• Advances in nano research
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• v.14 no.1
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• pp.1-15
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• 2023

Driven by the scaling down of transistor node technology, graphene became of interest to many researchers following the success of its fabrication as graphene nanoribbons (GNRs). However, during the fabrication of GNRs, it is not uncommon to have defects within the GNR structures. Scaling down node technology also changes the modelling approach from the classical Boltzmann transport equation to the quantum transport theory because the quantum confinement effects become significant at sub-10 nanometer dimensions. The aim of this study is to examine the effect of Stone-Wales defects on the electronic properties of GNRs using a tight-binding model, based on Non-Equilibrium Green's Function (NEGF) via numeric computation methods using MATLAB. Armchair and zigzag edge defects are also implemented in the GNR structures to mimic the practical fabrication process. Electronic properties of pristine and defected GNRs of various lengths and widths were computed, including their band structure and density of states (DOS). The results show that Stone-Wales defects cause fluctuation in the band structure and increase the bandgap values for both armchair GNRs (AGNRs) and zigzag GNRs (ZGNRs) at every simulated width. In addition, Stone-Wales defects reduce the numerical computation DOS for both AGNRs and ZGNRs. However, when the lengths of the structures increase with fixed widths, the effect of the Stone-Wales defects become less significant.

• Geomechanics and Engineering
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• v.32 no.6
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.131-139
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• 2009

Recently frequent occurrences of heavy rainfall and increases of rainfall intensity resulted in severe flood damage in Korea. In order to mitigate the vulnerability of flood, it is necessary to estimate proper design rainfalls considering the increasing trend of extreme rainfalls for hydrologic planning and design. This study focused the estimation of design rainfalls in a design target year. Tests of trend indicated that there are 7 sites showing increasing trends among 56 sites which have hourly data more than 30 years in Korea. This study analyzed the relationship between mean of annual maximum rainfalls and parameters of the Gumbel distribution. Based on the relationship, this study estimated the probability density function and design rainfalls in a design target year, and then constructed the rainfall-frequency curve. The proposed method estimated the design rainfalls 6-20% higher than those from the stationary rainfall frequency analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.113-118
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• 2010

This paper presents an analysis of the instability phenomenon on motorways, with the aim of arriving at the definition of a control strategy suitable for keeping the flow stable. By using some results of the motorway reliability theory, a relationship and some flow characteristics is obtained, which shows that the existence of a reliability threshold critical for flow stability. The macroscopic flow characteristics corresponding to this threshold are very different in different situations, so that this control of flow stability requires the analysis of speed and density microscopic process surveyed on a cross section of the motorway carriage ways to be controlled. A method is presented, based on integrated moving average(IMA) analysis in real time of these processes, by which it is possible to detect the approach of instability before its effects become manifest, and to single out the proper control strategy in different situations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4B
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• pp.319-328
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• 2009

This study quantified the data by applying the EDA techniques considering the data structure, and the results were then used for the frequency analysis. Although traditional methods based on the method of moments provide very sensitive statistics to the extreme values, the EDA techniques have an advantage of providing very stable statistics with their small variation. For the application of the EDA techniques to the frequency analysis, it is necessary to normalization transform and inverse-transform to conserve the skewness of the raw data. That is, it is necessary to transform the raw data to make the data follow the normal distribution, to estimate the statistics by applying the EDA techniques, and then finally to inverse-transform the statistics of transformed data. These statistics decided are then applied for the frequency analysis with a given probability density function. This study analyzed the annual maxima one hour rainfall data at Seoul and Pohang stations. As a result, it was found that more stable rainfall quantiles, which were also less sensitive to extreme values, could be estimated by applying the EDA techniques. This methodology may be effectively used for the frequency analysis of rainfall at stations with especially high annual variations of rainfall due to climate change, etc.

• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• Korean Journal of Materials Research
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• v.33 no.10
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• pp.377-384
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• 2023

Exploring earth-abundant, highly effective and stable electrocatalysts for electrochemical water splitting is urgent and essential to the development of hydrogen (H₂) energy technology. Iron-cobalt layered double hydroxide (FeCo-LDH) has been widely used as an electrocatalystfor OER due to its facile synthesis, tunable components, and low cost. However, LDH synthesized by the traditional hydrothermal method tends to easily agglomerate, resulting in an unstable structure that can change or dissolve in an alkaline solution. Therefore, studying the real active phase is highly significant in the design of electrochemical electrode materials. Here, metal-organic frameworks (MOFs) are used as template precursors to derive FeCo-LDH from different iron sources. Iron salts with different anions have a significant impact on the morphology and charge transfer properties of the resulting materials. FeCo-LDH synthesized from iron sulfate solution (FeCo-LDH-SO₄) exhibits a hybrid structure of nanosheets and nanowires, quite different from other electrocatalysts that were synthesized from iron chloride and iron nitrate solutions. The final FeCo-LDH-SO₄ had an overpotential of 247 mV with a low Tafel-slope of 60.6 mV dec^-1 at a current density of 10 mA cm^-2 and delivered a long-term stability of 40 h for the OER. This work provides an innovative and feasible strategy to construct efficient electrocatalysts.

• Journal of The Korean Astronomical Society
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• v.56 no.1
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• pp.97-115
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• 2023

We present the deep homogeneous UBV RI photometric data of 135,071 stars down to V ~ 23 mag and I ~ 22 mag toward the Carina Nebula. These stars are cross-matched with those from the previous surveys in the X-ray, near-infrared, and mid-infrared wavelengths as well as the Gaia Early Data Release 3 (EDR3). This master catalog allows us to select reliable members and determine the fundamental parameters distance, size, stellar density of stellar clusters in this star-forming region. We revisit the reddening toward the nebula using the optical and the near-infrared colors of early-type stars. The foreground reddening [E(B-V)_fg] is determined to be 0.35 ± 0.02, and it seems to follow the standard reddening law. On the other hand, the total-to-selective extinction ratio of the intracluster medium (R_V,cl) decreases from the central region (Trumpler 14 and 16, R_V,cl ~ 4.5) to the northern region (Trumpler 15, R_V,cl ~ 3.4). It implies that the central region is more dusty than the northern region. We find that the distance modulus of the Carina Nebula to be 11.9 ± 0.3 mag (d = 2.4 ± 0.35 kpc) using a zero-age main-sequence fitting method, which is in good agreement with that derived from the Gaia EDR3 parallaxes. We also present the catalog of 3,331 pre-main-sequence (PMS) members and 14,974 PMS candidates down to V ~ 22 mag based on spectrophotometric properties of young stars at infrared, optical, and X-ray wavelengths. From the spatial distribution of PMS members and PMS candidates, we confirm that the member selection is very reliable down to faint stars. Our data will have a legacy value for follow-up studies with different scientific purposes.

• Composites Research
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• v.37 no.1
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• pp.32-39
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• 2024

Solid-state hydrogen storage is gaining prominence as a crucial subject in advancing the hydrogen-based economy and innovating energy storage technology. This storage method shows superior characteristics in terms of safety, storage, and operational efficiency compared to existing methods such as compression and liquefied hydrogen storage. In this study, we aim to evaluate the solid hydrogen storage performance on the nanotube surface by various structural design factors. This is accomplished through molecular dynamics simulations (MD) with the aim of uncovering the underlying ism. The simulation incorporates diverse carbon nanotubes (CNTs) - encompassing various diameters, multi-walled structures (MWNT), single-walled structures (SWNT), and boron-nitrogen nanotubes (BNNT). Analyzing the storage and effective release of hydrogen under different conditions via the radial density function (RDF) revealed that a reduction in radius and the implementation of a double-wall configuration contribute to heightened solid hydrogen storage. While the hydrogen storage capacity of boron-nitrogen nanotubes falls short of that of carbon nanotubes, they notably surpass carbon nanotubes in terms of effective hydrogen storage capacity.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.