• 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.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.237-248
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• 2022

Nowadays, more and more subway tunnels were planed and constructed underneath the ground of urban cities to relieve the congested traffic. Potential damage may occur in existing tunnel if the new tunnel is constructed too close. So far, previous studies mainly focused on the tunnel-tunnel interactions with circular shape. The difference between circular and horseshoe shaped tunnel in terms of deformation mechanism is not fully investigated. In this study, three-dimensional numerical parametric studies were carried out to explore the effect of different tunnel shapes on the complicated tunnel-tunnel interaction problem. Parameters considered include volume loss, tunnel stiffness and relative density. It is found that the value of volume loss play the most important role in the multi-tunnel interactions. For a typical condition in this study, the maximum invert settlement and gradient along longitudinal direction of horseshoe shaped tunnel was 50% and 96% larger than those in circular case, respectively. This is because of the larger vertical soil displacement underneath existing tunnel. Due to the discontinuous hoop axial stress in horseshoe shaped tunnel, significant shear stress was mobilized around the axillary angles. This resulted in substantial bending moment at the bottom plate and side walls of horseshoe shaped tunnel. Consequently, vertical elongation and horizontal compression in circular existing tunnel were 45% and 33% smaller than those in horseshoe case (at monitored section X/D = 0), which in latter case was mainly attributed to the bending induced deflection. The radial deformation stiffness of circular tunnel is more sensitive to the Young's modulus compared with horseshoe shaped tunnel. This is because of that circular tunnel resisted the radial deformation mainly by its hoop axial stress while horseshoe shaped tunnel do so mainly by its flexural rigidity. In addition, the reduction of soil stiffness beneath the circular tunnel was larger than that in horseshoe shaped tunnel at each level of relative density, indicating that large portion of tunneling effect were undertaken by the ground itself in circular tunnel case.

• Journal of Soil and Groundwater Environment
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• v.28 no.5
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• pp.12-24
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• 2023

The Songji lagoon is brackish environment with a mixture of saline and fresh water, and the interaction of groundwater-lagoon water creates a physicochemical gradient. Although some studies have been conducted on the hydrological and geochemical characteristics of the Songji lagoon, microbial ecological studies have not yet been conducted. In this study, we investigated the effect of groundwater and surface water interaction on water quality as well as microbial community changes in the Songji Lagoon using 16S rRNA gene sequencing. Hydrochemical analyses show that samples were classified as 5 hydrochemical facies (HF) and hydrochemical facies evolution (HFE) revealed the intrusion phase was more dominant (57.9%) than the freshening phase (42.1%). Higher microbial diversity was found in freshwater in comparison to saline water samples. The microbial community at the phylum level shows the most dominance of Proteobacteria with an average of 37.3%, followed by Bacteroidota, Actinobacteria, and Patescibacteria. Heat map analyses of the top 18 genera showed that samples were clustered into 5 groups based on type, and Pseudoalteromonas could be used potential indicator for seawater intrusion.

• Steel and Composite Structures
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• v.49 no.5
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• pp.487-502
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• 2023

In the realm of nanotechnology, the nonlocal strain gradient theory takes center stage as it scrutinizes the behavior of spinning cantilever nanobeams and nanotubes, pivotal components supporting various mechanical movements in sport structures. The dynamics of these structures have sparked debates within the scientific community, with some contending that nonlocal cantilever models fail to predict dynamic softening, while others propose that they can indeed exhibit stiffness softening characteristics. To address these disparities, this paper investigates the dynamic response of a nonlocal cantilever cylindrical beam under the influence of external discontinuous dynamic loads. The study employs four distinct models: the Euler-Bernoulli beam model, Timoshenko beam model, higher-order beam model, and a novel higher-order tube model. These models account for the effects of functionally graded materials (FGMs) in the radial tube direction, giving rise to nanotubes with varying properties. The Hamilton principle is employed to formulate the governing differential equations and precise boundary conditions. These equations are subsequently solved using the generalized differential quadrature element technique (GDQEM). This research not only advances our understanding of the dynamic behavior of nanotubes but also reveals the intriguing phenomena of both hardening and softening in the nonlocal parameter within cantilever nanostructures. Moreover, the findings hold promise for practical applications, including drug delivery, where the controlled vibrations of nanotubes can enhance the precision and efficiency of medication transport within the human body. By exploring the multifaceted characteristics of nanotubes, this study not only contributes to the design and manufacturing of rotating nanostructures but also offers insights into their potential role in revolutionizing drug delivery systems.

• Structural Engineering and Mechanics
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• v.90 no.3
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• pp.233-252
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• 2024

This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam's directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures.

• Structural Engineering and Mechanics
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• v.90 no.3
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• pp.253-261
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• 2024

This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam's directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures.