• The korean journal of orthodontics
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• v.52 no.6
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• pp.387-398
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• 2022

Objective: Controlling the incisal inclination is fundamental in orthodontics. However, the relationship between the inclination prescription and its clinical outcome is not obvious, and the incisal inclination changes generated by different bracket prescriptions were investigated. Methods: Twenty-eight non-extraction dental Class II patients (15 females, 13 males; mean age = 12.9) were retrospectively analyzed. Patients were treated using passive self-ligating fixed appliances with three inclination prescriptions for maxillary incisors (high, standard, low), and two for mandibular incisors (standard, low). Clinical outcomes were compared among different prescriptions, and regression analysis was used to explain the effects of bracket prescriptions and to understand the prescription selection criteria (α = 0.05). Results: For maxillary central incisors, low and high prescriptions were related to linguoversion (p = 0.046) and labioversion (p = 0.005), respectively, while standard prescription maintained the initial dental inclination. Maxillary lateral incisors did not show significant changes. For mandibular incisors, low prescription led to linguoversion (p = 0.005 for central incisors, p = 0.010 for lateral incisors), while standard prescription led to labioversion (p = 0.045 for central incisors, p = 0.005 for lateral incisors). The factors affecting inclination changes were the imposed change and selected prescription, while prescription selection was influenced by the initial dental inclination and initial intercanine distance. Conclusions: The direction of correction of incisal inclination can be controlled by choosing a certain prescription, but the final inclination may show limited consistency with it. The amount of imposed inclination change was the most relevant predictor of the clinical outcome.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Journal of Chest Surgery
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• v.56 no.5
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• pp.346-352
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• 2023

Background: A significant proportion of cardiac surgery intensive care unit (CSICU) patients require long-term ventilation, necessitating tracheostomy placement. The goal of this study was to evaluate the long-term postoperative outcomes and complications associated with percutaneous dilatational tracheostomy (PDT) in CSICU patients. Methods: All patients undergoing PDT after cardiac, thoracic, or vascular operations in the CSICU between January 1, 2013 and January 1, 2021 were identified. They were evaluated for mortality, decannulation time, and complications including bleeding, infection, and need for surgical intervention. Multivariable regression models were used to identify predictors of early decannulation and the complication rate. Results: Ninety-three patients were identified for this study (70 [75.3%] male and 23 [24.7%] female). Furthermore, 18.3% of patients had chronic obstructive pulmonary disease (COPD), 21.5% had history of stroke, 7.5% had end-stage renal disease, 33.3% had diabetes, and 59.1% were current smokers. The mean time from PDT to decannulation was 39 days. Roughly one-fifth (20.4%) of patients were on dual antiplatelet therapy and 81.7% had anticoagulation restarted 8 hours post-tracheostomy. Eight complications were noted, including 5 instances of bleeding requiring packing and 1 case of mediastinitis. There were no significant predictors of decannulation prior to discharge. Only COPD was identified as a negative predictor of decannulation at any point in time (hazard ratio, 0.28; 95% confidence interval, 0.08-0.95; p=0.04). Conclusion: Percutaneous tracheostomy is a safe and viable alternative to surgical tracheostomy in cardiac surgery ICU patients. Patients who undergo PDT have a relatively short duration of tracheostomy and do not have major post-procedural complications.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.1-20
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• 2024

This research aims to study and confirm enabling factors affecting the knowledge transfer and business process of community enterprise groups in Pattani province, Thailand. Key informants were community enterprise entrepreneurs; 30 people were selected purposively with criteria. This study used a mixed-methods approach and conducted semi-structured interviews to collect data. Qualitative data were analyzed using content analysis and classification, while quantitative data were analyzed using descriptive statistics with frequency, percentage, mean, and standard deviation. Moreover, inferential statistics chi-square value, Phi Cramer's V, and multiple regression analysis with the R program for statistical computing were employed to analyze the relationship between the variables, test the research hypothesis, and create forecasting equations. The research results revealed that the overview of enabling factors had a very high relationship (Cramer's V=0.965). Regarding community enterprise, it was found that enabling factors related to the knowledge transfer and business process consisted of four factors: regulations and administrative guidelines, business plan, reinforcement, and brainstorming. Reinforcement was the factor with the highest degree of correlation (Cramer's V=0.873) and predictor of influence on the knowledge transfer and business process (R₂=0.670, p<0.05). This study's findings can lead to the developing of guidelines for promoting community enterprises properly and timely. These guidelines are expected to be used to develop knowledge about business models for community enterprises, which will help to improve their competency and competitiveness.

• Clinical Endoscopy
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• v.57 no.4
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• pp.476-485
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• 2024

Background/Aims: Sedation has become a standard practice for patients undergoing gastrointestinal (GI) endoscopy. However, considering the serious cardiopulmonary adverse events associated with sedatives, it is important to identify patients at high risk. Machine learning can generate reasonable prediction for a wide range of medical conditions. This study aimed to evaluate the risk factors associated with sedation during GI endoscopy and develop a predictive model for hypoxia during endoscopy under sedation. Methods: This prospective observational study enrolled 446 patients who underwent sedative endoscopy at the Korea University Ansan Hospital. Clinical data were used as predictor variables to construct predictive models using the random forest method that is a machine learning algorithm. Results: Seventy-two of the 446 patients (16.1%) experienced life-threatening hypoxia requiring immediate medical intervention. Patients who developed hypoxia had higher body weight, body mass index (BMI), neck circumference, and Mallampati scores. Propofol alone and higher initial and total dose of propofol were significantly associated with hypoxia during sedative endoscopy. Among these variables, high BMI, neck circumference, and Mallampati score were independent risk factors for hypoxia. The area under the receiver operating characteristic curve for the random forest-based predictive model for hypoxia during sedative endoscopy was 0.82 (95% confidence interval, 0.79-0.86) and displayed a moderate discriminatory power. Conclusions: High BMI, neck circumference, and Mallampati score were independently associated with hypoxia during sedative endoscopy. We constructed a model with acceptable performance for predicting hypoxia during sedative endoscopy.

• Tunnel and Underground Space
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• v.15 no.4 s.57
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• pp.264-274
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• 2005

The optimum blasting pattern to excavate a quarry efficiently and economically can be determined based on the minimum production cost which is generally estimated according to rock fragmentation. Therefore it is a critical problem to predict fragment size distribution of blasted rocks over an entire quarry. By comparing various prediction models, it can be ascertained that the result obtained from Kuz-Ram model relatively coincides with that of field measurements. Kuz-Ram model uses the concept of rock factor to signify conditions of rock mass such as block size, rock jointing, strength and others. For the evaluation of total production cost, it is imperative to estimate 3-D spatial distribution of rock factor for the entire quarry. In this study, a sequential indicator simulation technique is adopted for estimation of spatial distribution of rock factor due to its higher reproducibility of spatial variability and distribution models than Kriging methods. Further, this can reduce the uncertainty of predictor using distribution information of sample data The entire quarry is classified into three types of rock mass and optimum blasting pattern is proposed for each type based on 3-D spatial distribution of rock factor. In addition, plane maps of rock factor distribution for each ground levels is provided to estimate production costs for each process and to make a plan for an optimum blasting pattern.

• Journal of Korean Society of Forest Science
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• v.86 no.3
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• pp.365-377
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• 1997

Much of the Cumberland Plateau of Tennessee, USA is in mixed hardwoods for which there are no applicable growth and yield predictors. Use of site index as a variable in growth and yield prediction models is limited in most stands because their history is not known and many may not be even-aged. Landtypes may offer an alternative to site index for these mixed stands because they were designed to include land of about equal productivity. To determine vegetation by landtype, dependency between landtype and detailed forest type was tested with Chi-square. Differences in productivity among landtypes were tested by employing regression analyses and analysis of variance(ANOVA). Basal area growth was fitted to the nonlinear models developed by Moser and Hall(1969). Basal area growth and volume growth were also predicted as a function of initial total basal area and initial volume with linear regression by landtype and by landtype class. Differences in basal area growth and volume growth by landtype were tested with ANOVA. Dependency between site class and landtype was tested with Chi-square. Vegetation types seem to be related to landtypes in the study area although the validity of the test is questionable because of a high proportion of sparsely occupied cells. No statistically significant differences in productivity among landtypes were found in this study.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.429-439
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• 2009

When Catastrophic extreme flood occurs due to dam break, the response time for flood warning is much shorter than for natural floods. Numerical models can be powerful tools to predict behaviors in flood wave propagation and to provide the information about the flooded area, wave front arrival time and water depth and so on. But flood wave propagation due to dam break can be a process of difficult mathematical characterization since the flood wave includes discontinuous flow and dry bed propagation. Nevertheless, a lot of numerical models using finite volume method have been recently developed to simulate flood inundation due to dam break. As Finite volume methods are based on the integral form of the conservation equations, finite volume model can easily capture discontinuous flows and shock wave. In this study the numerical model using Riemann approximate solvers and finite volume method applied to the conservative form for two-dimensional shallow water equation was developed. The MUSCL scheme with surface gradient method for reconstruction of conservation variables in continuity and momentum equations is used in the predictor-corrector procedure and the scheme is second order accurate both in space and time. The developed finite volume model is applied to 2D partial dam break flows and dam break flows with triangular bump and validated by comparing numerical solution with laboratory measurements data and other researcher's data.

• Korean Journal of Radiology
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• v.23 no.6
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• pp.664-673
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• 2022

Objective: To evaluate the performance of baseline clinical characteristics and pretherapeutic histogram parameters derived from T2 mapping of the extraocular muscles (EOMs) in the prediction of treatment response to intravenous glucocorticoid (IVGC) therapy for active and moderate-to-severe thyroid-associated ophthalmopathy (TAO) and to investigate the effect of fat-suppression (FS) in T2 mapping in this prediction. Materials and Methods: A total of 79 patients clinically diagnosed with active, moderate-to-severe TAO (47 female, 32 male; mean age ± standard deviation, 46.1 ± 10 years), including 43 patients with a total of 86 orbits in the responsive group and 36 patients with a total of 72 orbits in the unresponsive group, were enrolled. Baseline clinical characteristics and pretherapeutic histogram parameters derived from T2 mapping with FS (i.e., FS T2 mapping) or without FS (i.e., conventional T2 mapping) of EOMs were compared between the two groups. Independent predictors of treatment response to IVGC were identified using multivariable analysis. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the prediction models. Differences between the models were examined using the DeLong test. Results: Compared to the unresponsive group, the responsive group had a shorter disease duration, lower kurtosis (FS-kurtosis), lower standard deviation, larger 75th, 90th, and 95th (FS-95th) T2 relaxation times in FS mapping and lower kurtosis in conventional T2 mapping. Multivariable analysis revealed that disease duration, FS-95th percentile, and FS-kurtosis were independent predictors of treatment response. The combined model, integrating all identified predictors, had an optimized area under the ROC curve of 0.797, 88.4% sensitivity, and 62.5% specificity, which were significantly superior to those of the imaging model (p = 0.013). Conclusion: An integrated combination of disease duration, FS-95th percentile, and FS-kurtosis was a potential predictor of treatment response to IVGC in patients with active and moderate-to-severe TAO. FS T2 mapping was superior to conventional T2 mapping in terms of prediction.