• Journal of Ginseng Research
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• v.42 no.3
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• pp.298-303
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• 2018

Background: Panax ginseng is one of the most commonly used medicinal herbs worldwide for a variety of therapeutic properties including neurocognitive effects. Ginsenoside Rg1 is one of the most abundant active chemical constituents of this herb with known neuroprotective, anxiolytic, and cognition improving effects. Methods: We investigated the effects of Rg1 on the medial prefrontal cortex (mPFC), a key brain region involved in cognition, information processing, working memory, and decision making. In this study, the effects of systemic administration of Rg1 (1 mg/kg, 3 mg/kg, or 10 mg/kg) on (1) spontaneous firing of the medial prefrontal cortical neurons and (2) long-term potentiation (LTP) in the hippocampal-medial prefrontal cortical (HP-mPFC) pathway were investigated in male Sprague-Dawley rats. Results: The spontaneous neuronal activity of approximately 50% the recorded pyramidal cells in the mPFC was suppressed by Rg1. In addition, Rg1 attenuated LTP in the HP-mPFC pathway. These effects were not dose-dependent. Conclusion: This report suggests that acute treatment of Rg1 impairs LTP in the HP-mPFC pathway, perhaps by suppressing the firing of a subset of mPFC neurons that may contribute to the neurocognitive effects of Rg1.

• Journal of the Korean Geophysical Society
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• v.5 no.1
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• pp.63-71
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• 2002

The integral equation method is a powerful tool for electromagnetic numerical modeling. But the difficulty of this technique is the size of their linear equations, which demands excessive memory and calculation time to invert. This limitation of the integral equation method becomes critical in inverse problem. To overcome this limitation, a lot of approximation and series methods, such as conventional Born, modifed Born and extended Born, were developed. But all the methods need volume integration of Green tensor, which is very time consuming. In electromagnetic theory, Green tensor rapidly decreases as the distance between source and field cell increases. Therefore, the source cell which are far away from the field cell does not make an effect on the electric field of the field cell. Consequently, by ignoring the effect of Green tensor due to far away source cells, computing time for electromagnetic numerical modeling can be reduced dramatically. Comparisons of this new method against a full integral equation, extended Born approximation and series code show that the method is accurate enough much less time consuming.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3406-3412
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• 2021

A real-time digital time-stamp sorting algorithm used in the In-Beam positron emission tomography (In-Beam PET) is presented. The algorithm is operated in the field programmable gate array (FPGA) and a small amount of registers, MUX and memory cells are used. It is developed for sorting the data of annihilation event from front-end circuits, so as to identify the coincidence events efficiently in a large amount of data. In the In-Beam PET, each annihilation event is detected by the detector array and digitized by the analog to digital converter (ADC) in Data Acquisition Unit (DAQU), with a resolution of 14 bits and sampling rate of 50 MS/s. Test and preliminary operation have been implemented, it can perform a sorting operation under the event count rate up to 1 MHz per channel, and support four channels in total, count rate up to 4 MHz. The performance of this algorithm has been verified by pulse generator and ²²Na radiation source, which can sort the events with chaotic order into chronological order completely. The application of this algorithm provides not only an efficient solution for selection of coincidence events, but also a design of electronic circuit with a small-scale structure.

• Journal of IKEEE
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• v.25 no.3
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• pp.473-478
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• 2021

Although CNTFETs have attracted great attention due to their ability to increase semiconductor device performance by about 13 times, the commercialization of CNTFETs has been challenging because of the immature deposition process of CNTs. To overcome these difficulties, circuit design method considering the known limitations of the CNTFET manufacturing process is receiving increasing attention. SRAM is a major element constituting microprocessor and is regularly and repeatedly positioned in the cache memory; so, it has the advantage that CNTs can be more easily and densely deposited in SRAM than other circuit blocks. In order to take these advantages, this paper presents a circuit design method for SRAM cells considering CNT density and then evaluates its performance improvement using HSPICE simulation. As a result of simulation, it is found that when CNTFET is applied to SRAM, the gate width can be reduced by about 1.7 times and the read speed also can be improved by about 2 times when the CNT density was increased in the same gate width.

• Journal of IKEEE
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• v.22 no.4
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• pp.1168-1174
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• 2018

For the initial state of NAND flash memories, error-free and single-error cases are dominant due to a good channel environment on memory cells. It is important to deal with such cases, which affects the overall system performance. However, the conventional schemes for polar codes equally decode the codes even for the error-free and single-error cases since they cannot classify and decode separately. In this paper, a new pre-processing scheme for polar codes is proposed so as to improve the overall decoding latency by decoding the frequent error-free and single-error cases. Before the ordinary decoding process, the proposed scheme first decodes the frequent error-free and single-error cases. According to the experimental results, the proposed pre-processing scheme decreases the average decoding latency by 64% compared to the conventional scheme for (1024, 512) polar codes.

• Journal of Microbiology and Biotechnology
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• v.31 no.1
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• pp.51-62
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• 2021

Here, we investigated the prebiotic and antioxidant effects of Actinidia arguta sprout water extract (AASWE) on lipopolysaccharide (LPS)-induced cognitive deficit mice. AASWE increased viable cell count, titratable acidity, and acetic acid production in Lactobacillus reuteri strain and showed a cytoprotective effect on LPS-induced inflammation in HT-29 cells. We assessed the behavior of LPS-induced cognitive deficit mice using Y-maze, passive avoidance and Morris water maze tests and found that administration of AASWE significantly improved learning and memory function. The AASWE group showed antioxidant activity through downregulation of malondialdehyde levels and upregulation of superoxide dismutase levels in brain tissue. In addition, the AASWE group exhibited activation of the cholinergic system with decreased acetylcholinesterase activity in brain tissue. Furthermore, AASWE effectively downregulated inflammatory mediators such as phosphorylated-JNK, phosphorylated-NF-κB, TNF-α and interleukin-6. The major bioactive compounds of AASWE were identified as quercetin-3-O-arabinopyranosyl(1→2)-rhamnopyranosyl(1→6)-glucopyranose, quercetin-3-O-apiosyl(1 → 2)-galactoside, rutin, and 3-caffeoylquinic acid. Based on these results, we suggest that AASWE not only increases the growth of beneficial bacteria in the intestines, but also shows an ameliorating effect on LPS-induced cognitive impairment.

• Korean Journal of Materials Research
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• v.31 no.3
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• pp.132-138
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• 2021

Electrochromic devices (ECDs) have been drawing great attention due to their high color contrast, low power consumption, and memory effect, and can be used in smart windows, automatic dimming mirrors, and information display devices. As with other electronic devices such as LEDs (light emitting diodes), solar cells, and transistors, the mechanical flexibility of ECDs is one of the most important issue for their potential applications. In this paper, we report on flexible ECDs (f-ECDs) fabricated using an all-in-one EC gel, which is a mixture of electrolyte and EC material. The f-ECDs are compared with rigid ECDs (r-ECDs) on ITO glass substrate in terms of color contrast, coloration efficiency, and switching speed. It is confirmed that the f-ECDs embedding all-in-one gel show strong blue absorption and have competitive EC performance. Repetitive bending tests show a degradation of electrochromic performance, which must be improved using an optimized device fabrication process.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• Biomolecules & Therapeutics
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• v.31 no.3
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• pp.285-297
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• 2023

Alzheimer's disease (AD) is a neurodegenerative disease with progressive memory loss and the cognitive decline. AD is mainly caused by abnormal accumulation of misfolded amyloid β (Aβ), which leads to neurodegeneration via a number of possible mechanisms such as down-regulation of brain-derived neurotrophic factor-tropomyosin-related kinase B (BDNF-TRKB) signaling pathway. 7,8-Dihydroxyflavone (7,8-DHF), a TRKB agonist, has demonstrated potential to enhance BDNF-TRKB pathway in various neurodegenerative diseases. To expand the capacity of flavones as TRKB agonists, two natural flavones quercetin and apigenin, were evaluated. With tryptophan fluorescence quenching assay, we illustrated the direct interaction between quercetin/apigenin and TRKB extracellular domain. Employing Aβ folding reporter SH-SY5Y cells, we showed that quercetin and apigenin reduced Aβ-aggregation, oxidative stress, caspase-1 and acetylcholinesterase activities, as well as improved the neurite outgrowth. Treatments with quercetin and apigenin increased TRKB Tyr516 and Tyr817 and downstream cAMP-response-element binding protein (CREB) Ser133 to activate transcription of BDNF and BCL2 apoptosis regulator (BCL2), as well as reduced the expression of pro-apoptotic BCL2 associated X protein (BAX). Knockdown of TRKB counteracted the improvement of neurite outgrowth by quercetin and apigenin. Our results demonstrate that quercetin and apigenin are to work likely as a direct agonist on TRKB for their neuroprotective action, strengthening the therapeutic potential of quercetin and apigenin in treating AD.

• Computers and Concrete
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• v.31 no.5
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• pp.405-417
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• 2023

The submerged floating tunnel (SFT) is tethered by mooring lines anchored to the seabed, therefore, the structural integrity of the anchor should be sensitively managed. Despite their importance, reaction forces cannot be simply measured by attaching sensors or load cells because of the structural and environmental characteristics of the submerged structure. Therefore, we propose an effective method for estimating the reaction forces at the seabed anchor of a submerged floating tunnel using a structural pattern model. First, a structural pattern model is established to use the correlation between tunnel motion and anchor reactions via a deep learning algorithm. Once the pattern model is established, it is directly used to estimate the reaction forces by inputting the tunnel motion data, which can be directly measured inside the tunnel. Because the sequential characteristics of responses in the time domain should be considered, the long short-term memory (LSTM) algorithm is mainly used to recognize structural behavioral patterns. Using hydrodynamics-based simulations, big data on the structural behavior of the SFT under various waves were generated, and the prepared datasets were used to validate the proposed method. The simulation-based validation results clearly show that the proposed method can precisely estimate time-series reactions using only acceleration data. In addition to real-time structural health monitoring, the proposed method can be useful for forensics when an unexpected accident or failure is related to the seabed anchors of the SFT.