• The Korean Journal of Applied Statistics
• /
• v.35 no.2
• /
• pp.311-325
• /
• 2022

For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.

• Communications for Statistical Applications and Methods
• /
• v.26 no.3
• /
• pp.239-259
• /
• 2019

The transmuted generalized extreme value (TGEV) distribution was first introduced by Aryal and Tsokos (Nonlinear Analysis: Theory, Methods & Applications, 71, 401-407, 2009) and applied by Nascimento et al. (Hacettepe Journal of Mathematics and Statistics, 45, 1847-1864, 2016). However, they did not give explicit expressions for all the moments, tail behaviour, quantiles, survival and risk functions and order statistics. The TGEV distribution is a more flexible model than the simple GEV distribution to model extreme or rare events because the right tail of the TGEV is heavier than the GEV. In addition the TGEV distribution can adjusted various forms of asymmetry. In this article, explicit expressions for these measures of the TGEV are obtained. The tail behavior and the survival and risk functions were determined for positive gamma, the moments for nonzero gamma and the moment generating function for zero gamma. The performance of the maximum likelihood estimators (MLEs) of the TGEV parameters were tested through a series of Monte Carlo simulation experiments. In addition, the model was used to fit three real data sets related to financial returns.

• Journal of Navigation and Port Research
• /
• v.41 no.5
• /
• pp.297-302
• /
• 2017

The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel's position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity ${\alpha}-{\beta}$ filter, the ${\alpha}-{\beta}-{\gamma}$ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The ${\alpha}-{\beta}-{\gamma}$ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter's performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, ${\alpha}-{\beta}-{\gamma}-{\eta}$ algorithm as compared to the constant acceleration model, ${\alpha}-{\beta}-{\gamma}$ in terms of error reduction and stability of the filter during target maneuver.

• Nuclear Engineering and Technology
• /
• v.55 no.3
• /
• pp.1071-1083
• /
• 2023

The on-the-fly energy release per fission (OTFK) model is implemented in STREAM to continuously update the Kappa values during the depletion calculation. The explicit neutron and photon energy distribution, which has not been considered in previous STREAM versions, is incorporated into the existing on-the-fly model. The impacts of the modified OTFK model with explicit neutron and photon heating in STREAM on the power distribution, fuel temperature, and other core parameters during depletion with feedback calculations are studied using several problems from the VERA benchmark suit. Overall, the explicit heating calculation provides a better power map for the feedback calculations particularly when strong gamma emitters are present. Generally, the fuel temperature decreases when neutron and photon heating is employed because fission neutrons and gamma rays are transported away from their points of generation. This energy release model in STREAM indicates that gamma energy accounts for approximately 9.5%-10% of the total energy released, and approximately 2.4%-2.6% of the total energy released will be deposited in the coolant for the VERA 5, NuScale, and Yonggwang Unit 3 2D cores.

• Journal of Astronomy and Space Sciences
• /
• v.29 no.1
• /
• pp.51-55
• /
• 2012

A few high-mass X-ray binaries-consisting of an OB star plus compact companion-have been observed by Fermi and ground-based Cerenkov telescopes like High Energy Stereoscopic System (HESS) to be sources of very high energy (VHE; up to 30 TeV) ${\gamma}$-rays. This paper focuses on the prominent ${\gamma}$-ray source, LS 5039, which consists of a massive O6.5V star in a 3.9-day-period, mildly elliptical ($e{\approx}0.24$) orbit with its companion, assumed here to be an unmagnetized compact object (e.g., black hole). Using three dimensional smoothed particle hydrodynamics simulations of the Bondi-Hoyle accretion of the O-star wind onto the companion, we find that the orbital phase variation of the accretion follows very closely the simple Bondi-Hoyle-Lyttleton (BHL) rate for the local radius and wind speed. Moreover, a simple model, wherein intrinsic emission of ${\gamma}$-rays is assumed to track this accretion rate, reproduces quite well Fermi observations of the phase variation of ${\gamma}$-rays in the energy range 0.1-10 GeV. However for the VHE (0.1-30 TeV) radiation observed by the HESS Cerenkov telescope, it is important to account also for photon-photon interactions between the ${\gamma}$-rays and the stellar optical/UV radiation, which effectively attenuates much of the strong emission near periastron. When this is included, we find that this simple BHL accretion model also quite naturally fits the HESS light curve, thus making it a strong alternative to the pulsar-wind-shock models commonly invoked to explain such VHE ${\gamma}$-ray emission in massive-star binaries.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
• /
• v.23 no.2
• /
• pp.109-124
• /
• 2010

Objectives : The aim of this study is to investigate the antiallergic effects of YTT on allergic rhinitis by regulation of peroxisome proliferator-activated receptor gamma (PPAR-$\gamma$). Methods : The thirty rats were divided into three groups : normal group, control (allergic rhinitis elicited) group, sample (Yeotaectonggi-tang treated after allergic rhinitis elicitation) group. To induce allergic rhinitis in control group and sample group, rats were sensitized intraperitoneally with 0.1% ovalbumin(OVA) solution 3 times at intervals of 1 week. Then intranasal sensitization was performed by diffusing 0.1% ovalbumin(OVA) solution 3 times at intervals of 2 days. After that time rats in sample group were oral administration treated by YTT for 7days. The change of the amounts of eosinophil, substance P, MIP-2, PPAR-$\gamma$, IL-4, iNOS were observed in each group. we used the statistical method of ANOVA test(p<0.05). Results : The number of eosinophil in sample group noticeably decreased than control group. And the decrease of substance P and MIP-2 positive reaction were observed in mucosa. YTT inhibited IL-4 and iNOS production, mucus secretion, activation of mast cells and fibrosis remodeling by regulation of PPAR-$\gamma$ activation. Conclusion : According to the above results, it is considered that YTT mitigated mucosa damage on the mice model with allergic rhinitis by regulation of PPAR-$\gamma$.

• Journal of Life Science
• /
• v.21 no.5
• /
• pp.621-626
• /
• 2011

AMP-activated protein kinase (AMPK), a heterotrimeric complex comprising a catalytic ${\alpha}$ subunit and regulatory ${\beta}$ and ${\gamma}$ subunits, has been primarily studied as a major metabolic regulator in various organisms, but recent genetic studies discover its novel physiological functions. The first animal model with no functional AMPK ${\gamma}$ subunit gene was generated by using Drosophila genetics. AMPK ${\gamma}$ flies demonstrated lethality with severe defects in cuticle formation. Further histological analysis found that deletion of AMPK ${\gamma}$ causes severe defects in cell polarity in embryo epithelia. The phosphorylation of nonmuscle myosin regulatory light chain (MRLC), a critical regulator of epithelial cell polarity, was also diminished in AMPK ${\gamma}$ embryo epithelia. These defects in AMPK ${\gamma}$ mutant epithelia were successfully restored by over-expression of AMPK ${\gamma}$. Collectively, these results suggested that AMPK ${\gamma}$ is a critical cell polarity regulator in metazoan development.

• Nuclear Engineering and Technology
• /
• v.53 no.12
• /
• pp.4072-4079
• /
• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.31 no.4
• /
• pp.31-49
• /
• 1989

It is of the most urgent necessity to get hydrological time series of long duration for the establishment of rational design and operation criterion for the Agricultural hydraulic structures. This study was conducted to select best fitted frequency distribution for the monthly runoff and to simulate long series of generated flows by multi-season first order Markov model with comparison of statistical parameters which are derivated from observed and sy- nthetic flows in the five watersheds along Geum river basin. The results summarized through this study are as follows. 1. Both two parameter gamma and two parameter lognormal distribution were judged to be as good fitted distributions for monthly discharge by Kolmogorov-Smirnov method for goodness of fit test in all watersheds. 2. Statistical parameters were obtained from synthetic flows simulated by two parameter gamma distribution were closer to the results from observed flows than those of two para- meter lognormal distribution in all watersheds. 3. In general, fluctuation for the coefficient of variation based on two parameter gamma distribution was shown as more good agreement with the observed flow than that of two parameter lognormal distribution. Especially, coefficient of variation based on two parameter lognormal distribution was quite closer to that of observed flow during June and August in all years. 4. Monthly synthetic flows based on two parameter gamma distribution are considered to give more reasonably good results than those of two parameter lognormal distribution in the multi-season first order Markov model in all watersheds. 5. Synthetic monthly flows with 100 years for eack watershed were sjmulated by multi- season first order Markov model based on two parameter gamma distribution which is ack- nowledged to fit the actual distribution of monthly discharges of watersheds. Simulated sy- nthetic monthly flows may be considered to be contributed to the long series of discharges as an input data for the development of water resources. 6. It is to be desired that generation technique of synthetic flow in this study would be compared with other simulation techniques for the objective time series.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.1
• /
• pp.265-271
• /
• 2014

Interleukin-7 (IL-7) is a potent anti-apoptotic cytokine that enhances immune effector cell functions and is essential for lymphocyte survival. While it known to induce differentiation and proliferation in some haematological malignancies, including certain types of leukaemias and lymphomas, little is known about its role in solid tumours, including breast cancer. In the current study, we investigated whether IL-7 could enhance the in vivo antitumor activity of tumor-reactive $CD8^+$ T cells with induction of IFN-${\gamma}$ in a murine breast cancer model. Human IL-7 cDNA was constructed into the eukaryotic expression plasmid pcDNA3.1, and then the recombinational pcDNA3.1-IL-7 was intratumorally injected in the TM40D BALB/C mouse graft model. Serum and intracellular IFN-${\gamma}$ levels were measured by ELISA and flow cytometry, respectively. $CD8^+$ T cell-mediated cytotoxicity was analyzed using the MTT method. Our results showed that IL-7 administration significantly inhibited tumor growth from day 15 after direct intratumoral injection of pcDNA3.1-IL-7. The anti-tumor effect correlated with a marked increase in the level of IFN-${\gamma}$ and breast cancer cells-specific CTL cytotoxicity. In vitro cytotoxicity assays showed that IL-7-treatment could augment cytolytic activity of $CD8^+$ T cells from tumor bearing mice, while anti-IFN-${\gamma}$ blocked the function of $CD8^+$ T cells, suggesting that IFN-${\gamma}$ mediated the cytolytic activity of $CD8^+$ T cells. Furthermore, in vivo neutralization of $CD8^+$ T lymphocytes by CD8 antibodies reversed the antitumor benefit of IL-7. Thus, we demonstrated that IL-7 exerts anti-tumor activity mainly through activating $CD8^+$ T cells and stimulating them to secrete IFN-${\gamma}$ in a murine breast tumor model. Based on these results, our study points to a potential novel way to treat breast cancer and may have important implications for clinical immunotherapy.