• Pediatric Infection and Vaccine
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• v.3 no.2
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• pp.133-138
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• 1996

Purpose : In the treatment of MRSA infection, rapid detection of MRSA is extremely important. The mecA gene codes the new drug resistant polypeptides called PBP2' which mediates the clinically relevant resistance to all beta-lactam antibiotics. The identical mecA gene has been found in coagulase-negative staphylococcus with the methicillin-resistant phenotype. On the other hand, the femA gene was absent from coagulase negative staphylococcus strains with the methicillin resistant phenotype. This study is aimed at early detection and definite diagnosis of MRSA. Methods : A total of 24 MRSA strains were studied. All strains were tested for antimicrobial susceptibility and purified DNA. We amplified both mecA and femA genes by PCR in 24 strains. Results : In MRSA all the 16 strains (100%) carried femA gene and 11 strains (68.7%) carried mecA gene. In contrast, in methicillin sensitive staphylococcus all the 8 strains (100%) carried femA and only 3 strains (37.5%) were detected mecA. Conclusions : As results, there are difference in the phenotype and genotype of methicillin resistance by PCR of mecA and femA. Such disparities between methicillin resistance and the presence of mecA gene suggest the presence of control gene of the mecA.

• Electronics and Telecommunications Trends
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• v.37 no.4
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• pp.46-59
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• 2022

The 5G MEC (Multi-access Edge Computing) technology offers network and computing functionalities that allow application services to improve in terms of network delay, bandwidth, and security, by locating the application servers closer to the users at the edge nodes within the 5G network. To offer its interoperability within various networks and user equipment, standardization of the 5G MEC technology has been advanced in ETSI, 3GPP, and ITU-T, primarily for the MEC platform, transport support, and MEC federation. This article offers a brief review of the standardization activities for 5G MEC technology and the details about the system architecture and functionalities developed accordingly.

• Clinical and Experimental Pediatrics
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• v.46 no.1
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• pp.24-32
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• 2003

Purpose : Nosocomial infection with Staphylococcus aureus, especially methicillin resistant S. aureus, has become a serious concern in the neonatal intensive care unit. The aim of this study is to investigate the virulence factors, and the relationship between the antibiotic resistance and the associated genes of Staphylococcus aureus isolated from nasal cavity of neonates. Methods : Fifty one isolates of S. aureus were obtained from nasal swab taken in 28 neonates in the NICU and nursery of Pusan National University Hospital between February and May, 2001. They were tested in regard to antibiotic susceptibility, coagulase test and typing, plasmid DNA profile, as well as reactivity to enterotoxin A-E(sea, seb, sec, sed, see) genes and toxic shock syndrome toxin-1(tst) gene by polymerase chain reaction(PCR). Associated genes such as mecA, mecR1, mecI, and femA were also determined by PCR. The origin of MRSA strains was assessed using DNA fingerprinting by arbitrarily-primed polymerase chain reaction(AP-PCR). Results : Twenty three(45.1%) and six(11.8%) isolates were resistant to oxacillin and vancomycin respectively. Multidrug resistance to three or more of the antibiotics tested was observed in 51.0% of the isolates. Forty two isolates were coagulase positive and twenty two isolates had mecA gene. Sixteen isolates had both mecA and femA genes and had type I-III plasmids. 64.7% of isolates carried sec gene, and 80.4% carried tst gene. DNA fingerprinting by AP-PCR for 12 MRSA strains showed 10 distinct patterns, suggesting different origins. Conclusion : We confirmed that the prevalence of nasal carriage of S. aureus and the incidence of antimicrobial-resistant S. aureus, especially vancomycin resistance, is very high in neonates who were admitted in NICU and nursery. It is possible that these pathogens are responsible for serious nosocomial infections in neonates. The need for improved surveillance and continuous control of pathogens is emphasized.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.7
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• pp.827-838
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• 1999

멀티미디어 통신에서 통신 성능에 관련된 요구 사항들은 QoS 매개 변수들로서 서술된다. QoS매개 변수들에서 중요한 매개 변수 중의 하나가 전송 신뢰성이다. QoS 매개 변수로서의 신뢰성은 오류 감지, 보고 그리고 정정 기법으로 정의된다. 하지만 기존의 오류 제어 기법들은 멀티미디어 데이타의 통합된 관점을 고려하지 않았다. 그래서 우리는 MEC(multimedia-oriented error control)라고 명명된 오류 제어 기법을 제안하였다. 1 본 논문에서는 MEC기법의 성능을 조사 하였다. 성능평가 결과는 MEC기법이 기존의 오류 제어 기법보다도 낮은 전송지연(lower delay)과 호손율(blocking probability) 을 갖는다는 것을 볼 수 있었다. 결국 제안된 MEC기법은 수송 프로토콜에게 유연성과 높은 성능을 갖도록 해준다.Abstract Communication performance requirements are described as QoS parameters in multimedia communication. One of the important QoS parameters is the reliability of the transfer. As a QoS parameter, the reliability defines error detection, report and correction mechanisms. Conventional error control mechanisms, however, do not consider the integrated viewpoint of multimedia data. So we have proposed the MEC(multimedia-oriented error control). 1 In this paper, we have investigated the performance evaluation of the MEC. The results show that the MEC mechanism provides lower transfer delay and blocking probability than those of the conventional error recovery mechanism. Therefore, the proposed MEC mechanism makes the transport protocol have the flexibility and high performance.

• Journal of IKEEE
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• v.25 no.4
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• pp.598-606
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• 2021

In this paper, we presents a new service binding and resource management model for context based services in mobile edge computing (MEC) networks. The proposed control is composed of two layers: MEC service bindng control layer (MCL) and user context control layer (UCL). The MCL manages service binding construction, resource allocation, and service policy construction from a system point of view; and the UCL manages real-time service adaptation using meta-objects. Through simulations, we confirmed that the proposed control offers enhanced throughput and content transfer time when it is compared to the legacy computing and control models. The proposed control model can be employed as a key component for the context based various internet-of-things (IoT) services in MEC environments.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.5
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• pp.634-641
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• 2016

The objective of this research was to investigate the in vivo effects of treatment with mulberry extract complex (MEC) on cartilage degeneration and pain severity in an experimental model of rat degenerative arthritis. Monosodium iodoacetate ($2mg/50{\mu}L$) was injected into right knee joints of rats, followed by administration of MEC for 8 weeks at 400 mg/kg or 800 mg/kg of body weight. The experimental data show that treatment with MEC inhibited degradation of glycosaminoglycan and collagen in cartilage. On the other hand, concentrations of cartilage oligomeric matrix protein, C-terminal telopeptide-2, matrix metalloproteinase (MMP)-2, MMP-9, and MMP-13 in serum decreased in comparison with the control. The MEC at all dose levels could inhibit formation of xylene-induced ear edema. In this study, MEC demonstrated significant anti-arthritis activity, which is required for improvement of degenerative arthritis. Based on these results, MEC may be employed for the development of new health foods to ease symptoms of degenerative arthritis.

• Biomedical Science Letters
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• v.5 no.1
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• pp.131-133
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• 1999

In order to the investigate epidemiological characteristics of methicillin-resistant Staphylococcus aureus (MRSA), 31 strains of Staphylococcus aureus were isolated from the equipments of two hospitals in Chonbuk. And their antimicrobial resistance patterns against 7 kinds of antimicrobial agents and the identification of MRSA by polymerase chain reaction (PCR) were studied. Seven strains among 10 strains of methicillin resistant Staphylococcus aureus showed 554 bp DNA which was a part of mecA gene in PCR analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.45-47
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• 2022

Mobile edge computing (MEC), which enables mobile terminals to offload computational tasks to a server located at the user's edge, is considered an effective way to reduce the heavy computational burden and achieve efficient computational offloading. In this paper, we study a multi-user MEC system in which multiple user devices (UEs) can offload computation to the MEC server via a wireless channel. To solve the resource allocation and task offloading problem, we take the total cost of latency and energy consumption of all UEs as our optimization objective. To minimize the total cost of the considered MEC system, we propose an DRL-based method to solve the resource allocation problem in wireless MEC. Specifically, we propose a Asynchronous Advantage Actor-Critic (A3C)-based scheme. Asynchronous Advantage Actor-Critic (A3C) is applied to this framework and compared with DQN, and Double Q-Learning simulation results show that this scheme significantly reduces the total cost compared to other resource allocation schemes

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.74-77
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• 2022

최근 사물 인터넷(IoT)의 발전으로 계산 집약적이거나 지연시간에 민감한 태스크가 증가하면서, 모바일 엣지 컴퓨팅 기술이 주목받고 있지만 지상에 고정되어 있는 MEC 서버는 사용자의 요구사항 변화에 따라 서버의 위치를 변경하거나 유연하게 대처할 수 없다. 이 문제를 해결하기 위해 UAV(Unmanned Aerial Vehicle)를 추가로 이용해 엣지 서비스를 제공하는 기법이 연구되고 있다. 그러나 UAV는 지상 MEC와는 달리 배터리 용량이 제한되어 있어 태스크 마이그레이션을 통해 에너지 사용량을 최소화하는 것이 필요하다. 본 논문에서는 MEC 서버들 사이의 로드 밸런싱과 UAV MEC 서버의 에너지 효율성을 최적화하기 위해 강화학습 기법인 Q-learning을 이용한 태스크 마이그레이션 기법을 제안한다. 제안 시스템의 성능을 평가하기 위해 UAV의 개수에 따라 실험을 진행하여 잔여 에너지와 로드 밸런싱 측면에서 성능을 분석한다.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.