scholarly journals Bead Geometry Prediction Model for 9% Nickel Laser Weldment, Part 1: Global Regression Model vs. Modified Regression Model

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 793
Author(s):  
Jisun Kim ◽  
Jaewoong Kim ◽  
Changmin Pyo ◽  
Kwangsan Chun
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Prediction Models ◽  
Welding Process ◽  
P Value ◽  
Bead Geometry ◽  
Nickel Steel ◽  
Global Regression ◽  
Data Points ◽  
Bead Height

Due to its excellent toughness and stiffness in cryogenic conditions, 9% nickel steel is applied to LNG storage facilities, and its usage is increasing as a result of changes in environmental regulations. A study was conducted on the development of a predictive model to optimize the laser welding process of 9% nickel steel, and two prediction models were developed using one hundred data points obtained through experiments. A global regression model used as a general prediction model and a modified regression model using the p-value of the analysis of variance were developed, and their prediction performance was compared. It was found that the modified regression model was superior to the global regression model in terms of predicting the bead shape, including parameters such as penetration depth, bead height, and area ratio.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

Process Parameter Effects over Bead Properties during Material Deposition of PTAW Process

2020 ◽  
Vol 978 ◽  
pp. 55-63
Author(s):  
Soumen Mandal ◽  
Subrata Kumar ◽  
Manish Oraon
Keyword(s):  
Process Parameters ◽  
Arc Welding ◽  
Welding Process ◽  
Fusion Welding ◽  
Bead Geometry ◽  
Plasma Transferred Arc ◽  
Surface Method ◽  
Material Deposition ◽  
Bead Height ◽  
Plasma Transferred Arc Welding

The quality and geometry of deposited bead depend on their input process parameters and their interaction effects in fusion welding process. Minimum dilution and maximum bead size are the most desirable property in material processing applications. The effects of process parameters on dilution and bead geometry have been analysed during material deposition by Plasma Transferred Arc Welding (PTAW) process using the response surface method. The experimental data are used for modelling using three level factorial techniques. The mathematical models have been developed for bead height, width and dilution. The accuracy of the models has been checked using the analysis of variance. The effects of process parameters on bead geometry and dilution have been investigated.

Establishment of Prediction Models for venous thromboembolism in non-oncological urological inpatients--a single center experience

2020 ◽  
Author(s):  
Kaixuan Li ◽  
Haozhen Li ◽  
Quan Zhu ◽  
Ziqiang Wu ◽  
Zhao Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Multivariate Logistic Regression Model ◽  
Single Center Experience

Abstract Background To establish prediction models for venous thromboembolism (VTE) in non-oncological urological inpatients. Methods A retrospective analysis of 1453 inpatients was carried out and the risk factors for VTE had been clarified our previous studies. Results Risk factors included the following 5 factors: presence of previous VTE (X1), presence of anticoagulants or anti-platelet agents treatment before admission (X2), D-dimer value (≥ 0.89 µg/ml, X3), presence of lower extremity swelling (X4), presence of chest symptoms (X5). The logistic regression model is Logit (P) = − 5.970 + 2.882 * X1 + 2.588 * X2 + 3.141 * X3 + 1.794 * X4 + 3.553 * X5. When widened the p value to not exceeding 0.1 in multivariate logistic regression model, two addition risk factors were enrolled: Caprini score (≥ 5, X6), presence of complications (X7). The prediction model turns into Logit (P) = − 6.433 + 2.696 * X1 + 2.507 * X2 + 2.817 * X3 + 1.597 * X4 + 3.524 * X5 + 0.886 * X6 + 0.963 * X7. Internal verification results suggest both two models have a good predictive ability, but the prediction accuracy turns to be both only 43.0% when taking the additional 291 inpatients’ data in the two models. Conclusion We built two similar novel prediction models to predict VTE in non-oncological urological inpatients. Trial registration: This trial was retrospectively registered at http://www.chictr.org.cn/index.aspx under the public title“The incidence, risk factors and establishment of prediction model for VTE n urological inpatients” with a code ChiCTR1900027180 on November 3, 2019. (Specific URL to the registration web page: http://www.chictr.org.cn/showproj.aspx?proj=44677).

scholarly journals Spatial interaction of groundwater and surface topographic using geographically weighted regression in built-up area

2020 ◽  
Author(s):  
Asep Mulyadi ◽  
Moh. Dede ◽  
Millary Agung Widiawaty
Keyword(s):  
Regression Model ◽  
Geographically Weighted Regression ◽  
Flat Surface ◽  
Spatial Interaction ◽  
Weighted Regression ◽  
P Value ◽  
Groundwater Levels ◽  
Geographically Weighted Regression Model ◽  
Primary Water ◽  
Global Regression

Groundwater is a primary water resource for human living. In Indonesia, excessive exploitation of groundwater generally occurs in the built-up area due to over-discharge processes characterized by a cone of depression. This research revealed the spatial interaction between groundwater levels and surface topographic using geographically weighted regression in built-up area. Groundwater levels data are obtained from 72 wells in Cikembang, Bandung Regency, whereas surface topographic based on BIG's DEMNas data which has 8 meters spatial resolution. This study showed significant spatial interaction between groundwater levels and surface topographic in the built-up area. The interaction has a clustered pattern with p-value less than 0.01. It indicated in the area with flat surface topographic has lower groundwater levels than others. There are several points who indicated the cone of depression in the built-up area with flat topographic. The geographically weighted regression model has high spatial variability and better results than the global regression model to assess groundwater level interaction with surface topographic.

Predicting Fatality Rates Due to Earthquakes Accounting for Community Vulnerability

2019 ◽  
Vol 35 (2) ◽  
pp. 513-536 ◽  
Author(s):  
Yi (Victor) Wang ◽  
Paolo Gardoni ◽  
Colleen Murphy ◽  
Stéphane Guerrier
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Hazard Map ◽  
Seismic Risk Analysis ◽  
Community Vulnerability ◽  
Building Inventory ◽  
Socioeconomic Characteristics ◽  
Data Points ◽  
Seismic Hazard Map ◽  
Earthquake Fatalities

The existing prediction models for earthquake fatalities usually require a detailed building inventory that might not be readily available. In addition, existing models tend to overlook the socioeconomic characteristics of communities of interest as well as zero-fatality data points. This paper presents a methodology that develops a probabilistic zero-inflated beta regression model to predict earthquake fatality rates given the geographic distributions of earthquake intensities with data reflecting community vulnerability. As an illustration, the prediction model is calibrated using fatality data from 61 earthquakes affecting Taiwan from 1999 to 2016, as well as information on the socioeconomic and environmental characteristics of the affected communities. Using a local seismic hazard map, the calibrated prediction model is used in a seismic risk analysis for Taiwan that predicts the expected fatality rates and counts caused by earthquakes in future years.

scholarly journals Mediastinal/hilar lymphadenopathy: A possibly overlooked variable in the classification of lung nodules

2021 ◽  
Author(s):  
qingxia fan
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Lung Nodule ◽  
Added Value ◽  
Lung Nodules ◽  
Hilar Lymphadenopathy

Abstract Background Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included. Methods A total of 359 patients with pulmonary nodules were considered for enrollment in the study. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn. Results There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The AUC of the training and validation sets was 0.93 (95% CI, 0.90–0.97) and 0.91 (95% CI, 0.85–0.98), respectively. In the validation set with or without mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI, 0.90–0.99) and 0.91 (95%CI, 0.87–0.95), respectively. The Hosmer-Lemeshow goodness-of-fit statistic was 0.22. A nomogram was drawn to visualize the model. Conclusions We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of the inclusion of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, the presence of mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy, highlighting the importance of this variable in clinical practice.

scholarly journals Enhanced co-expression extrapolation (COXEN) gene selection method for building anticancer drug response prediction models.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14073-e14073
Author(s):  
Yitan Zhu ◽  
Thomas S. Brettin ◽  
Fangfang Xia ◽  
Maulik Shukla ◽  
Alexander Partin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Drug Response ◽  
Prediction Models ◽  
Gene Selection ◽  
Expression Patterns ◽  
Response Prediction ◽  
Precision Oncology ◽  
General Drug ◽  
Multiple Drugs

e14073 Background: Accurate prediction of tumor response to a drug treatment is of paramount importance for precision oncology. The co-expression extrapolation (COXEN) gene selection approach has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug. Here, we enhance the original COXEN approach to select genes that are predictive of the efficacies of multiple drugs simultaneously for building general drug response prediction model. Methods: We implemented two methods to select predictive genes. The first method ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs. The second method uses a linear regression model to evaluate the prediction power of a gene for all drugs while the drugs are one-hot encoded in the regression model. Among the predictive genes, we further select genes by evaluating the preservation of co-expression patterns between cancer cases with drug response data available and cancer cases for which drug response needs to be predicted, because the preservation of co-expression patterns indicates the similarity of genomic regulations between cancer cases. Results: To test the enhanced COXEN method, we used a lightGBM regression model to predict drug response based on the selected genes on two benchmark in vitro drug screening datasets. The table below compares the performance of prediction models built based on 200 genes selected by the enhanced COXEN method to that of models built on 200 genes randomly picked from the LINCS gene set, which includes 976 “landmark” genes well-representing cellular transcriptomic changes identified in the Library of Integrated Network-Based Cellular Signatures (LINCS) project. The enhanced COXEN approach selects genes better than random LINCS genes as demonstrated by the increased average coefficient of determination (R2) for predicting the area under the dose response curve through cross-validation. Pair-wise t-test indicates the improvement is statistically significant (p-value ≤ 0.05) on both datasets. Conclusions: Our result demonstrates the benefit of using an enhanced COXEN approach to select genes for building general drug response prediction model. [Table: see text]

scholarly journals Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1858
Author(s):  
Jeyaganesh Devaraj ◽  
Aiman Ziout ◽  
Jaber E. Abu Qudeiri
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Bead Geometry ◽  
Learning Models ◽  
Weld Bead Geometry ◽  
Taguchi Optimization ◽  
Initial Design ◽  
Machine Learning Models

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

scholarly journals A Study on the Weldment Hardening Discrimination Procedure and Improvement of Flux Cored Arc Welding Process of ASTM A553-1 (9% Nickel Steel) Material Using Bead Geometry Distribution

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1282
Author(s):  
Minho Park ◽  
Jisun Kim ◽  
Changmin Pyo ◽  
Jaewoong Kim ◽  
Kwangsan Chun
Keyword(s):  
Base Metal ◽  
Arc Welding ◽  
Welding Process ◽  
Fuel Oil ◽  
Bead Geometry ◽  
Nickel Steel ◽  
Welding Quality ◽  
Dilution Ratio ◽  
Flux Cored Arc Welding ◽  
Arc Welding Process

As a result of strengthened sulfur content standards for ship fuel oil in IMO regulations, major domestic and foreign carriers have a high and growing demand for liquefied natural gas (LNG) powered ships and related equipment. For LNG operation in a cryogenic environment, a storage tank and fuel supply system that uses steel with excellent brittleness and fatigue strength is required. Ships that use LNG have a high vulnerability to explosion and fire. For this reason, 9% Ni is typically used, since a ship requires high quality products with special materials and structural technologies that guarantee operability at cryogenic temperatures. However, there is an urgent need for research to derive a uniform welding quality, since high process difficulty and differences in welding quality related to a welder’s skills can cause a deterioration of the weld quality in the 9% Ni steel welding process. For 9% Ni steel, the higher the dilution ratio of the base metal, the lower the strength. In order to secure the required strength, excessive dilution of the base metal should be avoided, and the relationship between dilution ratio and strength should be investigated. According to previous research, if it exceeds 25% it may be lower than the API standard of 363 MPa for hardening welds. Therefore, in this study, the flux cored arc welding process is performed by establishing criteria that can be evaluated based on the SVM method in order to determine the structure of the weld to be cured according to the dilution rate of the base metal. We would like to propose a multipurpose optimization algorithm to ensure uniform quality of 9% Ni steel.

Sign in / Sign up

Export Citation Format

Share Document