• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.3
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• pp.93-100
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• 2002

In this paper, in ordor to examine the relevant technical facts which are very instructive to revise the domestic standard for lightning protection systems, standards and technical guideline for the protection of structure against lightning were reviewed, and several issues of the domestic standards were experimentally investigated. As a consequence, the insulator of relatively low implse voltage and a large percentage of lighting rods is flashovered by relatively low impulse voltage and a lage percentage of lighting current flows through supporting mast. Thus the potential gradient in the vicinity of supporter for lighting rods is extremely increased and the role of lighting propection systems is nullified. It seems obvious that the flashover of insular supporting lighting rod can range from erratic operation of microelectronic devices to minor physical ham or even death, or costly damage electrical equipment.

• Journal of Soil and Groundwater Environment
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• v.8 no.1
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• pp.9-16
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• 2003

A new method has been proposed and developed that solves the problem of decreasing electroosmotic flow rate by excess $H^{+}$ and precipitation of heavy metal by $OH^{-}$. An electrolytic solution was circulated between the anode and cathode compartments that enabled the pH at the anode and cathode to be controlled. The change of the soil pH by circulation systems affects the operation time, by lowering the rate of increase of the electric potential gradient, and the removal efficiency of heavy metals, by affecting the soil pH. Since there was no effluent from the cathode compartment in circulation system, there was no need to treat the wastewater after the experiment, which resulted in the reduction of influent electrolyte volume.

• Steel and Composite Structures
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• v.33 no.4
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• pp.509-523
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• 2019

In the present work, the buckling analysis of micro sandwich plate with an isotropic/orthotropic cores and piezoelectric/polymeric nanocomposite face sheets is studied. In this research, two cases for core of micro sandwich plate is considered that involve five isotropic Devineycell materials (H30, H45, H60, H100 and H200) and an orthotropic material also two cases for facesheets of micro sandwich plate is illustrated that include piezoelectric layers reinforced by carbon and boron-nitride nanotubes and polymeric matrix reinforced by carbon nanotubes under temperature-dependent and hydro material properties on the elastic foundations. The first order shear deformation theory (FSDT) is adopted to model micro sandwich plate and to apply size dependent effects from modified strain gradient theory. The governing equations are derived using the minimum total potential energy principle and then solved by analytical method. Also, the effects of different parameters such as size dependent, side ratio, volume fraction, various material properties for cores and facesheets and temperature and humidity changes on the dimensionless critical buckling load are investigated. It is shown from the results that the dimensionless critical buckling load for boron nitride nanotube is lower than that of for carbon nanotube. It is illustrated that the dimensionless critical buckling load for Devineycell H200 is highest and lowest for H30. Also, the obtained results for micro sandwich plate with piezoelectric facesheets reinforced by carbon nanotubes (case b) is higher than other states (cases a and c).The results of this research can be used in aircraft, automotive, shipbuilding industries and biomedicine.

• Ocean Systems Engineering
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• v.8 no.4
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• pp.381-407
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• 2018

This paper presents a hybrid numerical approach, which combines a two-phase Navier-Stokes model (NS) and the fully nonlinear potential theory (FNPT), for modelling wave-structure interaction. The former governs the computational domain near the structure, where the viscous and turbulent effects are significant, and is solved by OpenFOAM/InterDyMFoam which utilising the finite volume method (FVM) with a Volume of Fluid (VOF) for the phase identification. The latter covers the rest of the domain, where the fluid may be considered as incompressible, inviscid and irrotational, and solved by using the Quasi Arbitrary Lagrangian-Eulerian finite element method (QALE-FEM). These two models are weakly coupled using a zonal (spatially hierarchical) approach. Considering the inconsistence of the solutions at the boundaries between two different sub-domains governed by two fundamentally different models, a relaxation (transitional) zone is introduced, where the velocity, pressure and surface elevations are taken as the weighted summation of the solutions by two models. In order to tackle the challenges associated and maximise the computational efficiency, further developments of the QALE-FEM have been made. These include the derivation of an arbitrary Lagrangian-Eulerian FNPT and application of a robust gradient calculation scheme for estimating the velocity. The present hybrid model is applied to the numerical simulation of a fixed horizontal cylinder subjected to a unidirectional wave with or without following current. The convergence property, the optimisation of the relaxation zone, the accuracy and the computational efficiency are discussed. Although the idea of the weakly coupling using the zonal approach is not new, the present hybrid model is the first one to couple the QALE-FEM with OpenFOAM solver and/or to be applied to numerical simulate the wave-structure interaction with presence of current.

• Advances in nano research
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• v.7 no.4
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• pp.233-240
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• 2019

The present research work reports in-vitro anti-cancer activity of biologically synthesized ZnO nanoparticles (ZnO NPs) against human carcinoma cells viz SCC-40, SK-MEL-2 and SCC-29B using Sulforhodamine-B (SRB) Assay. ZnO NPs were synthesized by a unique and novel biological route using Temperature-gradient phenomenon where the extract of combination of Catharanthus roseus (L.) G. Don (C. roseus), Azadirachta indica (A. indica), Ficus religiosa (F. religiosa) and NaOH solution were used as synthesis medium. The morphology of the ZnO NPs was characterized by Transmission Electron Microscopy (TEM). TEM images reveal that particle size of the samples reduces from 76 nm to 53 nm with the increase in reaction temperature and 68 nm to 38 nm with the increase in molar concentration of NaOH respectively. XRD study confirms the presence of elements and reduction in crystallite size with increase in reaction temperature and NaOH concentration. The diffraction peaks show broadening and a slight shift towards lower Bragg angle ($2{\theta}$) which represents the reduction in crystallite size as well as presence of uniform strain. The FTIR spectra of the extract show transmittance peak fingerprint of Zn-O bond and presence of bioactive molecules These NPs exhibit inhibition greater than 50% for SCC-40, SK-MEL-2 and SCC-29B cell lines and more than 50% cell kill for SCC-29B cells at concentrations < $80{\mu}g/ml$. Nanoparticles with smallest size have shown better anti-cancer activity and peculiar cell-selectivity. The combination of extracts of these plants with ZnO NPs can be used in targeted drug delivery as an effective anti-cancer agent, a potential application in cancer treatment.

• Journal of Ecology and Environment
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• v.45 no.1
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• pp.1-9
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• 2021

Background: Abandoned coal piles after the closure of mines have a potential negative influence on the environment, such as soil acidification and heavy metal contamination. Therefore, revegetation by efficient species is required. For this, we wanted to identify the role of Silene acaulis in the succession of coal piles as a pioneer and a nurse plant. S. acaulis is a well-studied cushion plant living in the Arctic and alpine environments in the northern hemisphere. It has a highly compact cushion-like form and hosts more plant species under its canopy by ameliorating stressful microhabitats. In this research, we surveyed vegetation cover on open plots and co-occurring species within S. acaulis cushions in coal piles with different slope aspects and a control site where no coal was found. The plant cover and the similarity of communities among sites were compared. Also, the interaction effects of S. acaulis were assessed by rarefaction curves. Results: S. acaulis was a dominant species with the highest cover (6.7%) on the coal piles and occurred with other well-known pioneer species. Plant communities on the coal piles were significantly different from the control site. We found that the pioneer species S. acaulis showed facilitation, neutral, and competition effect in the north-east facing slope, the south-east facing slope, and the flat ground, respectively. This result was consistent with the stress gradient hypothesis because the facilitation only occurred on the north-east facing slope, which was the most stressed condition, although all the interactions observed were not statistically significant. Conclusions: S. acaulis was a dominant pioneer plant in the succession of coal piles. The interaction effect of S. acaulis on other species depended on the slope and its direction on the coal piles. Overall, it plays an important role in the succession of coal piles in the High Arctic, Svalbard.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.119-134
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• 2023

The synoptic, thermodynamic, and dynamic characteristics of a snowfall event that occurred in the Yeongdong region on March 1-2, 2021, were investigated. Surface weather charts, ERA5 reanalysis data, rawinsonde data, GK-2A satellite data, and WISSDOM data were used for analysis. The snow depth, exceeding 10 cm, was observed at four weather stations during the analysis period. The maximum snow depth (37.4 cm) occurred at Bukgangneung. According to the analysis of the weather charts, old and dry air was trapped within relatively warm, humid air in the upper atmosphere over the East Sea and adjacent Yeongdong region. This caused unstable atmospheric conditions that led to developing convective clouds and snowfall over Bukgangneung. In particular, based on the thermodynamic and kinematic vertical analysis, we suggest that strong winds attributable to the vertical gradient of potential temperature in the low layer and the development of convective instability due to cold advection played a significant role in the occurrence of snowfall in the Yeongdong region. These results were confirmed from the vertical analysis of the rawinsonde data.

• Ocean and Polar Research
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• v.45 no.2
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• pp.57-69
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• 2023

Growth responses along the gradient of water temperature, salinity, and light intensity and cytotoxicity against Artemia nauplii were explored using Ostreopsis sp. and Coolia canariensis strains, representing the two potentially toxic epiphytic dinoflagellate (EPD) species from Jeju coastal waters of Korea. Variation in maximum growth rate (GRmax) and maximum biomass yield (Ymax) along the environmental gradients was quite contrasting between the two strains, which appears to be reflected in the in situ abundance distribution of the corresponding genera. The more eurythermal characteristics of Ostreopsis sp. strain were in good agreement with the relative distribution of Ostreopsis spp. and Coolia spp. in 520 macroalgal samples collected from 6 stations. The more stenohaline C. canariensis strain was well matched by a markedly narrower range of salinities in the in situ distribution of Coolia spp. than the salinity range for Ostreopsis species. The differences in light adaptation between the high light-preferring Ostreopsis sp. strain and the more euryphotic C. canariensis strain were remarkably consistent with the distinct vertical profiles of Ostreopsis spp. and Coolia spp. abundance in the red alga Amphiroa sp. off Moom-seom. Cytotoxicity against Artemia nauplii in the Ostreopsis sp. preparation with 1000 cells ml^-1 was similar to that in C. canariensis preparation with 12000 cells ml^-1, which is noteworthy. Thus, the new potential cytotoxicity risks from C. canariensis along with the well-known toxic genus Ostreopsis may be introduced to Jeju coasts, which necessitates further exploration into the contrasting ecological niches occupied by EPD species in relation to their cytotoxicity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.