• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.23-29
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• 2010

Korean shipbuilding companies have sometimes carried out sea trials to measure a vessel's speed performance around the western channel of the Straits of Korea, where the flow fields are very complicated because of the effect of various flows such as sea, tidal, geostrophic, and wind-driven currents. Because these flows seem to present significant interference to a ship, the numerical reproduction of the flow-fields in the vicinity of the target sites could provide a better understanding of the sea environments while performing sea trials. In this study, we used the MEC ocean model to simulate the tidal currents around Tsushima Island and compared the simulated tidal amplitudes and currents with the measurements of Teague et al. (2001). The tidal amplitudes of the present simulation results agreed well with the observations. Based on the numerical simulation, the optimal direction and proper sites for a speed trial are described.

• Biomedical Science Letters
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• v.27 no.4
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• pp.195-207
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• 2021

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial pathogen capable of causing human diseases, such as soft tissue infection, bacteremia, endocarditis, toxic shock syndrome, pneumonia, and sepsis. Although the incidence rate of diseases caused by MRSA has declined in recent years, these diseases still pose a clinical threat due to their consistently high morbidity and mortality rates. However, the role of virulence factors in staphylococcal infections remains incompletely understood. Methicillin resistance, which confers resistance to all β-lactam antibiotics in cellular islets, is mediated by the mecA gene in the staphylococcal cassette chromosome mec (SCCmec). Differences in SCCmec types and differences in their sizes and structures serve epidemiological purposes and are used to differentiate between hospital-associated (HA)-MRSA and community-associated (CA)-MRSA. Some virulence factors of S. aureus are also providing a distinction between HA-MRSA and CA-MRSA. These factors vary depending on the presence of toxins, adhesion, immune evasion, and other virulence determinants. In this review, we summarized an overview of MRSA such as resistance mechanisms, SCCmec types, HA- and CA-MRSA, and virulence factors that enhance pathogenicity or MRSA epidemiology, transmission, and genetic diversity.

• Journal of Veterinary Clinics
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• v.22 no.1
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• pp.50-55
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• 2005

We investigated the contamination of animal hospital floor, beauty table, computer keyboard, exam table, operation table and forcep handle by isolations of aerobic bacteria in small animal hospitals in Gwangju. The total number of aerobic bacteria was 52 isolates and Staphylococcus spp. (38 isolates) were the predominant isolates (69.71 %) of them. The prevalent contaminated areas were floor (17 isolates), beauty table (13 isolates) and computer keyboard (9 isolates). The detection of methicillin-resistant (mecA) gene, determined by PCR, showed that 3 of the 17 coagulase-negative Staphylococcus spp. (CNS) isolates possessed the mecA gene. For evaluating the antibiotic susceptibility patterns of the isolates, disk diffusion method was used. The majority of isolates showed high susceptibility to amoxicillin (92.1 %), ceftiofur (84.2%) and polymixin B (73.7%). Also they showed the high resistant to ampicilline (66.7%), penicillin (65%) and kanamycin (56.5%). These results suggest extensive contamination of aerobic bacteria in animal hospital environment.

• Journal of Life Science
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• v.26 no.2
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• pp.220-225
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• 2016

Coagulase-negative staphylococci (CNS) have recently become the bacteria most frequently found in clinical infections. The aim of this study was to investigate the prevalence, antimicrobial susceptibilities, and molecular characteristics of CNS isolates from dental clinic environments in Busan, Korea. One hundred and fifty-four samples were collected from 10 dental clinics and dental hospitals in Busan from December 2014 to January 2015. Species were identified by matrix-assisted laser desorption/ionization–time-of-flight. Antimicrobial susceptibility was determined by disk diffusion methods. A polymerase chain reaction was performed to detect mecA, mupA gene, and SCCmec types. Of the 154 samples, 10(6.5%) isolates were identified as CNS (5 Staphylococcus epidermidis, 2 Staphylococcus capitis, 2 Staphylococcus, and 1 Staphylococcus haemolyticus). Among the 10 isolates, 6 were resistant to penicillin, 5 were resistant to gentamicin, 3 were resistant to tetracycline, and 2 were resistant to cefoxitin and erythromycin. However, clindamycin, ciprofloxacin, teicoplanin, and trimethoprim-sulfamethoxazole resistant isolates were not present. Genes encoding mecA were detected in 4 (2 S. warneri and 2 S. haemolyticus) isolates, and mupA in 1 (S. epidermidis) isolate. One methicillin-resistant CNS (S. warneri) isolate was determined as being of the SCCmec type I. It is concluded that CNS resistant to various antimicrobial agents was widely distributed in dental clinic environments in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4849-4864
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• 2019

Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.

• The Korean Journal of Oral and Maxillofacial Pathology
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• v.42 no.6
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• pp.167-174
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• 2018

Natural products are vastly utilized as a source of chemotherapeutic agents for human cancers. Kochia scopraia is traditionally used for the cure of urological and dermatological diseases. Recently, methanol extract of Kochia scoparia (MEKS) has been shown to have anti-cancer activity to various human cancers. However, there is no report demonstrating the anti-cancer activity of MEKS in human mucoepidermoid carcinoma (MEC) cells. In this study, the authors studied the effects of MEKS on the cell proliferation and underlying mechanism in YD15 human MEC cells. MEKS decreased YD15 cell proliferation proven by trypan blue exclusion assay and induced apoptosis, evidenced by cell cycle analysis and western blotting. Autophagy induction by MEKS was verified by western blotting. In addition, MEKS regulated the expression of phosphorylated Akt, phosphorylated p38 and Nrf2 protein. This results can imply that MEKS might be a potential candidate for the treatment of human MEC cells.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.11-17
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• 2021

Due to the recent development of the Internet of Things (IoT) and the increase in services using drones, research on IoD is actively underway. Drones have limited computational power and storage size, and when communicating between drones, data is exchanged after proper authentication between entities. Drones must be secure from traceability because they contain sensitive information such as location and travel path. In this paper, we point out a fatal security vulnerability that can be caused by the use of pseudonyms and certificates in existing IoD research and propose a solution.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.1-10
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• 2023

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.2
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• pp.110-117
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• 2018

In the present study, mec complex typing and SCCmec typing were performed to analyze the molecular genetic characteristics of 20 methicillin-resistant Staphylococcus aureus (MRSA) strains isolated from clinical specimens and 4 strains isolated from the ICU environments of secondary medical institutions in a Chungnam province, Korea, from June to July of 2017. Among a total of 20 MRSA strains isolated from clinical specimens, 8 cases (40%) were SCCmec type II, one case (5%) was SCCmec type IVa, and 11 cases (55%) were not-typeable in SCCmec type analysis. Among 4 MRSA isolates from the ICU environment, one strain did not have the mecA gene and 3 strains were typed as SCCmec types II, III, and IVa, respectively. Data from the present study showed that the origin of MRSA isolated from the clinical specimens was different from those from the ICU environment in most cases but the origin was concordant in one case. In this case, MRSA might be transmitted by healthcare workers to the ICU environment. Further study with a large number of cases and other hospital infection-related microorganisms will be needed. This continuous follow-up study might provide useful information on infection control in medical institutions.