• Journal of Society of Preventive Korean Medicine
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• v.16 no.1
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• pp.1-13
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• 2012

The objective of this study is to discuss the role of integrative medicine (IM) in contemporary health care settings, and how and which factors affect and facilitate the success of IM in terms of the integration of complementary and alternative medicine (CAM) and conventional medicine in primary health care (PHC). IM is meant to provide the best possible health care, for both patient and physician. The way of IM use in the developed countries presents various ways that IM can be provided, and it appears that strategies have been successfully developed to facilitate integration. Although few of the barriers to the integration of CAM and conventional medicine have been resolved, concerns over the legitimacy of CAM in health care (e.g., safety, biomedical evidence, and efficacy) are being overcome by the use of evidence-based practice in IM delivery. There are dominant models of IM that have been developed. The model types signify different levels of equity between CAM and conventional medicine in regard to the power, autonomy, and control held by each. However, the factors common to all IM models, whether describing CAM as supplementary or complementary to conventional medicine, is the concept of a health care model that aspires to be client-centred and holistic, with focus on health rather than disease as well as mutual respect among peer practitioners. Finally, this study concluded that the growth and viability of traditional Korean medicine(TKM) depend on evidence-based practices and identifying the successful influences on the integration of TKM and conventional medicine for recognition of its inherent value in PHC. Some recommendations for the integration of TKM and conventional medicine were suggested.

• Computers and Concrete
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• v.12 no.6
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1226-1234
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• 2015

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.260-268
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• 2008

Optical diffuse reflectance sensing has potential for rapid and reliable on-site estimation of soil properties. For good results, proper calibration to measured soil properties is required. One issue is whether it is necessary to develop calibrations using samples from the specific area or areas (e.g., field, soil series) in which the sensor will be applied, or whether a general "factory" calibration is sufficient. A further question is if specific calibration is required, how many sample points are needed. In this study, these issues were addressed using data from 42 paddy fields representing 14 distinct soil series accounting for 74% of the total Korean paddy field area. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on coefficient of determination ($R^2$) root mean square error of prediction (RMSEP), and RPD, the ratio of standard deviation to RMSEP. When sample data from a soil series were included in the calibration stage (full information calibration), RPD values of prediction models were increased by 0.03 to 3.32, compared with results from calibration models not including data from the test soil series (calibration without site-specific information). Higher $R^2$ values were also obtained in most cases. Including some samples from the test soil series (hybrid calibration) generally increased RPD rapidly up to a certain number of sample points. A large portion of the potential improvement could be obtained by adding about 8 to 22 points, depending on the soil properties to be estimated, where the numbers were 10 to 18 for pH, 18-22 for EC, and 8 to 22 for total C. These results provide guidance on sampling and calibration requirements for NIR soil property estimation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.5
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• pp.379-384
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• 2014

This article describes the dynamic behaviors of a rubber tired gantry crane(RTGC) under typical load conditions which are used in the design of gantry cranes. In order to investigate the dynamic characteristics of an RTGC, we developed a finite element crane model for its huge structure. The finite element model was validated with the modal test results, e.g., natural frequencies and normal modes. And other components of RTGC were converted into detailed 3D CAD models and finally transformed to rigid body models in a dynamic simulation program ADAMS. The load conditions considered in this paper were a normal operating condition(OP1) and container hanging condition with no external loads. As a result, we could find there was large influence of crane's vibration owing to its structural stiffness and deformation. And the vibration of crane could made the movements of RTGC, which occurs crash or malfunction of crane works.

• Advances in aircraft and spacecraft science
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• v.4 no.4
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• pp.353-369
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• 2017

Flutter is a dangerous phenomenon encountered in flexible structures subjected to aerodynamic forces. This includes aircraft, buildings and bridges. Flutter occurs as a result of interactions between aerodynamic, stiffness, and inertia forces on a structure. In an aircraft, as the speed of the flow increases, there may be a point at which the structural damping is insufficient to damp out the motion which is increasing due to aerodynamic energy being added to the structure. This vibration can cause structural failure, and therefore considering flutter characteristics is an essential part of designing an aircraft. Scientists and engineers studied flutter and developed theories and mathematical tools to analyze the phenomenon. Strip theory aerodynamics, beam structural models, unsteady lifting surface methods (e.g., Doublet-Lattice) and finite element models expanded analysis capabilities. Periodic Structures have been in the focus of research for their useful characteristics and ability to attenuate vibration in frequency bands called "stop-bands". A periodic structure consists of cells which differ in material or geometry. As vibration waves travel along the structure and face the cell boundaries, some waves pass and some are reflected back, which may cause destructive interference with the succeeding waves. This may reduce the vibration level of the structure, and hence improve its dynamic performance. In this paper, for the first time, we analyze the flutter characteristics of a wing with a periodic change in its sandwich construction. The new technique preserves the external geometry of the wing structure and depends on changing the material of the sandwich core. The periodic analysis and the vibration response characteristics of the model are investigated using a finite element model for the wing. Previous studies investigating the dynamic bending response of a periodic sandwich beam in the absence of flow have shown promising results.

• Biomolecules & Therapeutics
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• v.22 no.5
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• pp.460-466
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• 2014

Nicotine addiction is a worldwide problem. However, previous studies characterizing the rewarding and reinforcing effects of nicotine in animal models have reported inconsistent findings. It was observed that the addictive effects are variable on different factors (e.g. route, dose, and age). Here, we evaluated the rewarding and reinforcing effects of nicotine in different routes of administration, across a wide dose range, and in different age groups. Two of the most widely used animal models of drug addiction were employed: the conditioned place preference (CPP) and self-administration (SA) tests. Nicotine CPP was evaluated in different routes [intraperitoneal (i.p.) and subcutaneous (s.c.)], doses (0.05 to 1.0 mg/kg) and age [adolescent and adult rats]. Similarly, intravenous nicotine SA was assessed in different doses (0.01 to 0.06 mg/kg/infusion) and age (adolescent and adult rats). In the CPP test, s.c. nicotine produced greater response than i.p. The 0.2 mg/kg dose produced highest CPP response in adolescent, while 0.6 mg/kg in adult rats; which were also confirmed in 7 days pretreated rats. In the SA test, adolescent rats readily self-administer 0.03 mg/kg/infusion of nicotine. Doses that produced nicotine CPP and SA induced blood nicotine levels that corresponded well with human smokers. In conclusion, we have demonstrated that nicotine produces reliable CPP [0.2 mg/kg dose (s.c.)] in adolescents and [0.6 mg/kg dose (s.c.)] in adults, and SA [0.03 mg/kg/infusion] in adolescent rats. Both tests indicate that adolescent rats are more sensitive to the rewarding and reinforcing effects of nicotine.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.311-314
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• 2006

This paper concerns about total dynamic analysis of integrated mining system. This system consists of vertical steel pipe, intermediate buffer station, flexible pipe and self-propelled miner. The self-propelled miner and buffer are assumed as rigid-body of 6-dof. Discrete models of vertical steel pipe and flexible pipe are adopted, which are obtained by means of lumped-parameter method. The motion of mining vessel is not considered. Instead, the motion of mining vessel is taken into account in form of various boundary conditions (e.g. forced excitation in slow motion and/or fast oscillation and so on). A terramechanics model of extremely soft cohesive soil is applied to the self-propelled miner. The hydrodynamic forces and moments are included in the dynamic models of vehicle and lifting pipe system. Hinged and fixed constraints are used to define the connections between sub-systems (vertical steel pipe, buffer, flexible pipe, miner). Equations of motion of the coupled model are derived with respect to the each local coordinates system. Four Euler parameters are used to express the orientations of the sub-systems. To solve the equations of motion of the total dynamic model, an incremental-iterative formulation is employed. Newmark-b method is used for time-domain integration. The total dynamic responses of integrated mining system are investigated.

• 한국해양학회지
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• v.26 no.2
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• pp.108-116
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• 1991

A production model was constructed by combining the production rate and biomass of Sargassum confusum measured at monthly intervals on the coast of Ohori, Korea, to estimate the algal production for a given period. The production for a certain period, e.g., for a year (P/SUB yr/), was calculated from the equation: P/SUB yr/ = .int.P/SUB t/$.$B/SUB t/dt, where pl and Bl are the production rate and biomass at time t. P/SUB l/ was considered as a function of temperature and light. Photosynthesis-Irradiance curves obtained from the in situ experiments were applied for P/SUB l/ Temperature and light intensity can be expressed as periodic functions of time (T, L=f(t)). Diurnal values of water temperature and light intensity at 3 m depth where S. confusum mainly found were substituted into the equation of P/SUB l/. Simulations using our models show that temperature was one of the most sensitive factors operating on the primary production. Thirty percent decrease of light intensity by cloud cover was estimated to decrease the annual production by 5%.

• Smart Media Journal
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• v.9 no.2
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• pp.22-32
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• 2020

The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer's disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy.