• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1122-1133
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• 2019

We have designed and implemented a new data processing framework called MOHA(Mtc On HAdoop) which can effectively support Many-Task Computing(MTC) applications in a YARN-based Hadoop platform. MTC applications can be composed of a very large number of computational tasks ranging from hundreds of thousands to millions of tasks, and each MTC application may have different resource usage patterns. Therefore, we have implemented MOHA-TaskExecutor(a pilot-job that executes real MTC application tasks)'s Adaptive Parallel Computability which can adaptively execute multiple tasks simultaneously, in order to improve the parallel computability of a YARN container and the overall system throughput. We have implemented multi-threaded version of TaskExecutor which can "independently and dynamically" adjust the number of concurrently running tasks, and in order to find the optimal number of concurrent tasks, we have employed Hill-Climbing algorithm.

• Journal of KIISE
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• v.42 no.1
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• pp.15-22
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• 2015

In this paper, we present a case study for a drug repositioning simulation based on distributed supercomputing technology that requires highly efficient processing of large-scale computations. Drug repositioning is the application of known drugs and compounds to new indications (i.e., new diseases), and this process requires efficient processing of a large number of docking tasks with relatively short per-task execution times. This mechanism shows the main characteristics of a Many-Task Computing (MTC) application, and as a representative case of MTC applications, we have applied a drug repositioning simulation in our HTCaaS system which can leverage distributed supercomputing infrastructure, and show that efficient task dispatching, dynamic resource allocation and load balancing, reliability, and seamless integration of multiple computing resources are crucial to support these challenging scientific applications.

• Journal of KIISE
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• v.43 no.6
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• pp.613-620
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• 2016

This paper presents a MOHA(Many-Task Computing on Hadoop) framework which aims to effectively apply the Many-Task Computing(MTC) technologies originally developed for high-performance processing of many tasks, to the existing Big Data processing platform Hadoop. We present basic concepts, motivation, preliminary results of PoC based on distributed message queue, and future research directions of MOHA. MTC applications may have relatively low I/O requirements per task. However, a very large number of tasks should be efficiently processed with potentially heavy inter-communications based on files. Therefore, MTC applications can show another pattern of data-intensive workloads compared to existing Hadoop applications, typically based on relatively large data block sizes. Through an effective convergence of MTC and Big Data technologies, we can introduce a new MOHA framework which can support the large-scale scientific applications along with the Hadoop ecosystem, which is evolving into a multi-application platform.

• Korean Journal of Clinical Pharmacy
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• v.26 no.4
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• pp.318-323
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• 2016

Background: Multidisciplinary team care (MTC) is a collaborative approach to treatment plan and ongoing care. We aimed to evaluate the clinical effect of MTC on the regulation of chronic kidney disease-mineral and bone disorder (CKD-MBD) complications in dialysis patients. Methods: This retrospective observational study was approved by the institutional review board. Among patients who have undergone dialysis at admission, the patients admitted to the nephrology ward were allocated to MTC group, and the others to usual care (UC) group. The MTC group had collaborative care by nephrologists, nurses, pharmacists, and nutritionists. The endpoints were the regulation of corrected calcium (cCa) and phosphate (P), the percent of patients in target level of cCa-P product ($cCa{\times}P$), and the prescription rate of non-calcium based P-binders. Results: A total of 163 patients were included from January to December 2009. A significant difference was shown in the percentage of patients in target $cCa{\times}P$ level at admission (MTC vs. UC, 81.40% vs. 91.67%; P = 0.038), but there was no significant difference at discharge. During admission, the cCa and P levels of patients in only UC group were significantly changed. In addition, compared with UC group, patients in MTC group were more likely prescribed appropriate P-binders, when they had higher $cCa{\times}P$ levels than $55mg^2/dL^2$ (P <0.001). Conclusion: It was found that MTC had beneficial effect on improving the regulation of CKD-MBD and the appropriate phosphate binder uses. Therefore, application of the MTC is anticipated to enhance quality of clinical care in chronic diseases.

• Journal of Biosystems Engineering
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• v.34 no.1
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• pp.57-62
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• 2009

Kinematic analysis of MTC (Muscle-Tendon Complex) units is a key indicator for diagnosis of patients with musculoskeletal disorders because the contracture or shortening of musculo-tendinous units is known to produce pathological gaits. Therefore, the principal objective of this study was to assess the length change in the triceps surae prior to and after wearing an AFO (Ankle-Foot Orthoses) in patients with musculoskeletal disorders during a gait. In this study, analyses were conducted using a Muscle Tendon Complex model coupled with the trajectory data from markers attached to anatomical landmarks. As a result, the maximum length change in the triceps surae during a gait was 4.87% when a barefoot walking group and a walking group with AFO were compared. In particular, the difference in length changes between both groups in Soleus MTC units was found to be statistically significant in all gait phases. Our results revealed that MTC length in the AFO walking group was clearly increased over that of the barefoot walking group. In the future, further studies will be required in order to more adequately assess musculoskeletal disorders using many cases studies with regard to agricultural working conditions because this study deals with the kinematic analysis of musculo-tendinous units in the case of clinical experiments.

• Korean Journal of Veterinary Service
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• v.30 no.4
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• pp.527-532
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• 2007

In present study, we described the reliability of the dual priming oligonucleotide (DPO) multiplex polymerase chain reaction (PCR) for the detection of Mycobacterium tuberculosis complex (MTC) and non-Mycobacterium tuberculosis (NMT) in blood samples of the Korea native cattle, Hanwoo. Among 340 samples 22 (6.5%) were positive in using DPO multiplex PCR, 21 (6.2%) were positive in PCR. The relative agreement between 2 tests was 99.7%, and the agreement quotient (kappa), was 0.95 (excellent). In these results, we demonstrated the successful application of DPO multiplex PCR for the diagnosis of bovine tuberculosis in Hanwoo. Multiplex PCR, using DPO primers, can be useful for the simple diagnosis of bovine tuberculosis in bovine blood samples.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4601-4619
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• 2022

The classical Probabilistic Safety Analysis (PSA) does not include any time dependence explicitly. However, the success criteria (SC) could evolve during the cycle for some initiating events. In that sense, there is a type of sequence in which this time-dependency is quite important, the family of Anticipated Transient without Scram (ATWS) sequences in Pressurized Water Reactors. Therefore, a new risk-informed approach is proposed in this paper, which makes it possible to obtain the time-dependent SC evolution of the safety functions affected by the Moderator Temperature Coefficient (MTC) value. Then, the evolution of the ATWS conditional core damage probability (CCDP) could be obtained using a PSA model. To quantify the CCDP, the average values of the time-dependent failure probabilities must be computed. Finally, the comparison between the CCDP obtained through the application of the classical PSA approach and the new one makes it possible to quantify the impact of time-dependence on the SC of the headers that this new risk-informed ATWS approach can provide.

• Journal of KIISE
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• v.42 no.5
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• pp.573-579
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• 2015

Nationwide supercomputing infrastructures in Korea consist of geographically distributed supercomputing clusters. We developed High-Throughput Computing as a Service(HTCaaS) based on these distributed national supecomputing clusters to facilitate the ease at which scientists can explore large-scale and complex scientific problems. In this paper, we present our mechanism for dynamically managing computing resources and show its effectiveness through a case study of a real scientific application called drug repositioning. Specifically, we show that the resource utilization, accuracy, reliability, and usability can be improved by applying our resource management mechanism. The mechanism is based on the concepts of waiting time and success rate in order to identify valid computing resources. The results show a reduction in the total job completion time and improvement of the overall system throughput.