using bivariate correlation and multivariate logistic regression analysis. Results: About 13% of the respondents had adhered to prostate cancer screening guidelines over the previous decade. Four out of the seven HBM-driven factors (perceived susceptibility, benefits and barriers to PSA test, and health motivation) were statistically significant. Those with greater levels of susceptibility, benefits of PSA test and health motivation and lower levels of barriers to PSA testing were more likely to participate in prostate cancer screening. Family history, presence of urinary symptoms, age, and knowledge about prostate cancer significantly predicted the participation in prostate cancer screening. Conclusions: Health professionals should focus more on the four modifiable HBMrelated factors to encourage older adults to participate in prostate cancer screening. Intervention programs, which lower perceived barriers to PSA testing and increase susceptibility, benefits of PSA testing and health motivation, should be developed and implemented.

• Development and Reproduction
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• v.23 no.4
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• pp.333-344
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• 2019

In vertebrate reproductive system, estrogen receptor (ER) plays a pivotal role in mediation of estrogenic signaling pathways. In the present study, we report the cDNA cloning, expression analysis, and transcriptional activity of ERβ1 subtype from medaka Oryzias dancena. The deduced O. dancena ERβ1 (odERβ1; 519 amino acids) contained six characteristic A/B to E/F domains with very short activation function 2 region (called AF2). A phylogenetic analysis indicated that odERβ1 was highly conserved among teleost ERβ1 subgroup. A conventional RT-PCR revealed that the odERβ1 transcripts were widely distributed in the multiple tissues, the ovary, brain, gill, intestine, kidney, and muscle. Further, the relatively higher odERβ1 expressions in the ovary and brain were clearly reproduced in RT-qPCR assay. When HA-fused odERβ1 expression vector was transfected into HEK293 cells, an immunoreactivity for odERβ1 was mainly detected in the nucleus part. Finally, an estrogen responsive element driven luciferase reporter assays demonstrated that the transcriptional activity of odERβ1 significantly increased by estradiol-17β (E2) in a dose dependent manner (p<0.05). However, fold-activation of odERβ1 in the presence of E2 was markedly weak, when it compared with those of O. latipes ERβ1. Taken together, these data suggest that odERβ1 represents a functional variant of teleost ERβ subtype and provides a basic tool allowing future studies examining the function of F domain of ERβ1 subtype and expanding our knowledge of ERβ evolution.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5396-5402
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• 2013

In this study, one for grades 1 Institute of Foundation Engineering Practice training to enable the creativity of problem-based learning as an alternative (PBL: Problem-Based Learning) training system was applied. The reason for the development of education systems engineering knowledge and practice skills related to the University no basis for first year students in the theory and practice based on the characteristics of the materials used and the tools to use, how to use the machine was to be accomplished. Hands-on training for existing structured learning plan driven by the faculty and students conducted hands-on training in the uniform limits on the ability of the practice has been shown. However, in this study on the basis of PBL learning materials, and the students themselves leading learner-centered practice problems derive and small unit exercises the ability to decide how to proceed on improving the educational process proposes. As a result of these studies, about 30% more than traditional hands-on training to improve the ability of learning respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.121-129
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• 2009

Today, it is difficult to re-use and share spatial information, because of the explosive growth of heterogeneous information and specific characters of spatial information accumulated by diverse local agency. A spatial analysis of subsurface spatial informa-tion, one of the National Spatial Data Infrastructure, needs related spatial information such as, topographical map, geologic map, underground facility map, etc. However, current methods using standard format or spatial datawarehouse cannot consider a se-mantic hetergenity. In this paper, the layered ontology model which consists of generic concept, measuremnt scale, spatial model, and subsurface spatial information has developed. Also, the current ontology building method pertained to human experts is a expensive and time-consuming process. We have developed the MDA-based metamodel(UML Profile) of ontology that can be a easy under-standing and flexiblity of environment change. The semantic quality of devleoped ontology model has evaluated by reasoning engine, Pellet. We expect to improve a semantic sharing, and strengthen capacities for developing GIS experts system using knowledge representation ability of ontology.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.131-138
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• 2008

This paper designed an Internet Convention e-Portal Information System through an experience and knowledges accumulated in case of information utilization regarding international meeting and conventions in knowledge information societies. Established the Non-stop Cluster system that was the H/W side, L4 and the applied system which was the 3-tier structure and Firewall, VPN, IDS/IPS security control system, S/W side, that utilized the WAS MVC architecture used WAS and a SOA architecture. an ASP EAI function. A user and a manager, designs and constructor, the user efficiency who were an evaluation basis of a Verification commissioner, Ubiquitous evaluated system stability. Information security anger, and evaluated former system and comparison. Therefore is evaluated to the excellent Convention e-Portal Systems that a performance 25.9% improvement consisted of compare with the existing Convention e-Portal Systems, and will contribute so as to be able to carry out-driven enemy role in development of the industrial our country Convention Ubiquitous Internet IT information Industry and International Society Conventions.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.475-486
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• 2013

Recent healthcare studies evaluate the effective impacts of a patient educational information intervention in hospitals on adherence to communicating knowledge, life quality, and therapeutic response in patients treated with highly active therapy in medical organizations. Therefore, the aim of this study is to design and implement the effective and systematic frameworks by providing clinical patient-oriented education system(CPES), which enhances health promotion and medical cures. CPES initiatives accelerate positive financial returns with network-driven educational system. To validate the CPES, the author analyzes the efficiency and effectiveness using statistical survey works. With this outcome, it is compared with previous system. One of the findings is that the system can improve compliance with treatment right dietetics and give patients self-manage their conditions in S hospital. Patients' acknowledge of their condition and its treatment process can be improved through the educational systems.

• Geotechnical Engineering
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• v.10 no.3
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• pp.79-96
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• 1994

A tunnel design expert system entitled NESTED is developed using the artificial neural network. The expert system includes three neural network computer models designed for the stability assessment of underground openings and the estimation of correlation between the RMR and Q systems. The expert system consists of the three models and the computerized rock mass classification programs that could be driven under the same user interface. As the structure of the neural network, a multi -layer neural network which adopts an or ror back-propagation learning algorithm is used. To set up its knowledge base from the prior case histories, an engineering database which can control the incomplete and erroneous information by learning process is developed. A series of experiments comparing the results of the neural network with the actual field observations have demonstrated the inferring capabilities of the neural network to identify the possible failure modes and the support timing. The neural network expert system thus complements the incomplete geological data and provides suitable support recommendations for preliminary design of tunnels in rock masses.

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.831-847
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• 2012

Impact from water-borne debris during tsunami and flood events pose a potential threat to structures. Debris impact forces specified by current codes and standards are based on rigid body dynamics, leading to forces that are dependent on total debris mass. However, shipping containers and other debris are unlikely to be rigid compared to the walls, columns and other structures that they impact. The application of a simple one-dimensional model to obtain impact force magnitude and duration, based on acoustic wave propagation in a flexible projectile, is explored. The focus herein is on in-air impact. Based on small-scale experiments, the applicability of the model to predict actual impact forces is investigated. The tests show that the force and duration are reasonably well represented by the simple model, but they also show how actual impact differs from the ideal model. A more detailed three-dimensional finite element model is also developed to understand more clearly the physical phenomena involved in the experimental tests. The tests and the FE results reveal important characteristics of actual impact, knowledge of which can be used to guide larger scale experiments and detailed modeling. The one-dimensional model is extended to consider water-driven debris as well. When fluid is used to propel the 1-D model, an estimate of the 'added mass' effect is possible. In this extended model the debris impact force depends on the wave propagation in the two media, and the conditions under which the fluid increases the impact force are discussed.