• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.25-30
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• 2013

In this work, we investigate the statistical properties of gamma exposure rates using well-known analysis methods, such as Autocorrelation Function Analysis(ACF), Rescaled Range Analysis(R/S Analysis), and Detrended Fluctuation Analysis(DFA). Especially, DFA is an important method to reliably detect long-range correlations in non-stationary time series. Our data are measured by Gangneung regional radiation monitoring station over the period of 1998 to 2011. First, we find a crossover indicating two different governing regimes in fluctuations of gamma exposure rates. Within a year, they show a strong long-ranged memory while this property vanishes over the range of time period longer than one year. Second, our finding is very securely supported by a variety of analysis tools. Those tools yield many relevant exponents which satisfies the well known relation between them.

• Journal of Pharmacopuncture
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• v.22 no.4
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• pp.231-238
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• 2019

Objectives: Alzheimer's disease (AD), an overwhelming neurodegenerative disease, has deleterious effects on the brain that consequently causes memory loss and language impairment. This study was intended to investigate the neuroprotective activity of the two essential oils (EOs) from Iranian Pistacia khinjuk (PK) leaves and Allium sativum (AS) cloves against β-Amyloid 25-35 (Aβ25-35) induced elevation of cholinesterase enzymes in AD. Methods: The EOs of PK (PKEO) and AS (ASEO) were prepared and analyzed in terms of extraction yield, phenolic content, and cholinergic markers in vitro. Moreover, both were administered orally to adult male Wistar rats at concentrations of 1, 2, and 3%. The inhibitory potential of PKEO and ASEO was compared with Donepezil (0.75 mg/kg) against the high activities of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. Results: PKEO reached an inhibition rate of 83.6% and 81.4% against AChE and BChE, respectively. ASEO had lower anti-cholinesterase activity (65.4% and 31.5% for the inhibition AChE and BChE). PKEO was found to have more phenolic content than ASEO. A significantly positive correlation was observed between the total phenolics and anti-cholinesterase potential. In rats, both EOs decreased the enzyme activity in a concentration-dependent manner. As compared with Donepezil, the significant difference in the AChE and BChE inhibition occurred as rats were treated with PKEO 3% (p < 0.05). Conclusion: It could be concluded that PKEO and ASEO are potent inhibitors of AChE and BChE in rats that hold promise to be used for the treatment of AD.

• Korean Journal of Materials Research
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• v.27 no.11
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• pp.590-596
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• 2017

We demonstrated a CNT synaptic transistor by integrating 6,6-phenyl-C61 butyric acid methyl ester(PCBM) molecules as charge storage molecules in a polyimide(PI) dielectric layer with carbon nanotubes(CNTs) for the transistor channel. Specifically, we fabricated and compared three different kinds of CNT-based synaptic transistors: a control device with $Al_2O_3/PI$, a single PCBM device with $Al_2O_3/PI:PCBM$(0.1 wt%), and a double PCBM device with $Al_2O_3/PI:PCBM$(0.1 wt%)/PI:PCBM(0.05 wt%). Statistically, essential device parameters such as Off and On currents, On/Off ratio, device yield, and long-term retention stability for the three kinds of transistor devices were extracted and compared. Notably, the double PCBM device exhibited the most excellent memory transistor behavior. Pulse response properties with postsynaptic dynamic current were also evaluated. Among all of the testing devices, double PCBM device consumed such low power for stand-by and its peak current ratio was so large that the postsynaptic current was also reliably and repeatedly generated. Postsynaptic hole currents through the CNT channel can be generated by electrons trapped in the PCBM molecules and last for a relatively short time(~ hundreds of msec). Under one certain testing configuration, the electrons trapped in the PCBM can also be preserved in a nonvolatile manner for a long-term period. Its integrated platform with extremely low stand-by power should pave a promising road toward next-generation neuromorphic systems, which would emulate the brain power of 20 W.

• Structural Engineering and Mechanics
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• v.21 no.6
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• pp.713-735
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• 2005

Due to the complexity of mathematical expressions, the literature concerning the free vibration analysis of plates carrying multiple three-degree-of-freedom (dof) spring-mass systems is rare. In this paper, the three degrees of freedom (dof's) for a spring-mass system refer to the translational motion of its lumped mass in the vertical ($\bar{z}$) direction and the two pitching motions of its lumped mass about the two horizontal ($\bar{x}$ and $\bar{y}$) axes. The basic concept of this paper is to replace each three-dof spring-mass system by a set of equivalent springs, so that the free vibration characteristics of a rectangular plate carrying any number of three-dof spring-mass systems can be obtained from those of the same plate supported by the same number of sets of equivalent springs. Since the three dof's of the lumped mass for each three-dof spring-mass system are eliminated to yield a set of equivalent springs, the total dof of the entire vibrating system is not affected by the total number of the spring-mass systems attached to the rectangular plate. However, this is not true in the conventional finite element method (FEM), where the total dof of the entire vibrating system increases three if one more three-dof spring-mass system is attached to the rectangular plate. Hence, the computer storage memory required by using the presented equivalent spring method (ESM) is less than that required by the conventional FEM, and the more the total number of the three-dof spring-mass systems attached to the plate, the more the advantage of the ESM. In addition, since manufacturing a spring with the specified stiffness is much easier than making a three-dof spring-mass system with the specified spring constants and mass magnitude, the presented theory of replacing a three-dof spring-mass system by a set of equivalent springs will be also significant from this viewpoint.

• Journal of Marine Bioscience and Biotechnology
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• v.12 no.2
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• pp.81-90
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• 2020

Sialic acid, which is contained in about 60-160 mg/kg in the edible bird's nest (EBN), is known to facilitate in the proper formation of synapses and improve memory function. The objective of this study is to extract effectively the sialic acid from edible bird's nest using affinity bead technology (ABT). After preparing the non-polar polymeric bead "KJM-278-28A" having a porous network structure, and then desorbed sialic acid was concentrated and dried. The analysis of the physicochemical properties of bead "KJM-278-28A" showed that the particle size was 400-700 ㎛, the moisture holding capacity was 67-70%, the surface area (BET) was 705-900 ㎡/g, and the average pore diameter 70-87 Å. The adsorption capacity of the bead "KJM-278-28A" for sialic acid was shown a strong physical force to bind sialic acid to the bead surface of 400 mg/L. In addition, as a result of analyzing the adsorption and desorption effects of sialic acid on water, ethanol, and 10% ethanol on the bead, it was confirmed that desorption effectively occurs from the beads when only ethanol is used. As a result of HPLC measurement of the separated sialic acid solution, a total of four sialic acid peaks of N-acetyl-neuraminic acid (Neu5Ac), α,β-anomer of Neu5Ac and N-glycoly-neuraminic acid were identified. Through these results, it was confirmed that it is possible to separate sialic acid from EBN extract with efficient and high yield when using ABT.

• Journal of Periodontal and Implant Science
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• v.52 no.6
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• pp.437-454
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• 2022

Embryonic stem cells have been a popular research topic in regenerative medicine owing to their pluripotency and applicability. However, due to the difficulty in harvesting them and their low yield efficiency, advanced cell reprogramming technology has been introduced as an alternative. Dental stem cells have entered the spotlight due to their regenerative potential and their ability to be obtained from biological waste generated after dental treatment. Cell reprogramming, a process of reverting mature somatic cells into stem cells, and transdifferentiation, a direct conversion between different cell types without induction of a pluripotent state, have helped overcome the shortcomings of stem cells and raised interest in their regenerative potential. Furthermore, the potential of these cells to return to their original cell types due to their epigenetic memory has reinforced the need to control the epigenetic background for successful management of cellular differentiation. Herein, we discuss all available sources of dental stem cells, the procedures used to obtain these cells, and their ability to differentiate into the desired cells. We also introduce the concepts of cell reprogramming and transdifferentiation in terms of genetics and epigenetics, including DNA methylation, histone modification, and non-coding RNA. Finally, we discuss a novel therapeutic avenue for using dental-derived cells as stem cells, and explain cell reprogramming and transdifferentiation, which are used in regenerative medicine and tissue engineering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• The Journal of Internal Korean Medicine
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• v.35 no.1
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• pp.37-49
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• 2014

Objectives & Methods : The objective of this study was to evaluate the single oral dose toxicity of Chongmyung-tang (CMT) in ICR mice. Korean traditional herbal prescription CMT has traditionally been used as a neuroprotective for treatment of learning disability and memory improvement. CMT, lyophilized aqueous extracts (yield=9.7%) were administered to female and male mice with oral dose of 2,000, 1,000 and 500 mg/kg (body weight) according to the recommendation of Korea Food and Drug Administration (KFDA) Guidelines. Animals were monitored for mortality, changes in body weight, clinical signs and gross observation during 14 days after administration upon necropsy; organ weight and histopathology of 14 principle organs were examined. Results : We could not find any CMT extracts treatment related mortalities, clinical signs, changes in body and organ weight, or gross and histopathological observations against 14 principle organs up to 2,000 mg/kg in both female and male mice, except for some accidental sporadic findings which did not show any obvious dose-relations and most of which also demonstrated in both the female and male vehicle control mice in this experiments. Conclusions : Based on the results of this experiment, the 50% lethal dose ($LD_{50}$) and approximate lethal dose (ALD) of CMT extracts after single oral treatment in female and male mice can be considered to be over 2,000 mg/kg, and is likely to be safe in humans.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.