• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.3
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• pp.598-625
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• 2014

To support the procedure for determining an optimal liquefaction cycle for FLNG FEED, an ontological modeling method which can automatically generate various alternative liquefaction cycles were carried out in this paper. General rules in combining equipment are extracted from existing onshore liquefaction cycles like C3MR and DMR cycle. A generic relational model which represents whole relations of the plant elements has all these rules, and it is expressed by using the system entity structure (SES), an ontological framework that hierarchically represents the elements of a system and their relationships. By using a process called pruning which reduces the SES to a candidate, various alternative relational models of the liquefaction cycles can be automatically generated. These alternatives were provided by XML-based formats, and they can be used for choosing an optimal liquefaction cycle on the basis of the assessments such as process simulation and reliability analysis.

• Journal of Educational Research in Mathematics
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• v.27 no.4
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• pp.791-810
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• 2017

This study explored the possibility of using visual models in teaching proportional reasoning based on the review of previous studies. Many studies on proportional reasoning emphasize that students tend to simply apply formal procedures without understanding the meaning behind them and that using visual models may be an alternative to help students develop informal strategies and proportional reasoning. Given these, we re-constructed and implemented the unit of a textbook to teach sixth graders proportional reasoning with ratio table, double number line, and double tape diagram. The results of this study showed that such visual models helped students understand the meaning of proportion, explore the properties of proportion, and solve proportional problems. However, several difficulties that students experienced in using the visual models led us to suggest cautionary notes when to teach proportional reasoning with visual models. As such, this study is expected to provide empirical information for textbook developers and teachers who teach proportional reasoning with visual models.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.2
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• pp.87-94
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• 2014

In solid mechanics, the peridynamics theory has provided a suitable framework for material failure and damage propagation simulation. Peridynamics is computationally expensive since it is required to solve enormous nonlocal interactions based upon integro-differential equations. Thus, multiscale coupling methods with other local models are of interest for efficient and accurate implementations of peridynamics. In this study, peridynamic models are restricted to regions where discontinuities or stress concentrations are present. In the domains characterized by smooth displacements, classical local models can be employed. We introduce a recently developed blending scheme to concurrently couple bond-based peridynamic models and the Navier equation of classical elasticity. We demonstrate numerically that the proposed blended model is suitable for point loads and static fracture, suggesting an alternative framework for cases where peridynamic models are too expensive, while classical local models are not accurate enough.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Journal of Periodontal and Implant Science
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• v.52 no.6
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• pp.479-495
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• 2022

Purpose: Rodent models have emerged as an alternative to established larger animal models for peri-implantitis research. However, the construct validity of rodent models is controversial due to a lack of consensus regarding their histological, morphological, and biochemical characteristics. This systematic review sought to validate rodent models by characterizing their morphological changes, particularly marginal bone loss (MBL), a hallmark of peri-implantitis. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A literature search was performed electronically using MEDLINE (PubMed), and Embase, identifying pre-clinical studies reporting MBL after experimental peri-implantitis induction in rodents. Each study's risk of bias was assessed using the Systematic Review Center for Laboratory animal Experimentation (SYRCLE) risk of bias tool. A meta-analysis was performed for the difference in MBL, comparing healthy implants to those with experimental peri-implantitis. Results: Of the 1,014 unique records retrieved, 23 studies that met the eligibility criteria were included. Peri-implantitis was induced using 4 methods: ligatures, lipopolysaccharide, microbial infection, and titanium particles. Studies presented high to unclear risks of bias. During the osseointegration phase, 11.6% and 6.4%-11.3% of implants inserted in mice and rats, respectively, had failed to osseointegrate. Twelve studies were included in the meta-analysis of the linear MBL measured using micro-computed tomography. Following experimental peri-implantitis, the MBL was estimated to be 0.25 mm (95% confidence interval [CI], 0.14-0.36 mm) in mice and 0.26 mm (95% CI, 0.19-0.34 mm) in rats. The resulting peri-implant MBL was circumferential, consisting of supra- and infrabony components. Conclusions: Experimental peri-implantitis in rodent models results in circumferential MBL, with morphology consistent with the clinical presentation of peri-implantitis. While rodent models are promising, there is still a need to further characterize their healing potentials, standardize experiment protocols, and improve the reporting of results and methodology.

• Journal of muscle and joint health
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• v.6 no.2
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• pp.253-277
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• 1999

Patients with chronic disease have various treatment patterns because it shows a progressive degenerative feature. Especially various physical and emotional problems of the rheumatoid arthritis patients leave them shopping around various types of treatment. According to previous studies, over 70% of patients with arthritis experienced the traditional oriental medicine or folk remedies simultaneously with medical treatment within one year after the onset of disease. The purposes of this study are 1) to compare the patterns of treatment-seeking behaviors between Korean arthritis patients and Americans ; and 2) test two models of treatment-seeking behaviors by path analysis, one for early treatment-seeking behavior model(ETBM) and the other is chronic treatment-seeking behavior model (CTBM) in Korean sample. The interview survey was performed to 133 RA patients with structured questionnaire at out-patient clinic or public health center. Patients characteristics such as age, duration of disease were similar in two countries except higher educational background in Americans. There were no patients using only alternative therapies or no medical treatment in the US. Most of the American patients have chosen both medical treatment and alternative therapy, while the Koreans less than American. In Korea, combined treatment group usually consists of the people who are younger, more educated and higher economic status than the characters of other groups in early or chronic stages. In early stage, they tend to have strong belief of curing from the disease, satisfy the relationship with their physicians and comply with direction of the medical professional. The paths of two models were explained by 70% in ETBM and 33% in CTBM. When the models were modified, almost all paths of the CTBM were the same as the previous one, but direct determinant factor was changed from the relationship with physicians to the lay referral system in chronic model. These two models' explanation powers became 94% and 88%, respectively. The attitude or perception of disease, lay referral system and the relationship with medical personnel are the main determinants of treatment-seeking behaviors.

• Earthquakes and Structures
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• v.4 no.5
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• pp.451-470
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• 2013

It generally accepted that most building structures shall exhibit a nonlinear response when subjected to medium-high intensity earthquakes. It is currently known, however, that this phenomenon is not properly modelled in the majority of cases, especially at the design stage, where only simple linear methods have effectively been used. Recently, as a result of the exponential progress of computational tools, nonlinear modelling and analysis have gradually been brought to a more promising level. A wide range of modelling alternatives developed over the years is hence at the designer's disposal for the seismic design and assessment of engineering structures. The objective of the study presented herein is to test some of these models in an existing structure, and observe their performance in nonlinear static and dynamic analyses. This evaluation is done by the use of two of a known range of advanced computer programs: SAP2000 and SeismoStruct. The different models will focus on the element flexural mechanism with both lumped and distributed plasticity element models. In order to appraise the reliability and feasibility of each alternative, the programs capabilities and the amount of labour and time required for modelling and performing the analyses are also discussed. The results obtained show the difficulties that may be met, not only in performing nonlinear analyses, but also on their dependency on both the chosen nonlinear structural models and the adopted computer programs. It is then suggested that these procedures should only be used by experienced designers, provided that they are aware of these difficulties and with a critical stance towards the result of the analyses.

• Computers and Concrete
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• v.33 no.2
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

• Steel and Composite Structures
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• v.52 no.3
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

• Journal of the Korean housing association
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• v.20 no.1
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• pp.45-58
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• 2009

This research starts from the questioning of current housing development on the hilly site in Korea. It aims to investigate various design techniques of low-rise housing as an alternative housing plan on hilly sites. Several generic solutions of the combination of building type and road pattern are tested for a simulation process, and evaluated in terms of crucial design issues; development density, parking space and open space. As a result, four reasonable models are selected for making full use of geographical features of hilly site, two models each land slope of $18^{\circ}$ and $26^{\circ}$. Several design techniques for each model are also suggested, in ensuring the development feasibility by considering land slope, vehicular access and parking, common open space, and community facilities.