• Journal of Dental Anesthesia and Pain Medicine
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• v.24 no.5
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• pp.341-350
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• 2024

Background: Achieving successful pain control and adequate anesthesia through an inferior alveolar nerve block for endodontic treatment in cases with symptomatic irreversible pulpitis (SIP) is difficult, especially in mandibular molars. This study was designed to compare the effect of oral medication with ibuprofen and soft laser therapy on inferior alveolar nerve block during endodontic treatment. Methods: The trial comprised 180 patients (45 each group) with SIP. Four groups of patients were created: group 1 received 400 mg of ibuprofen; group 2 received soft laser irradiation; group 3 received a combination of soft laser and ibuprofen 400 mg; and group 4 received a placebo 1 h prior to local anesthesia. Patients recorded their pain scores on the Heft-Parker visual analog scale (VAS) before the start of intervention, 15 min after anesthesia, during access cavity preparation, and ultimately during root canal instrumentation. Each patient also rated their level of discomfort on a VAS. Every stage with no or minimal discomfort was deemed successful. The chi-square, Kruskal-Wallis, and one-way analysis of variance tests were used to evaluate the data. Results: The best success rate was achieved for soft laser ibuprofen combination, ibuprofen and soft laser groups reported similar success results, and control group recorded the least pain scores. The mean pain scores were lowest for group 3 and highest for group 4 (P < 0.001). Ibuprofen and soft laser combination was significantly better than control group (P < 0.001). There was no significant difference between ibuprofen and laser groups (P = 0.24). Conclusions: For teeth with irreversible pulpitis, preoperative ibuprofen treatment combined with soft laser irradiation greatly improved the success rates of inferior alveolar nerve block anesthesia.

• Journal of Technologic Dentistry
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• v.46 no.3
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• pp.73-83
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• 2024

Purpose: This study aimed to investigate whether the accuracy and internal porosity of removable partial denture frameworks differ depending on the build direction in the selective laser melting method. Methods: A partially edentulous maxillary study model was scanned, and the anterior-posterior palatal bar was then digitally designed. The angles formed between the z-axis and the path of the insertion and removal were divided into five groups: -60°, -30°, 0°, 30°, and 60°. For each group, three removable partial denture metal frameworks were fabricated and used as specimens. The inner surface of each sample was scanned and superimposed on the design file to obtain the root mean square (RMS) value, and the average RMS value of each group was measured. One sample was randomly selected from each group, and the equivalent diameter and sphericity of the pores were analyzed using industrial X-ray three-dimensional computed tomography. To compare statistical differences between groups, the Kruskal-Wallis test of SPSS Statistics ver. 27.0 (IBM) was used (α=0.05). Results: The average RMS values of the whole inner surface accuracy of the specimens were in the order of -60°<0°<-30°<30°<60° (p<0.05). The equivalent diameter and sphericity of internal pores were significantly different among groups (p<0.001). Conclusion: The build orientation of the selective laser melting method influences the accuracy and internal porosity of removable partial denture frameworks.

• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.643-656
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• 2024

The prediction of the moment rotation behavior of semi-rigid connections has been the subject of extensive research. However, to improve the accuracy of these predictions, there is a growing interest in employing machine learning algorithms. This paper investigates the effectiveness of using Multi-gene genetic programming (MGGP) to predict the moment-rotation behavior of flush-end plate connections compared to that of artificial neural networks (ANN) and previous studies. It aims to automate the process of determining the most suitable equations to accurately describe the behavior of these types of connections. Experimental data was used to train ANN and MGGP. The performance of the models was assessed by comparing the values of coefficient of determination (R²), maximum absolute error (MAE), and root-mean-square error (RMSE). The results showed that MGGP produced more accurate, reliable, and general predictions compared to ANN and previous studies with an R² exceeding 0.99, an RMSE of 6.97, and an MAE of 38.68, highlighting its advantages over other models. The use of MGGP can lead to better modeling and more precise predictions in structural design. Additionally, an experimentally-based regression analysis was conducted to obtain the rotational capacity of FECs. A new equation was proposed and compared to previous ones, showing significant improvement in accuracy with an R² score of 0.738, an RMSE of 0.014, and an MAE of 0.024.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.1-9
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• 2024

In this paper, we propose a comparative analysis to evaluate the impact of activation functions and attention mechanisms on the performance of time-series models for Mars meteorological data. Mars meteorological data are nonlinear and irregular due to low atmospheric density, rapid temperature variations, and complex terrain. We use long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) architectures to evaluate the effectiveness of different activation functions and attention mechanisms. The activation functions tested include rectified linear unit (ReLU), leaky ReLU, exponential linear unit (ELU), Gaussian error linear unit (GELU), Swish, and scaled ELU (SELU), and model performance was measured using mean absolute error (MAE) and root mean square error (RMSE) metrics. Our results show that the integration of attentional mechanisms improves both MAE and RMSE, with Swish and ReLU achieving the best performance for minimum temperature prediction. Conversely, GELU and ELU were less effective for pressure prediction. These results highlight the critical role of selecting appropriate activation functions and attention mechanisms in improving model accuracy for complex time-series forecasting.

• Ocean and Polar Research
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• v.46 no.1
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• pp.17-30
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• 2024

A real-time numerical prediction system was developed to predict sea surface temperature (SST) in Cheonsu Bay to minimize damages caused by marine heatwaves. This system assimilated observation data using an ensemble Kalman filter and produced 7-day forecasts. Bias in the temperature forecasts were corrected based on observed data, and the bias-corrected predictions were evaluated against observations. Using this real-time numerical prediction system, daily SSTs were predicted in real-time for 7 days from July to August 2021. The forecasted SSTs from the numerical model were adjusted using observational data for bias correction. To assess the accuracy of the numerical prediction system, real-time hourly surface temperature observations as well as temperature and salinity profiles observed along two meridional sections within Cheonsu Bay were compared with the numerical model results. The root mean square error (RMSE) of the forecasted temperatures was 0.58℃, reducing to 0.36℃ after bias-correction. This emphasizes the crucial role of bias correction using observational data. Sensitivity experiments revealed the importance of accurate input of freshwater influx information such as discharge time, discharge volume, freshwater temperature in predicting real-time temperatures in coastal ocean heavily influenced by freshwater discharge. This study demonstrated that assimilating observational data into coastal ocean numerical models and correcting biases in forecasted SSTs can improve the accuracy of temperature prediction. The prediction methods used in this study can be applied to temperature predictions in other coastal areas.

• Anatomy and Cell Biology
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• v.55 no.1
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• pp.72-78
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• 2022

The purpose of this study was to analyze the accuracy of intra oral scanner (IOS) to confirm the applicability of IOS for the recording and analysis of tooth morphology in forensics. The less damaged mandible specimen with many teeth remaining was scanned three times using three types of intraoral scanners (CS3600, i500, and Trios3). For quantitative comparisons of the scanned images produced by these intraoral scanners, root mean square (RMS) values were computed using a three-dimensional analysis program and a one-way ANOVA was conducted with Tukey HSD (honestly significant difference) as a post-hoc analysis (α=0.05). The repeatability of the full scan data was highest with the i500 (0.14±0.03 mm), and the post-hoc analysis confirmed significant differences between the CS3600 and the i500 outcomes (P-value=0.003). The repeatability of the partial scan data for the teeth in the mandible was highest with the i500 (0.08±0.02 mm), and the post-hoc analysis confirmed significant differences between the CS3600 and the i500 (P-value=0.016). The precision of the full scan data was highest with the i500 (0.16±0.01 mm) but the differences were not statistically significant (P-value=0.091). Meanwhile, the precision of the partial scan data for the teeth in the mandible was highest with the Trios3 (0.22±0.02 mm), but the differences were not statistically significant (P-value=0.762). Considering that the scanning of other areas of the oral cavity in addition to the teeth is important in forensic odontology, the i500 scanner appears to be the most appropriate intraoral scanner for human identification. However, as the scope of oral scanning is generally limited to teeth in the practice of dentistry, additional discussions of how to apply the IOS in forensic odontology are needed. Ultimately, the results here can contribute to the overall discussion of the forensic applicability dental data produced by intraoral scanners.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.429-457
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• 2024

Purpose: This study aims to provide useful information to real estate investors by developing a profit determination model using artificial intelligence. The model analyzes the real estate markets of six selected cities from multiple perspectives, incorporating characteristics of the real estate market, economic indicators, and policies to determine potential profits. Methods: Data on real estate markets, economic indicators, and policies for five cities were collected and cleaned. The data was then normalized and split into training and testing sets. An AI model was developed using machine learning algorithms and trained with this data. The model was applied to the six cities, and its accuracy was evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared by comparing predicted profits to actual outcomes. Results: The profit determination model was successfully applied to the real estate markets of six cities, showing high accuracy and predictability in profit forecasts. The study provided valuable insights for real estate investors, demonstrating the model's utility for informed investment decisions. Conclusion: The study identified areas for future improvement, suggesting the integration of diverse data sources and advanced machine learning techniques to enhance predictive capabilities.

• Journal of the Korean GEO-environmental Society
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• v.25 no.11
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• pp.5-12
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• 2024

In this study, the liquefaction potential index was assessed using actual borehole data and seismic waves, and a predictive model was developed based on machine learning methods. A total of 10 features were selected including factors reflecting the characteristics of the seismic waves. To identify candidate methods, a preliminary test was conducted using commonly used machine learning methods for regression, followed by Bayesian optimization to optimize the hyperparameters for these candidate methods. Among artificial neural networks, Gaussian process regression, and random forest, it was found that the random forest effectively predicted the liquefaction potential index, as indicated by a low root mean square error, a high coefficient of determination, and considerations regarding overfitting. However, it was noted that the model tends to underestimate the liquefaction potential index when the index was 5 or higher.

• Progress in Medical Physics
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• v.25 no.2
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• pp.72-78
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• 2014

The aim of the study is to test a hypothesis that quasi-breath-hold (QBH) biofeedback improves the residual respiratory motion management in gated 3D thoracic MR imaging, reducing respiratory motion artifacts with insignificant acquisition time alteration. To test the hypothesis five healthy human subjects underwent two gated MR imaging studies based on a T2 weighted SPACE MR pulse sequence using a respiratory navigator of a 3T Siemens MRI: one under free breathing and the other under QBH biofeedback breathing. The QBH biofeedback system utilized the external marker position on the abdomen obtained with an RPM system (Real-time Position Management, Varian) to audio-visually guide a human subject for 2s breath-hold at 90% exhalation position in each respiratory cycle. The improvement in the upper liver breath-hold motion reproducibility within the gating window using the QBH biofeedback system has been assessed for a group of volunteers. We assessed the residual respiratory motion management within the gating window and respiratory motion artifacts in 3D thoracic MRI both with/without QBH biofeedback. In addition, the RMSE (root mean square error) of abdominal displacement has been investigated. The QBH biofeedback reduced the residual upper liver motion within the gating window during MR acquisitions (~6 minutes) compared to that for free breathing, resulting in the reduction of respiratory motion artifacts in lung and liver of gated 3D thoracic MR images. The abdominal motion reduction in the gated window was consistent with the residual motion reduction of the diaphragm with QBH biofeedback. Consequently, average RMSE (root mean square error) of abdominal displacement obtained from the RPM has been also reduced from 2.0 mm of free breathing to 0.7 mm of QBH biofeedback breathing over the entire cycle (67% reduction, p-value=0.02) and from 1.7 mm of free breathing to 0.7 mm of QBH biofeedback breathing in the gated window (58% reduction, p-value=0.14). The average baseline drift obtained using a linear fit was reduced from 5.5 mm/min with free breathing to 0.6 mm/min (89% reduction, p-value=0.017) with QBH biofeedback. The study demonstrated that the QBH biofeedback improved the upper liver breath-hold motion reproducibility during the gated 3D thoracic MR imaging. This system can provide clinically applicable motion management of the internal anatomy for gated medical imaging as well as gated radiotherapy.

• Restorative Dentistry and Endodontics
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• v.33 no.1
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• pp.1-8
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• 2008

The purpose of this study was to evaluate the effect of a new resin monomer, filler size and polishing technique on the surface roughness of composite resin restorations using confocal laser scanning microscopy. By adding new methoxylated Bis-GMA (Bis-M-GMA, 2,2-bis[4-(2-methoxy-3-methacryloyloxy propoxy) phenyl] propane) having low viscosity, the content of TEGDMA might be decreased. Three experimental composite resins were made: EX1 (Bis-M-GMA/TEGDMA = 95/5 wt%, 40 nm nanofillers); EX2 (Bis-M-GMA/TEGDMA = 95/5 wt%, 20 nm nanofillers); EX3 (Bis-GMA/TEGDMA = 70/30 wt%, 40 nm nanofillers). Filtek Z250 was used as a reference. Nine specimens (6 mm in diameter and 2 mm in thickness) for each experimental composite resin and Filtek Z250 were fabricated in a teflon mold and assigned to three groups. In Mylar strip group, specimens were left undisturbed. In Sof-lex group, specimens were ground with #1000 SiC paper and polished with Sof-lex discs. In DiaPolisher group, specimens were ground with #1000 SiC paper and polished with DiaPolisher polishing points. The Ra (Average roughness), Rq (Root mean square roughness), Rv (Valley roughness), Rp (Peak roughness), Rc (2D roughness) and Sc (3D roughness) values were determined using confocal laser scanning microscopy. The data were statistically analyzed by Two-way ANOVA and Tukey multiple comparisons test (p = 0.05). The type of composite resin and polishing technique significantly affected the surface roughness of the composite resin restorations (p < 0.001). EX3 showed the smoothest surface compared to the other composite resins (p < 0.05). Mylar strip resulted in smoother surface than other polishing techniques (p < 0.05). Bis-M-GMA. a new resin monomer having low viscosity, might reduce the amount of diluent, but showed adverse effect on the surface roughness of composite resin restorations.