• Journal of Periodontal and Implant Science
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• v.49 no.2
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• pp.105-113
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• 2019

Purpose: An unresolved inflammatory state contributes to the pathogenesis of periodontal disease and metabolic syndrome (MetS). Therefore, the purpose of this study was to evaluate the role of lipoxin A4 (LXA4), a proresolving lipid mediator, in the association between periodontal disease and MetS. Methods: Sixty-seven patients with MetS and 65 patients without MetS were included in the study. Sociodemographic information was obtained via a questionnaire, and detailed medical diagnoses were made. Periodontal parameters (plaque index [PI], gingival index [GI], probing pocket depth [PD], and clinical attachment level [CAL]) and metabolic parameters were measured, and serum LXA4 levels were determined. The associations among MetS, periodontal parameters, and serum LX levels were evaluated by adjusted multivariate linear regression analyses. Results: Patients with MetS were older and had a higher body mass index than patients without MetS. Periodontal parameters (PI, GI, PD, and CAL) were higher in patients with MetS than in those without MetS. Serum LXA4 levels were higher in patients without MetS. Multivariate linear regression analysis indicated a positive association between MetS and periodontal parameters (PD and CAL). Negative associations were established between MetS and LXA4 levels, and between LXA4 and periodontal parameters (PI, PD, and CAL). Conclusions: The presence of higher values of periodontal parameters in patients with MetS and the negative relationship of LXA4 with MetS and periodontal disease may support the protective role of proresolving lipid mediators in the association between periodontal disease and MetS.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Restorative Dentistry and Endodontics
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• v.38 no.3
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• pp.154-159
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• 2013

Objectives: The aim of this study was to compare the cleaning efficacy of different final irrigation regimens in canal and isthmus of mandibular molars, and to evaluate the influence of related variables on cleaning efficacy of the irrigation systems. Materials and Methods: Mesial root canals from 60 mandibular molars were prepared and divided into 4 experimental groups according to the final irrigation technique: Group C, syringe irrigation; Group U, ultrasonics activation; Group SC, VPro StreamClean irrigation; Group EV, EndoVac irrigation. Cross-sections at 1, 3 and 5 mm levels from the apex were examined to calculate remaining debris area in the canal and isthmus spaces. Statistical analysis was completed by using Kruskal-Wallis test and Mann-Whitney U test for comparison among groups, and multivariate linear analysis to identify the significant variables (regular replenishment of irrigant, vapor lock management, and ultrasonic activation of irrigant) affecting the cleaning efficacy of the experimental groups. Results: Group SC and EV showed significantly higher canal cleanliness values than group C and U at 1 mm level (p < 0.05), and higher isthmus cleanliness values than group U at 3 mm and all levels of group C (p < 0.05). Multivariate linear regression analysis demonstrated that all variables had independent positive correlation at 1 mm level of canal and at all levels of isthmus with statistical significances. Conclusions: Both VPro StreamClean and EndoVac system showed favorable result as final irrigation regimens for cleaning debris in the complicated root canal system having curved canal and/or isthmus. The debridement of the isthmi significantly depends on the variables rather than the canals.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.411-433
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• 2004

The shrinkage preliminary test ridge regression estimators (SPTRRE) based on Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests for estimating the regression parameters of the multiple linear regression model with multivariate Student's t error distribution are considered in this paper. The quadratic biases and risks of the proposed estimators are compared under both null and alternative hypotheses. It is observed that there is conflict among the three estimators with respect to their risks because of certain inequalities that exist among the test statistics. In the neighborhood of the restriction, the SPTRRE based on LM test has the smallest risk followed by the estimators based on LR and W tests. However, the SPTRRE based on W test performs the best followed by the LR and LM based estimators when the parameters move away from the subspace of the restrictions. Some tables for the maximum and minimum guaranteed efficiency of the proposed estimators have been given, which allow us to determine the optimum level of significance corresponding to the optimum estimator among proposed estimators. It is evident that in the choice of the smallest significance level to yield the best estimator the SPTRRE based on Wald test dominates the other two estimators.

• The Journal of Economics, Marketing and Management
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• v.4 no.3
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• pp.1-6
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• 2016

The purpose of the present study is to investigate the relationship between Managerial overconfidence and vehicle and parts manufacturing firm value of the listed companies on the Tehran Stock Exchange (TSE). The population includes 25 firms selected through systematic sampling. The data is collected from the audited financial statements of the firms provided by TSE's website from 2010 to 2015. In this study the variables, Overconfidence based on earning per share (OEPS), Overconfidence based on capital cost (OCC) has been used to investigate Managerial overconfidence. The results of multiple linear regression analysis show that there is a significant relationship between Overconfidence based on earning per share (OEPS) and firm value. In addition, there is a significant relationship between Overconfidence based on capital cost (OCC) The present research examined the relationship between Managerial overconfidence and vehicle and parts manufacturing firm value of the listed in Tehran Stock Exchange. The results of multivariate regression accepted two the hypotheses of the research. There is a significant relationship between Managerial overconfidence and vehicle and parts manufacturing firm value.

• Nutrition Research and Practice
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• v.15 no.4
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• pp.468-478
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• 2021

BACKGROUND/OBJECTIVES: This study aimed to analyze the association between dietary omega-3 fatty acid intake and depression in postmenopausal women using data from the Korea National Health and Nutrition Examination Survey (KNHANES) VI. SUBJECTS/METHODS: The KNHANES is a cross-sectional nationwide health and nutrition survey. Dietary data, including omega-3 fatty acids, were assessed using the 24-h recall method. Depression was evaluated using a survey questionnaire. The association between dietary omega-3 fatty acids and depression was evaluated using multivariate logistic regression analysis. Depression, according to the dietary omega-3 fatty acid intake, was expressed as the odds ratio (OR) with a 95% confidence interval (CI). A total of 4,150 postmenopausal women were included in the analysis. RESULTS: In the fully-adjusted model, the group with the highest dietary omega-3 fatty acid intake significantly showed lower prevalence of depression than the group with the lowest intake (OR, 0.52; 95% CI, 0.33-0.83); a significant linear trend was detected (P for trend = 0.04). According to the dose-response analysis using cubic restricted spline regression, this association was linear and monotonic (P for non-linearity = 0.32). CONCLUSIONS: In this study, the dietary omega-3 fatty acid intake in postmenopausal women was inversely proportional to depression in a dose-response manner. Large cohort studies are needed to verify the causality between omega-3 fatty acids and depression in Korean postmenopausal women.

• Structural Engineering and Mechanics
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• v.31 no.1
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1329-1344
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• 2015

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

• Journal of Korean society of Dental Hygiene
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• v.10 no.4
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• pp.671-681
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• 2010

Objectives : This study was to evaluate the association between perceived oral health and perceived oral symptoms among adults in Daegu. Methods : All 437 subjects aged 18 or more selected convenience sampling were surveyed cross-sectionally via the self-administrated questionnaire. The questionnaire was measured perceived oral symptoms and perceived oral health, and also obtained socio-demographic characteristics, oral health behaviors. To assess the crude associations, bivariate analysis were applied. For the adjusted association between perceived oral health and perceived oral symptoms, multivariate linear regression multiple regression analysis was conducted. Results : 33.2% of the adults rated their perceived oral health was good, and 30.9% as poor. Older age, low education, had peridontal disease was negatively perceived their oral health(p<0.05). As oral symptoms were more frequently perceived, the perceived oral health were negative. Among the factors of perceived oral symptoms, trouble biting/chewing, poor periodontal status, trouble of appearance of teeth were positively associated with the perceived oral health after adjusting for socio-demographic characteristics, oral health behaviors in the regression model. Age, education, income, recent dental treatment, and all perceived oral symptoms showed the highest impact of association with perceived oral health in the baseline-category logit model. Conclusions : Perceived oral health are significantly associated with perceived oral symptoms among adults in Daegu. The findings of this study will be helpful to design plans of oral health promotion in welfare institutions to increase the oral health related quality of life among the adults.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.