An Approach to Identify New Insecticides AgainstMyzus Persicae. In silicoStudy Based on Linear and Non‐linear Regression Techniques

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.

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Prediction of Composition of Anomalous Cryptoperthites from the Rattlesnake Mountain Sill, West Texas, Using Stepwise Multiple Linear Regression Techniques

Geological Society of America Bulletin ◽

10.1130/0016-7606(1973)84<949:pocoac>2.0.co;2 ◽

1973 ◽

Vol 84 (3) ◽

pp. 949 ◽

Cited By ~ 1

Author(s):

J. C. BUTLER

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Stepwise Multiple Linear Regression ◽

West Texas ◽

Regression Techniques

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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

Brain Sciences ◽

10.3390/brainsci11020271 ◽

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.

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