• Journal of Dental Anesthesia and Pain Medicine
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• v.19 no.1
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• pp.1-10
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• 2019

Periodontal procedures require adequate anesthesia not only to ensure the patient's comfort but also to enhance the operator's performance and minimize chair time. In the maxilla, anesthesia is often achieved using highly traumatic nerve blocks, apart from multiple local infiltrations through the buccal vestibule. In recent years, anterior middle superior alveolar (AMSA) field block has been claimed to be a less traumatic alternative to several of these conventional injections, and it has many other advantages. This critical review of the existing literature aimed to discuss the rationale, mechanism, effectiveness, extent, and duration of AMSA injections for periodontal surgical and non-surgical procedures in the maxilla. It also focused on future prospects, particularly in relation to computer-controlled local anesthetic delivery systems, which aim to achieve the goal of pain-free anesthesia. A literature search of different databases was performed to retrieve relevant articles related to AMSA injections. After analyzing the existing data, it can be concluded that this anesthetic technique may be used as a predictable method of effective palatal anesthesia with adequate duration for different periodontal procedures. It has additional advantages of being less traumatic, requiring lesser amounts of local anesthetics and vasoconstrictors, as well as achieving good hemostasis. However, its effect on the buccal periodontium appears highly unpredictable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1649-1665
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• 2021

In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• Journal of Yeungnam Medical Science
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• v.39 no.2
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• pp.98-107
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• 2022

Background: Stroke patients usually experience damage to multiple functions and a long rehabilitation period. Hence, there is a large volume of patient clinical information. It thus takes a long time for clinicians to identify the patient's information and essential pieces of information may be overlooked. To solve this, we stored the essential clinical information of stroke patients in a blockchain and implemented the blockchain technology using the Java programming language. Methods: We created a mini blockchain to store the medical information of patients using the Java programming language. Results: After generating a unique pair of public/private keys for identity verification, a patient's identity is verified by applying the Elliptic Curve Digital Signature Algorithm based on the generated keys. When the identity verification is complete, new medical data are stored in the transaction list and the generated transaction is verified. When verification is completed normally, the block hash value is derived using the transaction value and the hash value of the previous block. The hash value of the previous block is then stored in the generated block to interconnect the blocks. Conclusion: We demonstrated that blockchain can be used to store and deliver the patient information of stroke patients. It may be difficult to directly implement the code that we developed in the medical field, but it can serve as a starting point for the creation of a blockchain system to be used in the field.

• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.830-841
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• 2022

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.839-849
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• 2023

The development of automation technology to reduce human error by minimizing human intervention is accelerating with artificial intelligence and big data processing technology, even in the nuclear field. Among nuclear power plant operation modes, the startup and shutdown operations are still performed manually and thus have the potential for human error. As part of the development of an autonomous operation system for startup operation, this paper proposes an action coordinating strategy to obtain the optimal actions. The lower level of the system consists of operating blocks that are created by analyzing the operation tasks to achieve local goals through soft actor-critic algorithms. However, when multiple agents try to perform conflicting actions, a method is needed to coordinate them, and for this, an action coordination strategy was developed in this work as the upper level of the system. Three quantification methods were compared and evaluated based on the future plant state predicted by plant parameter prediction models using long short-term memory networks. Results confirmed that the optimal action to satisfy the limiting conditions for operation can be selected by coordinating the action sets. It is expected that this methodology can be generalized through future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2458-2482
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• 2023

Medical image segmentation techniques based on convolution neural networks indulge in feature extraction triggering redundancy of parameters and unsatisfactory target localization, which outcomes in less accurate segmentation results to assist doctors in diagnosis. In this paper, we propose a multi-level semantic-rich encoding-decoding network, which consists of a Pooling-Conv-Former (PCFormer) module and a Cbam-Dilated-Transformer (CDT) module. In the PCFormer module, it is used to tackle the issue of parameter explosion in the conservative transformer and to compensate for the feature loss in the down-sampling process. In the CDT module, the Cbam attention module is adopted to highlight the feature regions by blending the intersection of attention mechanisms implicitly, and the Dilated convolution-Concat (DCC) module is designed as a parallel concatenation of multiple atrous convolution blocks to display the expanded perceptual field explicitly. In addition, MultiHead Attention-DwConv-Transformer (MDTransformer) module is utilized to evidently distinguish the target region from the background region. Extensive experiments on medical image segmentation from Glas, SIIM-ACR, ISIC and LGG demonstrated that our proposed network outperforms existing advanced methods in terms of both objective evaluation and subjective visual performance.

• Genomics & Informatics
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• v.21 no.3
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.89-97
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• 2024

This research conducted a comparative analysis of two communication systems. The first system utilizes a conventional series configuration consisting of a symbol-rate least mean square (LMS) equalizer followed by a timing recovery loop. The second system introduces an innovative approach that integrates a symbol-rate LMS equalizer and a timing recovery component within a single loop, allowing mutual feedback between the two blocks. In this integrated system, the equalizer also provides timing error information, thereby eliminating the requirement for a separate threshold error detector. This study examines the performance curves of both system configurations. The simulation results revealed that the integrated system may offer improved stability in terms of multiple transmission challenges, including phase and frequency offsets and intersymbol interference. Further analysis and discussion highlight the significant insights and implications of the proposed architecture. Overall, the present findings provide an alternative perspective on the joint implementation of equalization and timing recovery in communication systems.

• Clinical Endoscopy
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• v.56 no.2
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• pp.143-154
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• 2023

Advanced malignant hilar biliary obstruction (MHBO) with inaccessible papilla poses a significant challenge to endoscopists, as drainage of multiple liver segments may be warranted. Transpapillary drainage may not be feasible in patients with surgically altered anatomy, duodenal stenosis, prior duodenal self-expanding metal stent, and after initial transpapillary drainage, but require re-intervention for draining separated liver segments. Endoscopic ultrasound-guided biliary drainage (EUS-BD) and percutaneous trans-hepatic biliary drainage are the feasible options in this scenario. The major advantages of EUS-BD over percutaneous trans-hepatic biliary drainage include a reduction in patient discomfort and internal drainage away from the tumor, thus reducing the possibility of tissue or tumor ingrowth. With innovations, EUS-BD is helpful not only for bilateral communicating MHBO but also for non-communicating systems with bridging hilar stents or isolated right intra-hepatic duct drainage by hepatico-duodenostomy. EUS-guided multi-stent drainage with specially designed cannulas and guidewires has become a reality. A combined approach with endoscopic retrograde cholangiopancreatography for re-intervention, interventional radiology, and intraductal tumor ablative therapies has been reported. Stent migration and bile leakage can be minimized with proper stent selection and technique, and stent blocks can be managed with EUS-guided interventions in a majority of cases. Future comparative studies are required to establish the role of EUS-guided interventions in MHBO as rescue or primary therapy.