• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.641-644
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• 2000

In vitro true digestibility of cup-plant (Silphium perfoliatum L.) is higher than other alternative forages and comparative to alfalfa (Medicago sativa L.) even at the high neutral detergent fiber (NDF) concentration. This study was conducted to determine whether the digestion kinetic parameters of cup-plant could explain high in vitro true digestibility of cup-plant at the several NDF levels. Cup-plant and alfalfa were both collected in Arlington and Lancaster, Wisconsin to meet the NDF content within 40 to 50% range. The collected samples were incubated with rumen juice to investigate the digestion kinetics at 3, 6, 9, 14, 20, 28, 36, 48, and 72 h. Kinetics was estimated by the model $R=D_0\;e-k(t-L)+U$ where R is residue remaining at time t, and $D_0$ is digestible fraction, k is digestion rate constant, L is discrete lag time, and U is indigestible fraction. Parameters of the model were estimated by the direct nonlinear least squares (DNLS) method. Digestion rate and potential extent of digestion were not statistically different in either forage. However, alfalfa had shorter lag time (p<0.05). The indigestible fraction increased with maturation in alfalfa and in cup-plant (p<0.05). The ratio of indigestible fraction to acid detergent lignin (ADL) was higher in cup-plant than in alfalfa (p<0.05). From the results, alfalfa is probably digested more rapidly than cup-plant, however, cup-plant maintains higher digestibility with maturation due to a relatively slower increase of indigestible fraction in NDF.

• Phonetics and Speech Sciences
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• v.8 no.3
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• pp.31-38
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• 2016

Speech inversion (acoustic-to-articulatory mapping) is not a trivial problem, despite the importance, due to the highly non-linear and non-unique nature. This study aimed to investigate the performance of Deep Neural Network (DNN) compared to that of traditional Artificial Neural Network (ANN) to address the problem. The Wisconsin X-ray Microbeam Database was employed and the acoustic signal and articulatory pellet information were the input and output in the models. Results showed that the performance of ANN deteriorated as the number of hidden layers increased. In contrast, DNN showed lower and more stable RMS even up to 10 deep hidden layers, suggesting that DNN is capable of learning acoustic-articulatory inversion mapping more efficiently than ANN.

• Asia Marketing Journal
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• v.24 no.2
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• pp.62-77
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• 2022

Seeded marketing campaign (SMC) is a newly created type of marketing activities with the widespread use of social media. Previous research has examined to find out the optimal seeding strategy that yields the best outcome from the campaign. This research explores the relationships between the characteristics of the seeded influencer and user engagement. The data consists of information from 1062 seeded Instagram posts posted in September 2020 in Korea and 778 seeded influencers who posted those contents. Analyzed by negative binomial regression, our quadratic model suggests that the relationship between user engagement and the number of followers of the seeded influencer draws an inverted U-shape, indicating influencers with greater number of followers may not always be the best choice for the marketers. Moreover, this research shows that the negative marginal impact coming from the huge number of followers can be attenuated when the influencer is an expert of the seeded product.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Wind and Structures
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• v.26 no.3
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• pp.147-161
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• 2018

For wind engineering applications downbursts are, presently, almost exclusively modeled, both experimentally and numerically, as transient impinging momentum jets (IJ), even though that model contains none of the physics of real events. As a result, there is no connection between the IJ-simulated downburst wind fields and the conditions of formation of the event. The cooling source (CS) model offers a significant improvement since it incorporates the negative buoyancy forcing and baroclinic vorticity generation that occurs in nature. The present work aims at using large-scale numerical simulation of downburst-producing thunderstorms to develop a simpler model that replicates some of the key physics whilst maintaining the relative simplicity of the IJ model. Using an example of such a simulated event it is found that the non-linear scaling of the velocity field, based on the peak potential temperature (and, hence, density) perturbation forcing immediately beneath the storm cloud, produces results for the radial location of the peak radial outflow wind speeds near the ground, the magnitude of that peak and the time at which the peak occurs that match well (typically within 5%) of those produced from a simple axi-symmetric constant-density dense source simulation. The evolution of the downdraft column within the simulated thunderstorm is significantly more complex than in any axi-symmetric model, with a sequence of downdraft winds that strengthen then weaken within a much longer period (>17 minutes) of consistently downwards winds over almost all heights up to at least 2,500 m.

• The Journal of Korean Academic Society of Nursing Education
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• v.24 no.1
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• pp.100-109
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• 2018

Purpose: This study evaluated the effect of a teaching model to improve cultural competency (TMCC) for Korean undergraduate nursing students. Methods: A nonequivalent control group pretest/posttest quasi-experimental study was conducted with a convenience sample of 168 undergraduate nursing students in South Korea. The experimental group of 121 seniors was taught a 13-week teaching model in order to improve cultural competence. A control group with 47 junior students underwent nursing major courses, but did not take this teaching model. Before and after the program, students' level of cultural competency was measured using the Questionnaire for Cultural Competence (QCC) consisting of three sub-scales: "awareness and desire," "encounter," and "nursing skill and knowledge." Results: After the experiment, the experimental group showed significantly higher improvement in the Questionnaire for Cultural Competence in the three sub-scales of "awareness and desire," "encounter," and "nursing skill and knowledge" than the control group (p=<.050). Conclusion: A teaching model to improve cultural competence was effective in improving Korean undergraduate nursing students' cultural competency. Further studies need to be repeated in order to identify the effectiveness of the teaching model to improve cultural competency with graduate or clinical nurses.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.46-51
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• 2004

his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.

• Biomolecules & Therapeutics
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• v.20 no.4
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• pp.386-392
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• 2012

The endothelin (ET) signaling pathway controls many physiological processes in myocardium and often becomes upregulated in heart diseases. The aim of the present study was to investigate the effects of ET receptor upregulation on the contractile function of adult ventricular myocytes. Primary cultured adult rat ventricular myocytes were used as a model system of ET receptor overexpression in the heart. Endothelin receptor type A ($ET_A$) or type B ($ET_B$) was overexpressed by Adenoviral infection, and the twitch responses of infected ventricular myocytes were measured after ET-1 stimulation. Overexpression of $ET_A$ exaggerated positive inotropic effect (PIE) and diastolic shortening of ET-1, and induced a new twitch response including twitch broadening. On the contrary, overexpression of $ET_B$ increased PIE of ET-1, but did not affect other two twitch responses. Control myocytes expressing endogenous receptors showed a parallel increase in twitch amplitude and systolic $Ca^{2+}$ in response to ET-1. However, intracellular $Ca^{2+}$ did not change in proportion to the changes in contractility in myocytes overexpressing $ET_A$. Overexpression of $ET_A$ enhanced both systolic and diastolic contractility without parallel changes in $Ca^{2+}$. Differential regulation of this nature indicates that upregulation of $ET_A$ may contribute to diastolic myocardial dysfunction by selectively targeting myofilament proteins that regulate resting cell length, twitch duration and responsiveness to prevailing $Ca^{2+}$.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1017-1026
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• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• Interaction and multiscale mechanics
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• v.2 no.3
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• pp.235-261
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• 2009

This study illustrates the differences between the elasto-plastic cap model and Lade's model with Cosserat rotation through the analyses of two large-scale geosynthetic-reinforced soil (GRS) retaining wall tests that were brought to failure using a monotonically increasing surcharge pressure. The finite element analyses with Lade's model were able to reasonably simulate the large-scale plane strain laboratory tests. On average, the finite element analyses gave reasonably good agreement with the experimental results in terms of global performances and shear band occurrences. In contrast, the cap model was not able to simulate the development of shear banding in the tests. In both test simulations the cap model predicted failure loads that were substantially less than the measured ones.