• Journal of Periodontal and Implant Science
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• v.31 no.4
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• pp.823-832
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• 2001

Periodontal therapy has dealt primarily with attempts at arresting progression of disease, however, more recent techniques have focused on regenerating the periodontal ligament having the capacity to regenerate the periodontium. The effect of chitosan(poly-N-acetyl glucosaminoglycan), a carbohydrate biopolymer extracted from chitin, on periodontal ligament regeneration is of particular interest. The purpose of this study was to evaluate the effect of chitosan on the human periodontal ligament fibroblasts(hPDLFs) in vitro, with special focus on their proliferative properties by M'IT assay, the synthesis of type I collagen by reverse transcription-polymerase chain reaction(RT-PCR) and the activity of alkaline phosphatase(ALP). Fibroblast populations were obtained from individuals with a healthy periodontium and cultured with ${\alpha}MEM$ as the control group. The experimental groups were cultured with chitosan in concentration of 0.01,0.1, 1,2mg/ml. The results are as follows; 1. Chitosan-induced proliferative responses of hPDLFs reached a plateau at the concentration of O.lmg/ml(p<0.05). 2. When hPDLFs were stimulated with 0.lmg/ml chitosan, mRNA expression of type I collagen was up-regulated. 3. When hPDLFs were stimulated with 0.lmg/ml chitosan, ALP activity was significantly up-regulated(p<0.05). In summary, chitosan(0.lmg/ml) enhanced the type I collagen synthesis in the early stage, and afterwards, facilitated differentiation into osteogenic cells. The results of this in vitro experiment suggest that chitosan potentiates the differentiation of osteoprogenitor cells and may facilitate the formation of bone.

• Journal of Veterinary Clinics
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• v.17 no.2
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• pp.411-415
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• 2000

For comparison with conventional barium-gastrointestinal study, modified method using barium and methylcellulose was performed in 16 normal dogs (4-8 kg) divided into two groups. The group 1 received 8ml/kg of 40% w/v barium suspension only, and group 2 had taken 15 ml/kg of 0.6% w/v methylcellulose after administration 4 ml/kg of 40% w/v barium suspension by feeding tube. The barium suspension was prepared mixing full strength-barium suspension with water and methylcellulose solution was produced by blending methylcellulose sterilized water Sequential radiographs, lateral and ventrodorsal projections were obtained at 5 minute, 20 minute,40 minute. 60 minute and every 30 minutes thereafter, until the contrast is seen in the large intestine Inage qual- ity was rated by three veterinary radiologists as " poor" " fair ". "good", or "excellent" We analyzed the relationship between image quality,, transit time. Between two techniques, the modified method with 4ml of 40% w/v barium suspension and 15 ml of 0.6% w/v methylcellulose showed much better image quality ["excellent" result in 7 of the 8 subjects (88%)] and shorter transit time (107 minutes) toe the cecum. In addition, the best image quality was obtained at 20 and 40 minutes after admin-istration of contrast agent. It call be concluded the modified gastrointestinal study using methylcel-lulose after administration of barium suspension was a simple technique and easily improved the image quality and diagnostic accuracy of gstrointestinal disorders in small animal.racy of gstrointestinal disorders in small animal.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.113-120
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• 2024

This study aims to assess the effectiveness of machine learning models in predicting the pass rates of physical therapy students in national exams. Traditional grade prediction methods primarily rely on past academic performance or demographic data. However, this study employed machine learning and deep learning techniques to analyze mock test scores with the goal of improving prediction accuracy. Data from 1,242 students across five Korean universities were collected and preprocessed, followed by analysis using various models. Models, including those generated and fine-tuned with the assistance of ChatGPT-4, were applied to the dataset. The results showed that H2OAutoML (GBM2) performed the best with an accuracy of 98.4%, while TabNet, LightGBM, and RandomForest also demonstrated high performance. This study demonstrates the exceptional effectiveness of H2OAutoML (GBM2) in predicting national exam pass rates and suggests that these AI-assisted models can significantly contribute to medical education and policy.

• Microbiology and Biotechnology Letters
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• v.48 no.3
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• pp.358-365
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• 2020

An organic solvent- and bleach-stable protease-producing strain was isolated from a polluted river water sample and identified as Aeromonas veronii OB3 on the basis of biochemical properties (API 20E) and 16S rRNA sequence analysis. The strain was found to hyper-produce alkaline protease when cultivated on fish waste powder-based medium (HVSP, 4080 U/ml). The biochemical properties and compatibility of OB3 with several detergents and additives were studied. Maximum activity was observed at pH 9.0 and 60℃. The crude protease displayed outstanding stability to the investigated surfactants and oxidants, such as Tween 80, Triton X-100, and H₂O₂, and almost 36% residual activity when incubated with 1% SDS. Remarkably, the enzyme demonstrated considerable compatibility with commercial detergents, retaining more than 100% of its activity with Ariel and Tide (1 h, 40℃). Moreover, washing performance of Tide significantly improved by the supplementation of small amounts of OB3 crude protease. These properties suggest the potential use of this alkaline protease as a bio-additive in the detergent industry and other biotechnological processes such as peptide synthesis.

• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.101-111
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• 2022

It is widely known that axially loaded fiber-reinforced polymer (FRP) confined concrete presents significant and enhanced mechanical properties with reference to the unconfined concrete. Therefore, to predict the mechanical behavior of FRP-confined concrete two quantities-peak strength and ultimate strain are required. Despite the significant advances, the determination of the ultimate strain of FRP-confined concrete is one of the most challenging problems to be resolved. This is often attributed to our persistence in desiring the conventional methods as the sole technique to examine this phenomenon and the complex nature of the ultimate strain of FRP-confined concrete. To bridge the research gap, this study adopted two machine learning (ML) techniques-artificial neural network (ANN) and Gaussian process regression (GPR)-to analyze observations obtained from 627 datasets of FRP-confined concrete circular and non-circular sections under axial loading test. Besides, the techniques are also used to predict the ultimate strain of FRP-confined concrete. Seven parameters namely width/diameter of the specimens, corner radius ratio, the strength of concrete, FRP elastic modulus, FRP thickness, FRP tensile rupture strain, and the axial strain of unconfined concrete-are the input parameters used to predict the ultimate strain of FRP-confined concrete. The results of the current study highlight the merit of using AI techniques in structural engineering applications given their extraordinary ability to comprehend multidimensional phenomena of FRP-confined concrete structures with ease, low computational cost, and high performance over the existing empirical models.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.629-641
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• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.

• Journal of the Korea Computer Graphics Society
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• v.30 no.2
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• pp.1-9
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• 2024

In this paper, we propose a new method to visualize different vector field patterns from density data. We use moving least squares (MLS), which is used in physics-based simulations and geometric processing. However, typical MLS does not take into account the nature of density, as it is interpolated to a higher order through vector-based constraints. In this paper, we design an algorithm that incorporates Monte Carlo-based weights into the MLS to efficiently account for the density characteristics implicit in the input data, allowing the algorithm to represent different forms of white noise. As a result, we experimentally demonstrate detailed vector fields that are difficult to represent using existing techniques such as naive MLS and divergence-constrained MLS.

• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Journal of Microbiology and Biotechnology
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• v.34 no.7
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• pp.1544-1549
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• 2024

This study presents a fluorescent mechanism for two-step amplification by combining two widely used techniques, exponential amplification reaction (EXPAR) and catalytic hairpin assembly (CHA). Pseudomonas aeruginosa (P. aeruginosa) engaged in competition with the complementary DNA in order to attach to the aptamer that had been fixed on the magnetic beads. The unbound complementary strand in the liquid above was utilized as a trigger sequence to initiate the protective-EXPAR (p-EXPAR) process, resulting in the generation of a substantial quantity of short single-stranded DNA (ssDNA). The amplified ssDNA can initiate the second CHA amplification process, resulting in the generation of many double-stranded DNA (dsDNA) products. The CHA reaction was initiated by the target/trigger DNA, resulting in the release of G-quadruplex sequences. These sequences have the ability to bond with the fluorescent amyloid dye thioflavin T (ThT), generating fluorescence signals. The method employed in this study demonstrated a detection limit of 16 CFU/ml and exhibited a strong linear correlation within the concentration range of 50 CFU/ml to 10⁵ CFU/ml. This method of signal amplification has been effectively utilized to create a fluorescent sensing platform without the need for labels, enabling the detection of P. aeruginosa with high sensitivity.