• Proceedings of the Korea Air Pollution Research Association Conference
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• 2010.04a
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• pp.232-236
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• 2010

• 한국지구물리탐사학회:학술대회논문집
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• 2009.10a
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• pp.129-129
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• 2009

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.05a
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• pp.1-1
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• 2003

No Abstract, See Full Text

• Electrical & Electronic Materials
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• v.11 no.2
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• pp.149-152
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• 1998

• 한국초전도학회:학술대회논문집
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• v.15
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• pp.37-37
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• 2005

• The Plant Pathology Journal
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• v.17 no.6
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• pp.376.2-376
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• 2001

• Journal of Gastric Cancer
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• v.17 no.4
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• pp.306-318
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• 2017

Purpose: Guidelines in Western countries recommend retrieving ${\geq}15$ lymph nodes (LNs) during gastric cancer resection. This study sought to determine whether the number of examined lymph nodes (eLNs), a proxy for lymphadenectomy, effects survival in node-negative disease. Materials and Methods: The US National Cancer Database (2003-2011) was reviewed for node-negative gastric adenocarcinoma. Treatment was categorized by neoadjuvant therapy (NAT) vs. initial resection, and further stratified by eLN. Kaplan-Meier and Weibull models were used to analyze overall survival. Results: Of the 1,036 patients who received NAT, 40.5% had ${\leq}10eLN$, and most underwent proximal gastrectomy (67.8%). In multivariate analysis, greater eLN was associated with improved survival (eLN 16-20: HR, 0.71; P=0.039, eLN 21-30: HR, 0.55; P=0.001). Of the 2,795 patients who underwent initial surgery, 42.5% had ${\leq}10eLN$, and the majority underwent proximal gastrectomy (57.2%). In multivariate analysis, greater eLN was associated with improved survival (eLN 11-15: HR, 0.81; P=0.021, eLN 16-20: HR, 0.73; P=0.004, eLN 21-30: HR, 0.62; P<0.001, and eLN >30: HR, 0.58; P<0.001). Conclusions: In the United States, the majority of node-negative gastrectomies include suboptimal eLN. In node-negative gastric cancer, greater LN retrieval appears to have therapeutic and prognostic value, irrespective of initial treatment, suggesting a survival benefit to meticulous lymphadenectomy.

• Archives of Craniofacial Surgery
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• v.21 no.4
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• pp.229-236
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• 2020

Background: Oncologic resection of the scalp confers several obstacles to the reconstructive surgeon dependent upon patient-specific and wound-specific factors. We aim to describe our experiences with various reconstructive methods, and delineate risk factors for coverage failure and complications in the setting of scalp reconstruction. Methods: A retrospective chart review was conducted, examining patients who underwent resection of fungating scalp tumors with subsequent soft-tissue reconstruction from 2003 to 2019. Patient demographics, wound and oncologic characteristics, treatment modalities, and outcomes were recorded and analyzed. Results: A total of 189 patients were appropriate for inclusion, undergoing a range of reconstructive methods from skin grafting to free flaps. Thirty-three patients (17.5%) underwent preoperative radiation. In all, 48 patients (25.4%) suffered wound site complications, 25 (13.2%) underwent reoperation, and 47 (24.9%) suffered from mortality. Preoperative radiation therapy was an independent risk factor for wound complications (odds ratio [OR], 2.85; 95% confidence interval [CI], 1.1-7.3; p=0.028) and reoperations (OR, 4.45; 95% CI, 1.5-13.2; p=0.007). Similarly, the presence of an underlying titanium mesh was an independent predictor of wound complications (OR, 2.49; 95% CI, 1.1-5.6; p=0.029) and reoperations (OR, 3.40; 95% CI, 1.2-9.7; p=0.020). Both immunosuppressed status (OR, 2.88; 95% CI, 1.2-7.1; p=0.021) and preoperative radiation therapy (OR, 3.34; 95% CI, 1.2-9.7; p=0.022) were risk factors for mortality. Conclusion: Both preoperative radiation and the presence of underlying titanium mesh are independent risk factors for wound site complications and increased reoperation rates following oncologic resection and reconstruction of the scalp. Additionally, preoperative radiation, along with an immunosuppressed state, may predict patient mortality following scalp resection and reconstruction.

• ETRI Journal
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• v.30 no.2
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• pp.194-204
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• 2008

The online collection of coarse-grained traffic information, such as the total number of flows, is gaining in importance due to a wide range of applications, such as congestion control and network security. In this paper, we focus on an active queue management scheme called SRED since it estimates the number of active flows and uses the quantity to indicate the level of congestion. However, SRED has several limitations, such as instability in estimating the number of active flows and underestimation of active flows in the presence of non-responsive traffic. We present a Markov model to examine the capability of SRED in estimating the number of flows. We show how the SRED cache hit rate can be used to quantify the number of active flows. We then propose a modified SRED scheme, called hash-based two-level caching (HaTCh), which uses hashing and a two-level caching mechanism to accurately estimate the number of active flows under various workloads. Simulation results indicate that the proposed scheme provides a more accurate estimation of the number of active flows than SRED, stabilizes the estimation with respect to workload fluctuations, and prevents performance degradation by efficiently isolating non-responsive flows.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.163-176
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• 2015

Self-organization of distributed wireless sensor nodes is a critical issue in wireless sensor networks (WSNs), since each sensor node has limited energy, bandwidth, and scalability. These issues prevent sensor nodes from actively collaborating with the other types of sensor nodes deployed in a typical heterogeneous and somewhat hostile environment. The automated self-organization of a WSN becomes more challenging as the number of sensor nodes increases in the network. In this paper, we propose a dynamic self-organized architecture that combines tree topology with a drawn-grid algorithm to automate the self-organization process for WSNs. In order to make our proposed architecture scalable, we assume that all participating active sensor nodes are unaware of their primary locations. In particular, this paper presents two algorithms called active-tree and drawn-grid. The proposed active-tree algorithm uses a tree topology to assign node IDs and define different roles to each participating sensor node. On the other hand, the drawn-grid algorithm divides the sensor nodes into cells with respect to the radio coverage area and the specific roles assigned by the active-tree algorithm. Thus, both proposed algorithms collaborate with each other to automate the self-organizing process for WSNs. The numerical and simulation results demonstrate that the proposed dynamic architecture performs much better than a static architecture in terms of the self-organization of wireless sensor nodes and energy consumption.