Describing Adequacy of Cure with Maximum Hardness Ratios and Non-linear Regression

10.2341/07-92 ◽  
2008 ◽  
Vol 33 (3) ◽  
pp. 312-320 ◽  
Author(s):  
M. Bouschlicher ◽  
K. Berning ◽  
F. Qian
Keyword(s):  
Linear Regression ◽  
Clinical Relevance ◽  
Accurate Description ◽  
Maximum Hardness ◽  
Curing Behavior ◽  
Non Linear ◽  
Light Curing

Clinical Relevance Redefined hardness ratios, based on extended cure intervals and maximum hardness when used in conjunction with non-linear regression, provide a readily available and accurate characterization of the curing performance of LCU-composite combinations, which is superior to the use of traditional per-specimen hardness ratios. It is recommended that the light curing guidelines provided to clinicians should be based on this more accurate description of curing behavior.

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

scholarly journals Clinical Relevance of Vitamins and Carotenoids With Liver Steatosis and Fibrosis Detected by Transient Elastography in Adults

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaohui Liu ◽  
Hong Shen ◽  
Mingfeng Chen ◽  
Jun Shao
Keyword(s):  
Liver Fibrosis ◽  
Linear Regression ◽  
Vitamin C ◽  
Vitamin B12 ◽  
Clinical Relevance ◽  
Transient Elastography ◽  
Liver Steatosis ◽  
Serum Vitamin ◽  
Non Linear ◽  
Β Carotene

Background: Vitamins and carotenoids may be involved in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). Previously related publications mainly focused on vitamin D and vitamin E, and studies on other vitamins and carotenoids and NAFLD are scarce.Methods: This study aimed to explore the clinical relevance of vitamin A, B vitamins (vitamin B1, vitamin B2, niacin, vitamin B6, folate, vitamin B12, and choline), vitamin C and carotenoids (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein + zeaxanthin) with liver steatosis and fibrosis in the 2017–2018 NHANES (N = 4,352). Liver steatosis and fibrosis were detected by transient elastography. Logistic regression, linear regression and restricted cubic splines were adopted to explore the non-linear dose-response relationships.Results: Higher intakes of vitamin C [0.68 (0.50–0.93)] and β-carotene [0.71 (0.54–0.93)] were inversely associated with liver steatosis. Higher levels of serum vitamin C [0.45 (0.32–0.62)] were inversely associated with liver fibrosis, while higher intakes of choline [1.43 (1.04–1.98)] and α-carotene [1.67 (1.01–2.74)] were positively associated with liver fibrosis. In addition, marginally inverse association between lutein + zeaxanthin and liver steatosis and positive association between vitamin B12 and liver fibrosis were found. In linear regression, the above-mentioned associations between vitamin C, β-carotene, and lutein + zeaxanthin and liver steatosis, and serum vitamin C, choline, α-carotene, and vitamin B12 and liver fibrosis were also found. The above-mentioned associations were mainly linear, while the relationship between β-carotene and liver steatosis might be non-linear.Conclusion: Vitamin C, α-carotene, β-carotene, lutein + zeaxanthin, choline and vitamin B12 may be associated with liver steatosis and fibrosis.

Effect of LED Light-Curing Spectral Emission Profile on Light-Cured Resin Cement Degree of Conversion

2020 ◽  
Author(s):  
RQ Ramos ◽  
RR Moraes ◽  
GC Lopes
Keyword(s):  
Clinical Relevance ◽  
Degree Of Conversion ◽  
Resin Cement ◽  
Light Activation ◽  
Spectral Emission ◽  
Led Light ◽  
Emission Profile ◽  
Light Curing

Clinical Relevance The use of multipeak LED light-curing guarantees efficiency on light activation of Ivocerin-containing light-cured resin cement.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

scholarly journals Is There a Relationship between the Elasticity of Brain Tumors, Changes in Diffusion Tensor Imaging, and Histological Findings? A Pilot Study Using Intraoperative Ultrasound Elastography

2021 ◽  
Vol 11 (2) ◽  
pp. 271
Author(s):  
Santiago Cepeda ◽  
Sergio García-García ◽  
María Velasco-Casares ◽  
Gabriel Fernández-Pérez ◽  
Tomás Zamora ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Diffusion Tensor Imaging ◽  
Linear Regression ◽  
Brain Tumors ◽  
Diffusion Tensor ◽  
Intraoperative Ultrasound ◽  
Ultrasound Elastography ◽  
Proliferation Index ◽  
Strong Positive Correlation ◽  
Non Linear

Intraoperative ultrasound elastography (IOUS-E) is a novel image modality applied in brain tumor assessment. However, the potential links between elastographic findings and other histological and neuroimaging features are unknown. This study aims to find associations between brain tumor elasticity, diffusion tensor imaging (DTI) metrics, and cell proliferation. A retrospective study was conducted to analyze consecutively admitted patients who underwent craniotomy for supratentorial brain tumors between March 2018 and February 2020. Patients evaluated by IOUS-E and preoperative DTI were included. A semi-quantitative analysis was performed to calculate the mean tissue elasticity (MTE). Diffusion coefficients and the tumor proliferation index by Ki-67 were registered. Relationships between the continuous variables were determined using the Spearman ρ test. A predictive model was developed based on non-linear regression using the MTE as the dependent variable. Forty patients were evaluated. The pathologic diagnoses were as follows: 21 high-grade gliomas (HGG); 9 low-grade gliomas (LGG); and 10 meningiomas. Cases with a proliferation index of less than 10% had significantly higher medians of MTE (110.34 vs. 79.99, p < 0.001) and fractional anisotropy (FA) (0.24 vs. 0.19, p = 0.020). We found a strong positive correlation between MTE and FA (rs (38) = 0.91, p < 0.001). A cubic spline non-linear regression model was obtained to predict tumoral MTE from FA (R2 = 0.78, p < 0.001). According to our results, tumor elasticity is associated with histopathological and DTI-derived metrics. These findings support the usefulness of IOUS-E as a complementary tool in brain tumor surgery.

scholarly journals Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Trend ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Linear Regression Models ◽  
Non Linear ◽  
Nonlinear Trends ◽  
The Impact ◽  
Non Parametric

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.

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