• Corrosion Science and Technology
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• v.18 no.5
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• pp.168-174
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• 2019

The sol-gel methodology has been applied successfully in the synthesis of a novel hybrid coating based on dimethoxymethyl-n-octadecylsilane precursor. The newly synthesized parent coating was functionalized further with two commercially-available corrosion-inhibitive pigments Moly-$white^{(R)}$ 101-ED and Hfucophos $Zapp^{(R)}$, applied to mild steel panels, and immersed continuously in 3.5% NaCl electrolytic solution for 288 h. The corrosion protection performance of the prepared functional coatings was evaluated using electrochemical impedance spectroscopy (EIS) and DC polarization techniques. An enhancement in the barrier properties has been revealed from the electrochemical characterization data of the hybrid films, in comparison with untreated mild steel substrates following long-term immersion in 3.5% NaCl. The corrosion resistance properties of the newly developed coatings over mild steel substrates found to be largely dependent on the type of the loaded inhibitive pigment in which the Moly-white inhibitor has a positive impact on the corrosion protection performance of the parent coating, while an opposite behavior was observed upon mixing the base polymeric matrix with the commercially-available Zapp corrosion inhibitor.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.04a
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• pp.465-465
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• 2011

Rolls-Royce is a global company producing advanced power systems for use on land, at sea and in the air. In order to develop competitive products and services, Rolls-Royce invests in technology, infrastructure and capability with much of the research carried out in a global network of University Technology Centres, such as the UTC in Thermal management at Pusan National University. Heat exchangers and thermal management play a critical role in today's gas turbine engines, maintaining the fuel and oil temperatures within the correct operational range. Future products are likely to place an increased duty on the thermal management system and thus require advances in heat exchanger design, installation and manufacturing. Heat exchangers further have the potential to play a vital role in Advanced Cycle Gas Turbine products. The Intercooled and recuperated WR21 marine gas turbine engine recently entered service with the Royal Navy and is delivering very attractive fuel burn in service. The development of an advanced cycle aero-engine is a significantly greater challenge, requiring better understanding of compact and light weight heat exchanger surfaces, novel installations and ducting systems and may required novel manufacturing techniques to achieve the volume, weight and cost necessary to realise a viable advanced cycle gas turbine aero-engine.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.71-73
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• 2019

The output current of the Grid Connected Inverter (GCI) can be polluted with harmonics mainly due to i) dead time in switches, ii) non-linearity of switches, iii) grid harmonics, and iv) DC link fluctuation. Therefore, it is essential to design the robust Harmonic Compensation (HC) technique for the improvement of output current quality and fulfill the IEEE 1547 Total harmonics Distortion (THD) limit i.e. <5%. The conventional harmonic techniques often are complex in implementation due to their i) additional hardware needs, ii) complex structure, iii) difficulty in tuning of parameters, iv) current controller compatibility issues, and v) higher computational burden. In this paper, to eliminate the harmonics from the GCI output current, a novel Digital Lock-In Amplifier (DLA) based harmonic detection is proposed. The advantage of DLA is that it extracts the harmonic information accurately, which is further compensated by means of PI controller in feed forward manner. Moreover, the proposed HC method does not require additional hardware and it works with any current controller reference frame. To show the effectiveness of the proposed HC method a 5kW GCI prototype built in laboratory. The output current THD is achieved less than 5% even with 10% load, which is verified by simulation and experiment.

• BMB Reports
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• v.52 no.3
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• pp.163-164
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• 2019

The ribosomal synthesis of proteins in the eukaryotic cytosol has always been thought to start from the unformylated N-terminal (Nt) methionine (Met). In contrast, in virtually all nascent proteins in bacteria and eukaryotic organelles, such as mitochondria and chloroplasts, Nt-formyl-methionine (fMet) is the first building block of ribosomal synthesis. Through extensive approaches, including mass spectrometric analyses of the N-termini of proteins and molecular genetic techniques with an affinity-purified antibody for Nt-formylation, we investigated whether Nt-formylated proteins could also be produced and have their own metabolic fate in the cytosol of a eukaryote, such as yeast Saccharomyces cerevisiae. We discovered that Nt-formylated proteins could be generated in the cytosol by yeast mitochondrial formyltransferase (Fmt1). These Nt-formylated proteins were massively upregulated in the stationary phase or upon starvation for specific amino acids and were crucial for the adaptation to specific stresses. The stress-activated kinase Gcn2 was strictly required for the upregulation of Nt-formylated proteins by regulating the activity of Fmt1 and its retention in the cytosol. We also found that the Nt-fMet residues of Nt-formylated proteins could be distinct N-terminal degradation signals, termed fMet/N-degrons, and that Psh1 E3 ubiquitin ligase mediated the selective destruction of Nt-formylated proteins as the recognition component of a novel eukaryotic fMet/N-end rule pathway, termed fMet/N-recognin.

• Journal of Microbiology and Biotechnology
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• v.29 no.5
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• pp.713-720
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• 2019

Acanthamoeba castellanii belonging to the T4 genotype may cause a fatal brain infection known as granulomatous amoebic encephalitis, and the vision-threatening eye infection Acanthamoeba keratitis. The aim of this study was to evaluate the antiamoebic effects of three clinically available antidiabetic drugs, Glimepiride, Vildagliptin and Repaglinide, against A. castellanii belonging to the T4 genotype. Furthermore, we attempted to conjugate these drugs with silver nanoparticles (AgNPs) to enhance their antiamoebic effects. Amoebicidal, encystation, excystation, and host cell cytotoxicity assays were performed to unravel any antiacanthamoebic effects. Vildagliptin conjugated silver nanoparticles (Vgt-AgNPs) characterized by spectroscopic techniques and atomic force microscopy were synthesized. All three drugs showed antiamoebic effects against A. castellanii and significantly blocked the encystation. These drugs also showed significant cysticidal effects and reduced host cell cytotoxicity caused by A. castellanii. Moreover, Vildagliptin-coated silver nanoparticles were successfully synthesized and are shown to enhance its antiacanthamoebic potency at significantly reduced concentration. The repurposed application of the tested antidiabetic drugs and their nanoparticles against free-living amoeba such as Acanthamoeba castellanii described here is a novel outcome that holds tremendous potential for future applications against devastating infection.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• Journal of Microbiology and Biotechnology
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• v.32 no.8
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• pp.1026-1033
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• 2022

This study presents a novel DNA part characterization technique that increases throughput by combinatorial DNA part assembly, solid plate-based quantitative fluorescence assay for phenotyping, and barcode tagging-based long-read sequencing for genotyping. We confirmed that the fluorescence intensities of colonies on plates were comparable to fluorescence at the single-cell level from a high-end, flow-cytometry device and developed a high-throughput image analysis pipeline. The barcode tagging-based long-read sequencing technique enabled rapid identification of all DNA parts and their combinations with a single sequencing experiment. Using our techniques, forty-four DNA parts (21 promoters and 23 RBSs) were successfully characterized in 72 h without any automated equipment. We anticipate that this high-throughput and easy-to-use part characterization technique will contribute to increasing part diversity and be useful for building genetic circuits and metabolic pathways in synthetic biology.

• Smart Structures and Systems
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• v.29 no.3
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• pp.513-522
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• 2022

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.71.1-71.1
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• 2021

We perform disk-halo decomposition of the Large Magellanic Cloud (LMC) using a novel HI velocity field extraction method, aimed at better deriving its HI kinematics and thus mass distribution in the galaxy including both baryons and dark matter. We decompose all the line-of-sight velocity profiles of the combined HI data cube of the LMC, taken from the Australia Telescope Compact Array (ATCA) and Parkes radio telescopes with an optimal number of Gaussian components. For this, we use a novel tool, the so-called BAYGAUD which performs profile decomposition based on Bayesian MCMC techniques. From this, we disentangle turbulent non-ordered HI gas motions from the decomposed gas components, and produce an HI bulk velocity field which better follows the global circular rotation of the galaxy. From a 2D tilted-ring analysis of the HI bulk velocity field, we derive the rotation curve of the LMC after correcting for its transverse, nutation and precession motions. The dynamical contributions of baryons like stars and gaseous components which are derived using the Spitzer 3.6 micron image and the HI data are then subtracted from the total kinematics of the LMC. Here, we present the bulk HI rotation curve, the mass models of stars and gaseous components, and the resulting dark matter density profile of the LMC.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.