scholarly journals Characterization of Friction-Stir Welded Joints of AA1100 by Factorial Design Based Hierarchical Regression Model

2020 ◽  
Vol 44 (4) ◽  
pp. 271-280
Author(s):  
Pallavi N. Senapati ◽  
Rajat K. Bhoi
Keyword(s):  
Regression Model ◽  
Factorial Design ◽  
Welded Joints ◽  
Hierarchical Regression ◽  
Friction Stir ◽  
Hierarchical Regression Model

scholarly journals Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming

2020 ◽  
Vol 92 (6) ◽  
pp. 7-16
Author(s):  
AKSHANSH MISHRA
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Regression Model ◽  
Decision Trees ◽  
Root Mean Square ◽  
Welded Joints ◽  
Mean Square ◽  
Friction Stir ◽  
Mean Square Errors

In modern computational science, the interplay existing between machine learning and optimization process marks the most vital developments. Optimization plays an important role in mechanical industries because it leads to reduce in material cost, time consumption and increase in production rate. The recent work focuses on performing the optimization task on Friction Stir Welding process for obtaining the maximum Ultimate Tensile Strength (UTS) of the friction stir welded joints. Two machine learning algorithms i.e. Artificial Neural Network (ANN) and Decision Trees regression model are selected for the purpose. The input variables are Tool Rotational Speed (RPM), Tool Traverse Speed (mm/min) and Axial Force (KN) while the output variable is Ultimate Tensile Strength (MPa). It is observed that in case of the Artificial Neural Networks the Root Mean Square Errors for training and testing sets are 0.842 and 0.808 respectively while in case of Decision Trees regression model, the training and testing sets result Root Mean Square Errors of 11.72 and 14.61. So, it can be concluded that ANN algorithm gives better and accurate result than Decision Tree regression algorithm.

scholarly journals Researcher Perceptions of a Self-Service Online Portal to Facilitate Volunteer Recruitment into Clinical Trials

ACI Open ◽  
2021 ◽  
Vol 05 (02) ◽  
pp. e59-e66
Author(s):  
Srinivas Emani ◽  
Yichuan Grace Hsieh ◽  
Greg Estey ◽  
Holly M. Parker ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Regression Model ◽  
Behavioral Intention ◽  
Self Efficacy ◽  
Hierarchical Regression ◽  
Intention To Use ◽  
Hierarchical Regression Model ◽  
Volunteer Recruitment ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

Abstract Background Recruitment of volunteers is a major challenge for clinical trials. There has been increasing development and use of Internet-based portals in recruitment for clinical research. There has been little research on researcher use and perceptions of these portals. Objectives This study evaluated researcher perceptions of use of Rally, an Internet-based portal for clinical trial volunteer recruitment. Methods A cross-sectional survey was developed and implemented to understand researcher perceptions. From theoretical models of information technology use, the survey adopted items in four domains: ease of use, usefulness, facilitating conditions, and self-efficacy. The dependent variable was researchers' behavioral intention to use Rally. The survey captured characteristics of researchers such as gender, age, and role. It was implemented using the REDCap survey tool. An email invitation followed by three reminders was sent to researchers. A hierarchical regression model was applied to assess predictors of behavioral intention. Results The survey response rate was 35.6% (152 surveys received from 427 contacted researchers). In the hierarchical regression model, facilitating conditions and self-efficacy predicted behavioral intention (F (4,94) = 6.478; p <0.001). The model explained 21.6% of the variance in behavioral intention (R-square change = 21.3%, p <0.001). Conclusion Facilitating conditions and self-efficacy predicted researchers' behavioral intention to use Rally for volunteer recruitment into clinical trials. Future research should document best practices and strategies for enhancing researcher use of online portals for volunteer recruitment.

scholarly journals SCHiRM: Single Cell Hierarchical Regression Model to detect dependencies in read count data

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Henrik Mannerström ◽  
Saara Hiltunen ◽  
Harri Lähdesmäki
Keyword(s):  
Regression Model ◽  
Single Cell ◽  
Count Data ◽  
Regulatory Networks ◽  
Research Question ◽  
Read Count ◽  
Hierarchical Regression ◽  
Specific Gene ◽  
Hierarchical Regression Model ◽  
Log Normal

AbstractMotivationModern single cell RNA sequencing (scRNA-seq) technologies have made it possible to measure the RNA content of individual cells. The scRNA-seq data provide us with detailed information about the cellular states but, despite several pioneering efforts, it remains an open research question how regulatory networks could be inferred from these noisy discrete read count data.ResultsHere, we introduce a hierarchical regression model which is designed for detecting dependencies in scRNA-seq and other count data. We model count data using the Poisson-log normal distribution and, by means of our hierarchical formulation, detect the dependencies between genes using linear regression model for the latent, cell-specific gene expression rate parameters. The hierarchical formulation allows us to model count data without artificial data transformations and makes it possible to incorporate normalization information directly into the latent layer of the model. We test the proposed approach using both simulated and experimental data. Our results show that the proposed approach performs better than standard regression techniques in parameter inference task as well as in variable selection task.AvailabilityAn implementation of the method is available athttps://github.com/jeintos/[email protected],[email protected]

scholarly journals The Influence of Clinical Nurses' Empathy on their Death Competence: A Cross-Sectional Survey Study

2021 ◽  
Author(s):  
Jiahao Wang ◽  
Zhengying Chen ◽  
Yiting Liu ◽  
Xiaoli Liao ◽  
Liuxin Long ◽  
...  
Keyword(s):  
Regression Model ◽  
Pearson Correlation ◽  
General Information ◽  
Survey Study ◽  
Hierarchical Regression ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Clinical Nurses ◽  
Hierarchical Regression Model ◽  
The Status

Abstract BackgroundEmpathy and death competence are important competences for clinical nurses. However, there is no clear consensus about what impact empathy has on death competency. Our study aimed to understand the status of the empathy and death competence of clinical nurses in China and to explore the effect of empathy on their competence.MethodsFor a survey conducted from May–June 2021, 1415 clinical nurses were selected by convenience sampling as the research objects. The Coping with Death Scale, the Jefferson Scale of Empathy—Health Professionals and a general information questionnaire designed by the researchers were used to investigate the status of the empathy and death competence of clinical nurses. The relationship between empathy and death competence was analysed by Pearson correlation, and the influence of the empathy of clinical nurses on their death competence was analysed by a hierarchical regression model.ResultPearson correlation analysis revealed that death competence was positively correlated with each dimension of empathy. Hierarchical regression model analysis revealed that after controlling for the influence of general information, nurses' empathy had a significant influence on their death competence, and this independently explained 5.8% of the variance in death competence.ConclusionsThe death competence of the clinical nurses in this sample was moderate to low level. Emotional nursing and transposition thinking are important influencing factors of death competence. Nursing managers should improve the empathy of clinical nurses to promote their death competence.

Fatigue Prediction by Thermographic Method of Aluminum Alloy 6082 Panels: Comparison Between FSW and MIG Welding

2007 ◽  
Vol 23 (04) ◽  
pp. 215-222
Author(s):  
Vincenzo. Crupi ◽  
Alberto. Marinò ◽  
Marco Biot ◽  
Giacomo. Risitanoison
Keyword(s):  
Aluminum Alloy ◽  
Welded Joints ◽  
Fatigue Behavior ◽  
Experimental Tests ◽  
Mig Welding ◽  
Friction Stir ◽  
Traditional Procedure ◽  
Thermographic Analysis ◽  
Good Agreement

This paper focuses on the fatigue behavior of aluminum alloy welded joints, which can represent points of weakness in the ship structure. The traditional methods of fatigue assessment of welded joints have some limitations and are extremely time consuming. The Risitano method, based on thermographic analysis, has been applied to overcome these difficulties by predicting the fatigue behavior of welds. Experimental tests have been carried out to assess and compare the fatigue capability(S-N curves, endurance limits) of different welded joints, obtained by means of friction stir welding (FSW) and metal inert gas (MIG) welding. Fatigue predictions obtained resorting to the thermographic method (TM) show good agreement with those derived from the traditional procedure. Thus, TM proves to be a powerful tool also for the characterization of the kind of welded joints mentioned.

scholarly journals Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Saeed Sharifi-Malvajerdi ◽  
Feiyu Zhu ◽  
Colin B. Fogarty ◽  
Michael P. Fay ◽  
Rick M. Fairhurst ◽  
...  
Keyword(s):  
Regression Model ◽  
Clearance Rate ◽  
Software Package ◽  
Malaria Parasite ◽  
Parasite Clearance ◽  
Hierarchical Regression ◽  
R Software ◽  
Bayesian Hierarchical ◽  
Hierarchical Regression Model
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