Evaluating yield strength of Ni-based superalloys via high throughput experiment and machine learning

2021 ◽  
pp. 2050015
Author(s):  
Feng Liu ◽  
Zexin Wang ◽  
Zi Wang ◽  
Zijun Qin ◽  
Zihang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Yield Strength ◽  
High Throughput ◽  
Pearson Correlation ◽  
Gaussian Process Regression ◽  
Correlation Coefficients ◽  
High Entropy ◽  
Feature Importance ◽  
Key Factor ◽  
The Individual

Yield strength (YS) is a key factor during design and application of Ni-based superalloys with complex compositions, hence it is of great significance to evaluate the YS prior to manufacturing. In this work, alloy diffusion-multiple technology was employed as a high-throughput way to yield the hardness dataset. Based on the composition and other descriptors, Pearson correlation coefficients, stability selection and feature importance were used to select the efficient feature variables. Thereafter, six different machine learning models were applied to predict the YS. Finally, the individual and interaction effect of Co and Mo could be effectively detected by the Gaussian process regression (GPR) model. The optimum composition of Ni-based superalloys with the largest YS at room temperature was determined using the trained GPR model and genetic algorithm. This method can be extended to predict the YS in other multicomponent alloys, such as Ti alloys, Co-based alloys, and high entropy alloys.

scholarly journals The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning

Machines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 184
Author(s):  
Yu-Cheng Chiu ◽  
Po-Hsun Wang ◽  
Yuh-Chung Hu
Keyword(s):  
Machine Learning ◽  
Machine Tool ◽  
Random Forests ◽  
Pearson Correlation ◽  
Gaussian Process Regression ◽  
Correlation Coefficients ◽  
Thermal Error ◽  
Error Modeling ◽  
Machining Process ◽  
Tool Manufacture

Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself. Real-time measurement of thermal error will interrupt the machining process. Therefore, this paper presents a machine learning model to estimate the thermal error of the spindle from its feature temperature points. The authors adopt random forests and Gaussian process regression to model the thermal error of the spindle and Pearson correlation coefficients to select the feature temperature points. The result shows that random forests collocating with Pearson correlation coefficients is an efficient and accurate method for the thermal error modeling of the spindle. Its accuracy reaches to 90.49% based on only four feature temperature points—two points at the bearings and two points at the inner housing—and the spindle speed. If the accuracy requirement is not very onerous, one can select just the temperature points of the bearings, because the installation of temperature sensors at these positions is acceptable for the spindle or machine tool manufacture, while the other positions may interfere with the cooling pipeline of the spindle.

The Use of Rubrics in the Clinical Evaluation of Podiatric Medical Students

2016 ◽  
Vol 106 (1) ◽  
pp. 60-67
Author(s):  
Kevin M Smith ◽  
Simon Geletta ◽  
Austin McArdle
Keyword(s):  
Medical Students ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Grade Point Averages ◽  
Evaluation Tool ◽  
Grade Point ◽  
Education Literature ◽  
Students First ◽  
The Individual ◽  
The Relationship

Background: We assessed the differences in podiatric medical students' clinical professionalism objective scores (CPOSs) by comparing a previous nonrubric evaluation tool with a more recently implemented objective-centered rubric evaluation tool. This type of study has never been performed or reported on in the podiatric medical education literature.Methods: We conducted a retrospective analysis of 89 third-year podiatric medical students between academic years 2010-2011 and 2011-2012. A Pearson correlation coefficient analysis was performed to compare CPOSs from the students' first (CPOS1) and second (CPOS2) rotations. A correlation analysis was performed comparing students' grade point averages (GPAs) with each of the individual CPOSs to verify the validity of the rubric evaluation tool.Results: The Pearson correlation coefficients for the relationship between 2012 CPOS1 and CPOS2 and GPA were r = 0.233 (P ≤ .093) and r = 0.290 (P < .035) and for the relationship between 2013 CPOS1 and CPOS2 and GPA were r = 0.525 (P = .001) and r = 0.730 (P < .001).Conclusions: These findings suggest that the use of a rubric in the evaluation of podiatric medical students' CPOSs is correlated with their GPAs, and CPOS2 demonstrated a higher correlation than CPOS1. We believe that implementation of the rubric evaluation tool has increased the accuracy of the evaluation of podiatric medical students with respect to CPOSs.

scholarly journals Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload

2021 ◽  
pp. 188-196 ◽  
Author(s):  
Lauren C. Benson ◽  
Carlyn Stilling ◽  
Oluwatoyosi B.A. Owoeye ◽  
Carolyn A. Emery
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Multiple Imputation ◽  
Perceived Exertion ◽  
Pearson Correlation ◽  
Imputation Method ◽  
Rating Of Perceived Exertion ◽  
Imputation Methods ◽  
Single Imputation ◽  
The Individual

Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93 (45 females, 48 males) high school basketball players throughout a season. Recorded data were simulated as missing and imputed using ten imputation methods based on the context of the individual, team and session. Both single imputation and machine learning methods were used to impute the simulated missing data. The difference between the imputed data and the actual workload values was computed as root mean squared error (RMSE). A generalized estimating equation determined the effect of imputation method on RMSE. Multiple imputation of the original dataset, with all known and actual missing workload data, was used to examine trends in longitudinal workload data. Following multiple imputation, a Pearson correlation evaluated the longitudinal association between jump count and sRPE over the season. A single imputation method based on the specific context of the session for which data are missing (team mean) was only outperformed by methods that combine information about the session and the individual (machine learning models). There was a significant and strong association between jump count and sRPE in the original data and imputed datasets using multiple imputation. The amount and nature of the missing data should be considered when choosing a method for single imputation of workload data in youth basketball. Multiple imputation using several predictor variables in a regression model can be used for analyses where workload is accumulated across an entire season.

scholarly journals Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7213
Author(s):  
Denis Klimenko ◽  
Nikita Stepanov ◽  
Jia Li ◽  
Qihong Fang ◽  
Sergey Zherebtsov
Keyword(s):  
Machine Learning ◽  
Yield Strength ◽  
Strength Prediction ◽  
Support Vector ◽  
High Entropy Alloys ◽  
High Entropy ◽  
Average Prediction Error ◽  
Calphad Modeling ◽  
Wide Range ◽  
Alloys Of The Al

The aim of this work was to provide a guidance to the prediction and design of high-entropy alloys with good performance. New promising compositions of refractory high-entropy alloys with the desired phase composition and mechanical properties (yield strength) have been predicted using a combination of machine learning, phenomenological rules and CALPHAD modeling. The yield strength prediction in a wide range of temperatures (20–800 °C) was made using a surrogate model based on a support-vector machine algorithm. The yield strength at 20 °C and 600 °C was predicted quite precisely (the average prediction error was 11% and 13.5%, respectively) with a decrease in the precision to slightly higher than 20% at 800 °C. An Al13Cr12Nb20Ti20V35 alloy with an excellent combination of ductility and yield strength at 20 °C (16.6% and 1295 MPa, respectively) and at 800 °C (more 50% and 898 MPa, respectively) was produced based on the prediction.

scholarly journals GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley

2021 ◽  
Vol 12 ◽  
Author(s):  
Sheikh Jubair ◽  
James R. Tucker ◽  
Nathan Henderson ◽  
Colin W. Hiebert ◽  
Ana Badea ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Molecular Markers ◽  
Fusarium Head Blight ◽  
High Throughput ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Spring Barley ◽  
Pearson Correlation ◽  
Head Blight

Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical (–1, 0, 1) or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods.

Extended connectivity interaction features: improving binding affinity prediction through chemical description

2020 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
José L Medina-Franco ◽  
Jordi Mestres ◽  
Xavier Barril
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Supplementary Information ◽  
Scoring Functions ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Extended Connectivity ◽  
Chemical Description

Abstract Motivation Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited. Results Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein–ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein−ligand atom-type pair counts that take into account each atom’s connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein–ligand affinities (pKd/pKi). The models were evaluated in terms of ‘scoring power’ on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power. Availability and implementation Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Slow or swift, your patients’ experience won’t drift: absence of correlation between physician productivity and the patient experience

CJEM ◽  
2016 ◽  
Vol 19 (5) ◽  
pp. 372-380
Author(s):  
Kasia Lenz ◽  
Andrew McRae ◽  
Dongmei Wang ◽  
Benjamin Higgins ◽  
Grant Innes ◽  
...  
Keyword(s):  
Path Analysis ◽  
Patient Experience ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Productivity Data ◽  
Physician Productivity ◽  
Number Of Patients ◽  
Experience Survey ◽  
Positive Correlations ◽  
The Individual

AbsractObjectivesTo evaluate the relationship between Emergency Physician (EP) productivity and patient satisfaction with Emergency Department (ED) care.MethodsThis retrospective observational study linked administrative and patient experience databases to measure correlations between the patient experience and EP productivity. The study was performed across three Calgary EDs (from June 2010 to July 2013). Patients>16 years old with completed Health Quality Council of Alberta (HQCA) ED Patient Experience Surveys were included. EP productivity was measured at the individual physician level and defined as the average number of patients seen per hour. The association between physician productivity and patient experience scores from six composite domains of the HQCA ED Patient Experience Survey were examined using Pearson correlation coefficients, linear regression modelling, and a path analysis.ResultsWe correlated 3,794 patient experience surveys with productivity data for 130 EPs. Very weak non-significant negative correlations existed between productivity and survey composites: “Staff Care and Communication” (r=-0.057, p=0.521), “Discharge Communication” (r=-0.144, p=0.102), and “Respect” (r=-0.027, p=0.760). Very weak, non-significant positive correlations existed between productivity and the composite domains: “Medication Communication” (r=0.003, p=0.974) and “Pain management” (r=0.020, p=0.824). A univariate general linear model yielded no statistically significant correlations between EP productivity and patient experience, and the path analysis failed to show a relationship between the variables.ConclusionWe found no correlation between EP productivity and the patient experience.

scholarly journals Magnitude of physiological curvatures of the spine and the incidence of contractures of selected muscle groups in students

2018 ◽  
Vol 10 (1) ◽  
pp. 31-37
Author(s):  
Elżbieta Olszewska ◽  
Piotr Tabor ◽  
Renata Czarniecka
Keyword(s):  
Hip Joint ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Shoulder Girdle ◽  
The Body ◽  
Body Posture ◽  
Inclination Angles ◽  
Shoulder Complex ◽  
Muscle Groups ◽  
The Individual

Summary Study aim: The aim of this study was to evaluate the incidence of contractures of selected muscle groups with respect to the magnitude of the physiological curvatures of the spine in young men with above-average levels of physical activity.Material and methods: The study included 96 students at the University of Physical Education in Warsaw aged between 20 and 22 years (21.2 ± 1.05). Ninety-five percent of the students participated in sports training activities. The study was conducted between January and February 2016. The selected traits of the body posture were evaluated with an inclinometer, which was used to measure the inclination angles of sections of the spine relative to the vertical. The ranges of motion in the shoulder complex and the pelvic complex were measured with a goniometer. Values of 175º (for the shoulder complex) and 174° (for the hip joint) were assumed to indicate a decreased range of motion.Results: The analysis of the individual results concerning mobility disorders in the shoulder complex and the pelvic complex revealed significant abnormalities in the researched group of students. About 90% of the study participants showed contrac­tures of selected muscle groups within the shoulder girdle, primarily in the right upper limb. Similar results were obtained for the incidence of contractures in the flexors of the hip joint. Flexion contractures in the hip joint were observed in around 84% of the participants, primarily in the left lower limb. The correlations between the inclination angles of the sections of the spine relative to the vertical and the ranges of motion in the shoulder complex and the pelvic complex, established using Pearson correlation coefficients, were ambiguous. The angles γ, β1 and α were inversely proportional to the range of raising motions of the upper limbs through flexion, where the correlation coefficients of all angles were statistically significant. Similar tendencies were observed for the correlations between the angles β2, β1 and α and the range of the extension movements at the hip joint, although the correlation coefficients were statistically significant only in the case of the angle β1.Conclusions: Ranges of movement in the shoulder complex and pelvic complex have an influence on magnitude of physiologi­cal curvatures of the spine and the functioning of body posture.

Machine-learning and high-throughput studies for high-entropy materials

2022 ◽  
Vol 147 ◽  
pp. 100645
Author(s):  
E-Wen Huang ◽  
Wen-Jay Lee ◽  
Sudhanshu Shekhar Singh ◽  
Poresh Kumar ◽  
Chih-Yu Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
High Entropy
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