• Archives of Plastic Surgery
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• v.46 no.1
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• pp.23-33
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• 2019

Background Venous malformations (VMs) are a common type of vascular malformation. However, their causes and management remain unclear, and few studies specific to VMs of the head and neck have been reported. This study describes our experiences with VMs of the head and neck. Methods This retrospective study included 82 patients who underwent treatment for head and neck VMs, among 222 who visited our vascular anomalies center. Medical records between 2003 and 2016 were reviewed to identify common features in the diagnosis and treatment. The diagnosis of suspected head and neck VMs was based on the results of imaging studies or biopsies, and the VMs were analyzed based on magnetic resonance imaging, computed tomography, and Doppler sonography findings. Results VMs were slightly more common in female patients (59.8%), and 45.1% of patients developed initial symptoms at the age of 10 or younger. Lesions were slightly more common on the right side (47.3%). The main sites involved were the cheek (27.7%) and lip area (25.5%). The muscle layer was commonly involved, in 98.7% of cases. Small lesions less than 5 cm in diameter accounted for 60.8% of cases, and well-defined types were slightly more prevalent at 55.4%. Improvement was observed in 77.1% of treated patients. Conclusions Early and accurate diagnosis and appropriate treatment according to individual symptoms are important for successful treatment of VMs. If treatment is delayed, the lesions can worsen, or recurrence becomes more likely. Therefore, VMs require a multidisciplinary approach for early and accurate diagnosis.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.74-78
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• 2019

As software becomes larger and network technology develops, the management of distributed data becomes more popular. Therefore, it is becoming increasingly important to use blockchain technology that can guarantee the integrity of data in various fields by utilizing existing infrastructure. Blockchain is a distributed computing technology that ensures that servers participating in a network maintain and manage data according to specific agreement algorithms and rules to ensure integrity. As smart contracts are applied, not only passwords but also various services to be applied to the code. In order to reinforce existing research on smart contract applied to the blockchain, we proposed a dynamic conditional rule of smart contract that can formalize rules of smart contract by introducing ontology and SWRL and manage rules dynamically in various situations. In the previous research, there is a module that receives the upper rule in the blockchain network, and the rule layer is formed according to this module. However, for every transaction request, it is a lot of resources to check the top rule in a blockchain network, or to provide it to every blockchain network by a reputable organization every time the rule is updated. To solve this problem, we propose to separate the module responsible for the upper rule into an independent server. Since the module responsible for the above rules is separated into servers, the rules underlying the service may be transformed or attacked in the middleware. Therefore, the security mechanism using TLS and PKI is added as an agent in consideration of the security factor. In this way, the benefits of computing resource management and security can be achieved at the same time.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.163-174
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• 2021

Track-bridge interaction has become an essential part in the design of bridges and rails in terms of modern railways. As a unique ballastless slab track, the longitudinal continuous slab track (LCST) or referred to as the China railway track system Type-II (CRTS II) slab track, demonstrates a complex force mechanism. Therefore, a comprehensive track-bridge interaction study between multi-span simply supported beam bridges and the LCST is presented in this work. In specific, we have developed an integrated finite element model to investigate the overall interaction effects of the LCST-bridge system subjected to the actions of temperature changes, traffic loads, and braking forces. In that place, the deformation patterns of the track and bridge, and the distributions of longitudinal forces and the interfacial shear stress are studied. Our results show that the additional rail stress has been reduced under various loads and the rail's deformation has become much smoother after the transition of the two continuous structural layers of the LCST. However, the influence of the temperature difference of bridges is significant and cannot be ignored as this action can bend the bridge like the traffic load. The uniform temperature change causes the tensile stress of the concrete track structure and further induce cracks in them. Additionally, the influences of the friction coefficient of the sliding layer and the interfacial bond characteristics on the LCST's performance are discussed. The systematic study presented in this work may have some potential impacts on the understanding of the overall mechanical behavior of the LCST-bridge system.

• Journal of Microbiology and Biotechnology
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• v.31 no.5
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• pp.756-763
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• 2021

Agarose is a linear polysaccharide composed of ᴅ-galactose and 3,6-anhydro-ʟ-galactose (AHG). It is a major component of the red algal cell wall and is gaining attention as an abundant marine biomass. However, the inability to ferment AHG is considered an obstacle in the large-scale use of agarose and could be addressed by understanding AHG catabolism in agarolytic microorganisms. Since AHG catabolism was uniquely confirmed in Vibrio sp. EJY3, a gram-negative marine bacterial species, we investigated AHG metabolism in Streptomyces coelicolor A3(2), an agarolytic gram-positive soil bacterium. Based on genomic data, the SCO3486 protein (492 amino acids) and the SCO3480 protein (361 amino acids) of S. coelicolor A3(2) showed identity with H2IFE7.1 (40% identity) encoding AHG dehydrogenase and H2IFX0.1 (42% identity) encoding 3,6-anhydro-ʟ-galactonate cycloisomerase, respectively, which are involved in the initial catabolism of AHG in Vibrio sp. EJY3. Thin layer chromatography and mass spectrometry of the bioconversion products catalyzed by recombinant SCO3486 and SCO3480 proteins, revealed that SCO3486 is an AHG dehydrogenase that oxidizes AHG to 3,6-anhydro-ʟ-galactonate, and SCO3480 is a 3,6-anhydro-ʟ-galactonate cycloisomerase that converts 3,6-anhydro-ʟ-galactonate to 2-keto-3-deoxygalactonate. SCO3486 showed maximum activity at pH 6.0 at 50℃, increased activity in the presence of iron ions, and activity against various aldehyde substrates, which is quite distinct from AHG-specific H2IFE7.1 in Vibrio sp. EJY3. Therefore, the catabolic pathway of AHG seems to be similar in most agar-degrading microorganisms, but the enzymes involved appear to be very diverse.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• ETRI Journal
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• v.44 no.4
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.2
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• pp.61-68
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• 2023

Ti and Ti alloys are used in the automobile and aerospace industries due to their high specific strength and excellent corrosion resistance. However their application is limited due to poor formability at room temperature and high unit cost. In order to overcome these issues, dissimilarly jointed materials, such as cladding materials, are widely investigated to utilize them in each industrial field because of an enhanced plasticity and relatively low cost. Among various dissimilar bonding processes, the rolled cladding process is widely used in Ti alloys, but has a disadvantage of low bonding strength. Although this problem can be solved through post-heat treatment, the mechanical properties at the bonded interface are deteriorated due to residual stress generated during post-heat treatment. Therefore, in this study, the microstructure change and residual stress trends at the interfaces of Ti/Al cladding materials were studied with increasing post-heat treatment temperature. As a result, compared to the as-rolled specimens, no difference in microstructure was observed in the specimens after postheat treatment at 300, 400, and 500℃. However, a new intermetallic compound layer was formed between Ti and Al when post-heat treatment was performed at a temperature of 600℃ or higher. Then, it was also confirmed that compressive residual stress with a large deviation was formed in Ti due to the difference in thermal expansion coefficient and modulus of elasticity between Ti Grade II and Al 1050.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.135-145
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• 2023

Developing high-fidelity Computational Fluid Dynamics (CFD) simulation methods used to evaluate the airwake characteristics along a flight deck of a large ship, the various kind of data such as actual ship measurement and wind tunnel results are required to verify the accuracy of CFD simulation. Inflow velocity profile at the bow, local unsteady flow field data around the flight deck, and highly reliable wind tunnel data which were measured after reviewing Atmospheric Boundary Layer (ABL) simulation and Reynolds Number effects were also used to determine the key parameters such as turbulence model, time resolution and accuracy, grid resolution and type, inflow condition, domain size, simulation length, and so on in STAR CCM⁺. Velocity ratio and turbulent intensity difference between Full-scale CFD and actual ship measurement at the measurement points show less than 2% and 1.7% respectively. And differences in velocity ratio and turbulence intensity between wind tunnel test and small-scale CFD are both less than 2.2%. Based upon this fact, the selected parameters in CFD simulation are highly reliable for a specific wind condition.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.161-167
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• 2010

To realize the U-healthcare system, the mobile patient monitoring system is of the essence. In this monitoring system, a patient's real-time data on biometrics and location must be transferred to predeterminate destination server ceaselessly. As the number of mobile patients increases steadily or mobile patients are moving into some specific area, the load balancing solution to real-time data congestion problem is needed. In this paper, we propose a new mobile patient monitoring system with Torus topology where three layers are connected hierarchically and the intermediate layer takes charge of priority-based load balancing. For the formalized design and verification of proposed system, we describe the overall structure with connectivity among its components and implement major components in pseudo-code by adopting a system specification-based approach. This approach makes the design and verification of our mobile patient monitoring system more flexible and accurate.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.