• The Plant Pathology Journal
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• v.36 no.6
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• pp.536-557
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• 2020

Sugarcane yellow leaf virus (SCYLV) is a distinct member of the Polerovirus genus of the Luteoviridae family. SCYLV is the major limitation to sugarcane production worldwide and presently occurring in most of the sugarcane growing countries. SCYLV having high genetic diversity within the species and presently ten genotypes are known to occur based on the complete genome sequence information. SCYLV is present in almost all the states of India where sugarcane is grown. Virion comprises of 180 coat protein units and are 24-29 nm in diameter. The genome of SCYLV is a monopartite and comprised of single-stranded (ss) positive-sense (+) linear RNA of about 6 kb in size. Virus genome consists of six open reading frames (ORFs) that are expressed by sub-genomic RNAs. The SCYLV is phloem-limited and transmitted by sugarcane aphid Melanaphis sacchari in a circulative and non-propagative manner. The other aphid species namely, Ceratovacuna lanigera, Rhopalosiphum rufiabdominalis, and R. maidis also been reported to transmit the virus. The virus is not transmitted mechanically, therefore, its transmission by M. sacchari has been studied in different countries. SCYLV has a limited natural host range and mainly infect sugarcane (Sachharum hybrid), grain sorghum (Sorghum bicolor), and Columbus grass (Sorghum almum). Recent insights in the protein-protein interactions of Polerovirus through protein interaction reporter (PIR) technology enable us to understand viral encoded proteins during virus replication, assembly, plant defence mechanism, short and long-distance travel of the virus. This review presents the recent understandings on virus biology, diagnosis, genetic diversity, virus-vector and host-virus interactions and conventional and next generation management approaches.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.47-56
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• 2003

Considering the high density developments along the major traffic corridors in Seoul, transit-oriented street designs will be a very effective to control traffic congestion along the corridors. For testing the effectiveness, we selected. for our case study, Kangnam Street, which is one of the most highly developed corridors in Seoul The traffic study on Kangnam street in 2000 shows that the daily average bus speed is 11.73km/h, which is 5km/h lower than the auto speed. The Central Bus Lane system was applied on the Kangnam street to test impact on bus speed as well as auto speed. Simulation results show that with Central Bus Lane have been improved the travel speeds of bus as well as auto on Kangnam street from 14.4km/hr to 35.0km/hr and from 25.1km/hr to 26.1km/hr, respectively. The bus market share increases about 6-8 percentages. Especially, 13.4% of bus users are increased for long-distance trips.

• Fire Science and Engineering
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• v.18 no.2
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• pp.57-67
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• 2004

The results of the risk assessing on general buses, consisting mainly of diesel-fueled buses, show that the frequency of the instantaneous release is 1.4${\times}$10$^{-3}$ /bus/year, from which the probability of the formation of fireball as a sub event becomes 1.7${\times}$104, and show that the leakage from the CNG-fueled buses is 0.002 event/year. Also, the frequency of gradual release due to a crack is estimated at 3.7${\times}$10$^{-3}$ /buses/year, and a subsequent probability at which this could lead to a jet flame as a sub event is 1.2${\times}$10$^{-3}$ This corresponds to 0.04event/year for the CNG-fueled buses. Dividing all the fired casualties by the running distance of diesel-fueled buses, the risk is 0.091 fire fatalities per 100-million miles. And the total fire risk fur CNG buses is approximately 0.17 per 100-million miles of travel. This means that CNG buses is twice or more dangerous than diesel buses. After all CNG buses are more susceptible to the major fires. In the aspect of the reliability of this study, generic models and the failure data used in assessing the risks of CNG buses are appropriate. However, more accurate physics-based models and databases should be supplemented with this study to provide the better results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Oriental Neuropsychiatry
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• v.30 no.3
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• pp.209-220
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• 2019

Objectives: As a general emotion, everyone can temporarily experience depression, but depressive disorder is a disease that excessively affects daily life. Among the various causes of depression, the deficiency of monoamine-based neurotransmitters such as serotonin and epinephrine are considered significant. Thus, antidepressants that target monoamines are used frequently. However, side effects such as nausea, vomiting, insomnia, anxiety, and sexual dysfunction are observed. Thus, it is necessary to develop a new therapeutic agent with fewer side effects. In this study, we investigated the antidepressant effect of JG02, used to treat depression by normalizing the flow of qi (氣) in Korean medicine. Methods: C57BL/6 mice were selected and randomly divided into six groups: normal, control, amitriptyline, and JG02 (50, 125, 250 mg/kg), respectively. Except for normal, depression was induced by applying restraint stress at the same time for six hours daily for 14 consecutive days. Saline, amitriptyline or JG02 samples were orally administered two hours before applying the stress. After that, a forced swimming test and an open field test were performed. Additionally, serum corticosterone, serotonin mRNA, BDNF mRNA, and protein in the hippocampal region were measured and compared. Results: JG02 decreased immobility time rate in the FST and increased the zone transition number and travel distance in the OFT. Also, JG02 inhibited the release of serum corticosterone, and increased serotonin, BDNF gene expression, and BDNF protein in the hippocampus. Conclusions: In this study, JG02 showed significant antidepressant effects on the chronic restraint stress mice model. When further research is performed based on JG02, the development of a new antidepressant is considered highly possible.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.73-80
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• 2010

A wireless sensor network which is deployed in hostile environment can be easily compromised by attackers. The selective forwarding attack can jam the packet or drop a sensitive packet such as the movement of the enemy on data flow path through the compromised node. Xiao, Yu and Gao proposed the checkpoint-based multi-hop acknowledgement scheme(CHEMAS). In CHEMAS, each path node enable to be the checkpoint node according to the pre-defined probability and then can detect the area where the selective forwarding attacks is generated through the checkpoint nodes. In this scheme, the number of hops is very important because this parameter may trade off between energy conservation and detection capacity. In this paper, we used the fuzzy rule system to determine adaptive threshold value which is the number of hops for the ACK packets. In every period, the base station determines threshold value while using fuzzy logic. The energy level, the number of compromised node, and the distance to each node from base station are used to determine threshold value in fuzzy logic.

• Journal of the Korean Geotechnical Society
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• v.23 no.12
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• pp.33-42
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• 2007

The assessment of shear wave velocity($V_s$) in soft soils is extremely difficult due to the soil disturbances during sampling and field access. After a ring type field $V_s$ probe(FVP) has been developed, it has been applied at the southern coastal area of the Korean peninsular. This study presents the upgraded FVP "blade type FVP", which minimizes soil disturbance during penetration. Design concerns of the blade type FVP include the tip shape, soil disturbance, transducers, protection of the cables, and the electromagnetic coupling between transducers and cables. The cross-talking between cables is removed by grouping and extra grounding of the cables. The shear wave velocity of the FVP is simply calculated by using the travel distance and the first arrival time. The large calibration chamber tests are carried out to investigate the disturbance effect due to the penetration of FVP blade and the validity of the shear waves measured by the FVP. The blade type FVP is tested in soils up to 30m in depth. The shear wave velocity is measured every 10cm. This study suggests that the upgraded blade type FVP may be an effective device for measuring the shear wave velocity with minimized soil disturbance in the field.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.1-14
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• 2023

This study derives quantitative data on how much the fiscal deficit of subway operation agencies can be reduced in the process of charging free rides for the elderly in metropolitan subways during peak periods. In smart card data, every trip of elderly is recorded except fares. Therefore, it is required to establish a methodology for estimating the fares of elderly passengers and distributing them to subway opertation agencies as income. This study builds a simultaneous dynamic traffic allocation model that reflects the assumption that elderly selects a minimum time route based on the departure time. The travel route of the elderly is estimated, and the distance-proportional fare charged to the elderly is calculated based on this, and the fare is distributed by reflecting the connected railway revenue allocation principle of the metropolitan subway operating agencies. As a result of conducting a case study for before and after COVID-19 in 2019 and 2020, it is analyzed that Seoul Metro's annual free loss of 360 billion won could be reduced 6~8% at the morning peak (07:00-08:59), and 13~16% at the morning and afternoon peak (18:00-19:59).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.443-451
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• 2010

This paper proposes the methodology about advisory safety speed based on real-time vehicular data collected from highway. The proposed model is useful information to drivers by appling seamless wireless communication and being collected from ECU(Engine Control Unit) equipment in every vehicle. Furthermore, this model also permits the use of realtime sensing data like as adverse weather and road-surface data. Here, the advisory safety speed is defined "the safety speed for drivers considering the time-dependent traffic condition and road-surface state parameter at uniform section", and the advisory safety speed model is developed by considering the parameters: inter-vehicles safe stopping distance, statistical vehicle speed, and real-time road-surface data. This model is evaluated by using the simulation technique for exploring the relationships between advisory safety speed and the dependent parameters like as traffic parameters(smooth condition and traffic jam), incident parameters(no-accident and accident) and road-surface parameters(dry, wet, snow). A simulation's results based on 12 scenarios show significant relationships and trends between 3 parameters and advisory safety speed. This model suggests that the advisory safety speed has more higher than average travel speed and is changeable by changing real-time incident states and road-surface states. The purpose of the research is to prove the new safety related services which are applicable in SMART Highway as traffic and IT convergence technology.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.551-560
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• 2009

This paper describes a newly developed pollutant transport model named ARPTM which was designed to simulate the transport and characteristics of pollutant materials after an accidental spill in upstream of river system up to a given position in the downstream. In particular, the ARPTM incorporated ADCP data to compute longitudinal dispersion coefficient and advection velocity which are necessary to apply one-dimensional advection-dispersion equation. ARPTM was built on top of the geographic information system platforms to take advantage of the technology's capabilities to track geo-referenced processes and visualize the simulated results in conjunction with associated geographic layers such as digital maps. The ARPTM computes travel distance, time, and concentration of the pollutant cloud in the given flow path from the river network, after quickly finding path between the spill of the pollutant material and any concerned points in the downstream. ARPTM is closely connected with a recently developed GIS-based Arc River database that stores inputs and outputs of ARPTM. ARPTM thereby assembles measurements, modeling, and cyberinfrastructure components to create a useful cyber-tool for determining and visualizing the dynamics of the clouds of pollutants while dispersing in space and time. ARPTM is expected to be potentially used for building warning system for the transport of pollutant materials in a large basin.