• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.57-73
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• 2021

The purpose of this study is to analyze the diffusion period and productivity of smartwork in an organization. Firms are increasingly interested in smartwork for non contact work and working from home because of the corona 19. The smartwork is a new technology that changes face-to-face work in an organization. It helps the work of individuals and organizations regardless of time and place. The theoretical background describes the complexity, system thinking, diffusion theory, smart work, organizational resistance, and productivity. This study analyzes the diffusion period and productivity of smart work through business simulation techniques. A simulation study progresses four stages. There are problem definition, hypothesis establishment and causal loop diagram, model construction and verification, and policy evaluation. The simulation models contain an individual's resistance variables organizational investment and leadership variables related to the operation of smartwork. The organizational investment variables include organizational culture, legal system, implement systems and technology investment. The individual resistance variables include cognitive, attitude, structure and technological resistance. The leadership includes leadership interest variables and performance linkage variables. The simulation executed the changes of a people number adopting smart work and the organizational productivity monthly. As a result of the simulation, many organization members have accepted the smart work innovation after 20 months. The organizational productivity through smart work showed very high value after 16 months. In scenario analysis, the individuals' awareness and attitude resistance showed very important variables to productivity and a personal change of smart work adoption. Meanwhile, The organizational investment showed that the high driving-force increased not productivity and the low driving-force showed decreased low productivity. Also, leadership variables showed a powerful driver for changing smart work productivity. The implication of the study has suggested extending complexity, diffusion theory and organization resistance theory based on simulation methods.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2135-2146
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• 2022

A nuclear energy facility is one of the most critical facilities to be safely protected during and after operation because the physical destruction of its barriers by an external attack could release radioactivity into the environment and can cause harmful effects. The barrier walls of nuclear energy facilities should be sufficiently robust to protect essential facilities from external attack or sabotage. Physical protection system (PPS) vulnerability assessment of a typical small nuclear research reactor was carried out by simulating an external attack with a tri-nitro toluene (TNT) shaped charge and results are presented. The reinforced concrete (RC) barrier wall of the research reactor located at a distance of 50 m from a TNT-shaped charge was the target of external attack. For the purpose of the impact assessment of the RC barrier wall, a finite element method (FEM) is utilized to simulate the destruction condition. The study results showed that a hole-size of diameter 342 mm at the front side and 364 mm at the back side was created on the RC barrier wall as a result of a 143.35 kg TNT-shaped charge. This aperture would be large enough to let at least one person can pass through at a time. For the purpose of the PPS vulnerability assessment, an Estimate of Adversary Sequence Interruption (EASI) model was used, which enabled the determination of most vulnerable path to the target with a probability of interruption equal to 0.43. The study showed that the RC barrier wall is vulnerable to a TNT-shaped charge impact, which could in turn reduce the effectiveness of the PPS.

• Journal of Soil and Groundwater Environment
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• v.27 no.spc
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• pp.64-74
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• 2022

The conceptual site model (CSM) is used as a key tool to support decision making in risk based management of contaminated sites. In this work, CSM was applied in Jeonju Industrial Complex where site investigation for groundwater contamination was conducted. Site background information including facility types, physical conditions, contaminants spill history, receptor exposure, and ecological information were collected and cross-checked with tabulated checklist necessary for CSM application. The CSM for contaminants migration utilized DNAPL transport model and narrative CSMs were constructed for source to receptor pathway, ecological exposure route, and contaminants fate and transport in the form of a diagram or flowchart. The component and uncertainty of preliminary CSM were reviewed using the data gap analysis while taking into account the purpose of the survey and the site management stage at the time of the survey. Through this approach, the potential utility of CSM was demonstrated in the site management process, such as assessing site conditions and planning follow-up survey work.

• Genomics & Informatics
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• v.19 no.3
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• pp.31.1-31.9
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• 2021

Leptospirosis is a zoonotic disease caused by spirochetes from the genus Leptospira. In Thailand, Leptospira interrogans is a major cause of leptospirosis. Leptospirosis patients present with a wide range of clinical manifestations from asymptomatic, mild infections to severe illness involving organ failure. For better understanding the difference between Leptospira isolates causing mild and severe leptospirosis, illumina sequencing was used to sequence genomic DNA in both serotypes. DNA of Leptospira isolated from two patients, one with mild and another with severe symptoms, were included in this study. The paired-end reads were removed adapters and trimmed with Q30 score using Trimmomatic. Trimmed reads were constructed to contigs and scaffolds using SPAdes. Cross-contamination of scaffolds was evaluated by ContEst16s. Prokka tool for bacterial annotation was used to annotate sequences from both Leptospira isolates. Predicted amino acid sequences from Prokka were searched in EggNOG and David gene ontology database to characterize gene ontology. In addition, Leptospira from mild and severe patients, that passed the criteria e-value < 10e^-5 from blastP against virulence factor database, were used to analyze with Venn diagram. From this study, we found 13 and 12 genes that were unique in the isolates from mild and severe patients, respectively. The 12 genes in the severe isolate might be virulence factor genes that affect disease severity. However, these genes should be validated in further study.

• 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.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.259-275
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• 2023

Pan African granitoids of Kaiama is comprised of K-feldspar rich granites, porphyritic granites, and granitic gneiss that are intruded by quartz veins and aplitic veins and dykes which trend NE-SW. In order to establish the geochemical signatures, petrogenesis, and tectonic settings of the lithological units, petrological, petrographical, and geochemical studies was carried out. Petrographic analysis reveals that the granitoids are dominantly composed of quartz, plagioclase feldspar, biotite, and k-feldspar with occasional muscovites, sericite, and opaque minerals that constitute very low proportion. Major, trace, and rare earth elements geochemical data reveal that the rocks have moderate to high silica (SiO₂=63-79.7%) and alumina (Al₂O₃=11.85-16.15) contents that correlate with the abundance of quartz, feldspars, and biotite. The rocks are calc-alkaline, peraluminous (ASI=1.0-<1.2), and S-type granitoids sourced by melting of pre-existing metasedimentary or sedimentary rocks containing Al, Na, and K oxides. They plot dominantly in the WPG and VAG fields suggesting emplacement in a post-collisional tectonic setting. On a multi-element variation diagram, the granitoids show depletion in Ba, K, P, Rb, and Ti while enrichment was observed for Th, U, Nd, Pb and Sm. Their rare-earth elements pattern is characterized by moderate fractionation ((La/Yb)_N=0.52-38.24) and pronounced negative Eu-anomaly (Eu/Eu^*=0.02-1.22) that points to the preservation of plagioclase from the source magma. Generally, the geochemical features of the granitoids show that they were derived by the partial melting of crustal rocks with some input from greywacke and pelitic materials in a typical post-collisional tectonic setting.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.251-258
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• 2006

This study is to develop an estimation method of freeway design capacity through the analysis of time headway distribution in continuum flow. Traffic flow-speed diagram and time headway distribution plotted from individual vehicle data shows: a) a road capacity is not deterministic but stochastic, b) time headway distribution for each vehicle speed group follows pearson type V distribution. The freeway design capacity estimation model is developed by determining a minimum time headway for capacity with stochastic method. The estimated capacity values for each design speed are lower when design speed ${\leq}80km/h$, and higher when design speed ${\geq}106km/h$ in comparison with HCM(2000)'s values. In addition, The distinguish difference is that this model leads flexible application in planning level by defining the capacity as stochastic distribution. In detail, this model could prevent a disutility to add a lane for only one excess demand in a road planning level.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• Advances in materials Research
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• v.13 no.1
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.2
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• pp.105-116
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• 2000

In order to investigate the seasonal variation of watermass in the central coast of the southern sea of korea, oceanographic observation on the fishing grounds were carried out by the trainingship of Yosu University on May, Aug. and Nov. in 1998 and Feb. in 1999. The resultes obtained are summerized as follows : 1). The watermass in the fishing ground were divided into the coastal water(30.0~31.6$\textperthousand$ ), mixing water(31.7~33.4$\textperthousand$) and the offshore water(33.5~35.0$\textperthousand$) according to the distribution of salinity from T-S diagram plotted all salinity data observed on May, Aug. and Nov. in 1998 and Feb. in 1999. 2) The ranges of temperature and salinity were from 14.1$^{\circ}C$ to 18.8$^{\circ}C$ and from 32.2$\textperthousand$ to 34.9$\textperthousand$ in spring(May), from 14.2$^{\circ}C$ to 27.7$^{\circ}C$ and from 29.0$\textperthousand$ to 34.7$\textperthousand$ in summer(August), from 13.4$^{\circ}C$ to 21.3$^{\circ}C$ and from 31.45$\textperthousand$ to 34.5$\textperthousand$ in autumn(November) and from 8.2$^{\circ}C$ to 14.8$^{\circ}C$ and from 33.9$\textperthousand$ to 34.6$\textperthousand$ in winter(February), respectively. 3) The distribution of watermass in the fishing ground varied largely each seasons, but a general tendency on the distribution was obtained. That is, in spring and autumm the offshore water was distributed most widely and in summer the coastal and mixing water occupied the fishing ground but in winter the offshore water prevailed. 4) Variation of temperature and salinity were appeared between the surface and 30m in the coastal region and between the surface and 50m in the open ocaen region. Therefore, in the summer the thermocline and halocline were made between surface and 30m layer with vertical gradients of 10.5$^{\circ}C$/30m and 4.0$\textperthousand$/30m in the coastal region and in the open ocean region the thermocline and halocline were made between surface and 50m layer with vertical gradients of 13.$0^{\circ}C$/50m and 3.8$\textperthousand$/50m.