• Ocean and Polar Research
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• v.26 no.4
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• pp.559-565
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• 2004

Total magnetic field measurements were performed to study paleomagnetism of three seamounts (OSM7, OSM8-1, and OSM8-2) to the northwest of the Marshall Islands in the western Pacific. The study area is located at the Ogasawara Fracture Zone which is a boundary between the Pigafetta and East Mariana basins. The magnetic parameters and paleopoles of three seamounts were derived from inversion of the measured magnetic field. The goodness-of-fit ratio of OSM7 is too low to be included to the estimation of parameters. The complex magnetic anomalies of center, scarcity of flank rift zones and steep slope at OSM7 suggest that the multiple intrusions of magma converge into the center of volcanic edifice. Inclination calculated from the magnetic anomalies of OSM8-1 and OSM8-2 is $-41.2^{\circ}$, and the paleolatitude calculated from the inclination is $23.6^{\circ}S$. The corresponding paleopoles for OSM8-1 and OSM8-2 are $(24^{\circ}42'W,\;48^{\circ}54'N)\;and\;(18^{\circ}18'W,\;48^{\circ}30'N)$, respectively. In comparison with the apparent polar wander path (APWP) of the Pacific plate, the paleopoles are close to 129-Ma pole. The paleopoles and paleolatitudes of OSM8-1 and OSM8-2 suggest that they were formed at similar time and location. The seamounts have drifted northward about $41^{\circ}$ from the paleolatitude to present latitude of seamounts.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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• pp.285-291
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• 2016

There has been an accelerating increase in water reuse due to growing world population, rapid urbanization, and increasing scarcity of water resources. However, it is well recognized that water reuse practice is associated with many human health and ecological risks due to numerous chemicals and pathogenic microorganisms. Especially, the potential transmission of infectious disease by hundreds of pathogenic viruses in wastewater is one of the most serious human health risks associated with water reuse. In this study, we determined the response of different bacteriophages representing various bacteriophage groups to chlorination in real wastewater in order to identify a more reliable bacteriophage indicator system for chlorination in wastewater. Different bacteriophages were spiked into secondary effluents from wastewater plants from three different geographic areas, and then subjected to various doses of free chlorine and contact time at $5^{\circ}C$ in a bench-scale batch disinfection system. The inactivation of ${\phi}X174$ was relatively rapid and reached ~4 log10 with a CT value of 5 mg/L*min. On the other hand, the inactivation of bacteriophage PRD1 and MS2 were much slower than the one for ${\phi}X174$ and only ~1 log10 inactivation was achieved by a CT value of 10 mg/L*min. Overall, the results of this study suggest that bacteriophage both MS2 and PRD1 could be a reliable indicator for human pathogenic viruses for chlorination in wastewater treatment processes and water reuse practice.

• Steel and Composite Structures
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• v.51 no.1
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• pp.25-41
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• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.111-111
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• 2017

The development and productivity of maize (Zea mays L.) is frequently impacted by water scarcity, and consequently to increased drought tolerance in a priority target in maize breeding programs. To elucidate the molecular mechanisms of resistance to drought stress in maize, RNA-seq of the public database was used for transcriptome profiling of the seedling stage exposed to drought stress of three levels, such as moderate, severe drought stress and re-watering. In silico analysis of differentially expressed genes (DEGs), 176 up-regulated and 166 down-regulated DEGs was detected at moderated stress in tolerance type. These DEGs was increasing degradation of amino acid metabolism in biological pathways. Six modules based on a total of 4,771 DEGs responses to drought stress by the analysis of co-expression network between tolerance and susceptible type was constructed and showed to similar module types. These modules were discriminated yellow, greenyellow, turquoise, royalblue, brown4 and plum1 with 318, 2433, 375, 183, 1405 and 56 DEGs, respectively. This study was selected 30 DEGs to predicted drought stress response gene and was evaluated expression levels using drought stress treated sample and re-watering sample by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). 23 genes was shown increasing with drought stress and decreasing with re-watering. This study contribute to a better understanding of the molecular mechanisms of maize seedling stage responses to drought stress and could be useful for developing maize cultivar resistant to drought stress.

• Computers and Concrete
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• v.30 no.4
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• pp.277-287
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• 2022

Due to the scarcity of extortionate experimental data, fatigue failure of the reinforced concrete (RC) element might be achieved economically adopting nonlinear finite element (FE) analysis as an alternative approach. However, conventional implicit dynamic analysis is expensive, quasi-static method overlooks interaction effects and inertia, direct cyclic analysis computes stabilized responses. Apart from this, explicit dynamic analysis may provide a numerical operating system for factual long-term responses. The study explores the fatigue behavior based on a simplified explicit dynamic solution employing nonlinear time domain analysis. Among fourteen RC beams, one beam is selected to validate under static loading, one under fatigue with the experimental study and other twelve to check the detail fatigue behavior. The SWOT (Strength, Weakness, Opportunities, Threats) analysis has been carried out to pinpoint the detail scenario in the adoption of numerical approach as an alternative to the experimental study. Excellent agreement of FE and experimental results is seen. The 3D nonlinear RC beam model at service fatigue limits is truthful to be used as an expedient contrivance to envisage the precise fatigue behavior. The simplified analysis approach for RC beam under fatigue offers savings in computation to predict responses providing acceptable accuracy rather than the complicated laboratory investigation. At higher frequency, the flexural failure occurs a bit earlier gradually compared to the repeated loading case of lower frequency. The deflection increases by 6%-10% at the end of first cycle for beams with increasing frequency of cyclic loading. However, at the end of fatigue loading, greater deflection occur earlier for higher load range because of more rapid stiffness degradation. For higher frequency, a slight boost in concrete compressive strains at an initial stage of loading has been seen indicating somewhat stepper increment. Stiffness degradation in larger loading cycle at same duration escalates the upsurge of the rate of strain in case of higher frequency.

• The Plant Pathology Journal
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• v.39 no.5
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• pp.466-485
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• 2023

Crop productivity can be obstructed by various biotic and abiotic stresses and thus these stresses are a threat to universal food security. The information on the use of viruses providing efficacy to plants facing growth challenges owing to stress is lacking. The role of induction of pathogen-related genes by microbes is also colossal in drought-endurance acquisition. Studies put forward the importance of viruses as sustainable means for defending plants against dual stress. A fundamental part of research focuses on a positive interplay between viruses and plants. Notably, the tomato yellow leaf curl virus (TYLCV) and tomato chlorosis virus (ToCV) possess the capacity to safeguard tomato host plants against severe drought conditions. This study aims to explore the combined effects of TYLCV, ToCV, and drought stress on two tomato cultivars, Money Maker (MK, UK) and Shalala (SH, Azerbaijan). The expression of pathogen-related four cellulose synthase gene families (CesA/Csl) which have been implicated in drought and virus resistance based on gene expression analysis, was assessed using the quantitative real-time polymerase chain reaction method. The molecular tests revealed significant upregulation of Ces-A2, Csl-D3,2, and Csl-D3,1 genes in TYLCV and ToCV-infected tomato plants. CesA/Csl genes, responsible for biosynthesis within the MK and SH tomato cultivars, play a role in defending against TYLCV and ToCV. Additionally, physiological parameters such as "relative water content," "specific leaf weight," "leaf area," and "dry biomass" were measured in dual-stressed tomatoes. Using these features, it might be possible to cultivate TYLCV-resistant plants during seasons characterized by water scarcity.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.220-220
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• 2015

Water, energy, and food security already became a risk that threatens people around the world. Increasing of resources demand, rapid urbanization, decreasing of natural resources and climate change are four major problems inducing resources' scarcity. Indeed, water, energy, and food are interconnected each other thus cannot be analyzed separately. That is, for simple example, energy needs water as source for hydropower plant, water needs energy for distribution, and food needs water and energy for production, which is defined as W-E-F nexus. Due to their complicated linkage, it needs a computer model to simulate and analyze the nexus. Development of a computer simulation model using system dynamics approach makes this linkage possible to be visualized and quantified. System dynamics can be defined as an approach to learn the feedback connections of all elements in a complex system, which mean, every element's interaction is simulated simultaneously. Present W-E-F nexus models do not calculate and simulate the element's interaction simultaneously. Existing models only calculate the amount of water and energy resources that needed to provide food, water, or energy without any interaction from the product to resources. The new proposed model tries to cope these lacks by adding the interactions, climate change effect, and government policy to optimize the best options to maintain the resources sustainability. On this first phase of development, the model is developed only to learn and analyze the interaction between elements based on scenario of fulfilling the increasing of resources demand, due to population growth. The model is developed using the Vensim, well-known system dynamics model software. The results are amount of total water, energy, and food demand and production for a certain time period and it is evaluated to determine the sustainability of resources.