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119 results for “Failure Analysis”
RIBuild: Analysis of models for failure
<p>The dataset consists of data used for analysing a number of models, each related to a specific failure mode or failure mechanism that affects the material properties of building materials. Three RIBuild partners (KUL, UNIVPM, RISE) were responsible of performing laboratory tests to evaluate the models chosen to characterize a specific failure mode (frost, algae, mould). One RIBuild partner (DTU/AAU) used measurement data from a WP3 test setup to validate simulations of wood rot in wooden beam ends.</p> <p>Further details to be found in RIBuild deliverable D2.2.</p> <p>Overview of data files to be found in 'RIBuild data WP2 Model analysis' as part of this dataset.</p>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Supplemental material to 'A variational rigid-block modelling approach to nonlinear elastic and kinematic analysis of failure mechanisms in historic masonry structures subjected to lateral actions'
<p>This repository contains the data necessary to reproduce the content of the article:</p> <blockquote> <p>A variational rigid-block modelling approach to nonlinear elastic and kinematic analysis of failure mechanisms in historic masonry structures subjected to lateral actions (2021). Earthquake Engineering & Structural Dynamics, 1–23. <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/eqe.3512">https://doi.org/10.1002/eqe.3512</a></p> </blockquote> <p>The file <strong>01_Dataset.zip</strong> contains the dataset. The companion document <strong>00_Dataset_description.pdf </strong>describes the content of the dataset, guiding the analyst to its use in order to (i) reproduce the article's results and (ii) compare the article's results to new results brought by the analyst, e.g. by comparison with other numerical models.</p> <p>Version history</p> <p>v2: updated references in 00_dataset description.pdf </p>
Progressive failure analysis of slip zone soils with strain-softening behavior and implications for landslide triggering mechanisms
<p>All figure for paper entitled “Progressive failure analysis of slip zone soils with strain-softening behavior and implications for landslide triggering mechanisms”</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Safety of Body Composition Analysis in Heart Failure Patients With Implantable Cardioverter Defibrillators (ICDs)
ClinicalTrials.gov study NCT01568606. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Spot Urinary Analysis to Assess Loop Diuretic Efficiency in Stable Heart Failure
ClinicalTrials.gov study NCT02288819. IPD Sharing: NO. Countries: 1. Publications: 3.
Comparative analysis of predictors of failure for high-flow nasal cannula in bronchiolitis
Open the record for dataset details and reuse information.
Data from: Failure modes analysis of electrofluidic display under thermal aging
Dielectric failure and optical switching failure in electro-fluidic display (EFD) is still a bottleneck for sufficient device lifetime. In this study, a dielectric redundancy-designed multilayer insulator of ParyleneC/AF1600X was applied in an EFD device. The reliability performance was systematically studied by tracking the applied voltage dependent leakage current and capacitance changes (I-V & C-V curve) with thermal aging time. The multilayer insulator shows a more stable performance in leakage current compared to that of single-layer insulator. The failure modes during operation underlying the single layer and the multilayer dielectric appear to be different as exemplified by microscopy images. The single layer AFX shows significant detachment. In addition, by quantitatively analyzing the C-V curves with aging time, we find that for the single AFX device, the dominant failure mode is "no-opening" of the pixels. For the muti-layer device, the dominant failure mode is "no-closing" of the pixels. This study provides tools for distinguishing the basic failure modes of an EFD device, and demonstrates a quantitative method for evaluating the reliability performance of the device under thermal aging.
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database
<p><b>Objective:</b> The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized.We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.</p> <p><b>Design: </b>A retrospective cohort study.</p> <p><b>Setting and Participants: </b>Data were extracted from the MIMIC-III database. Data on 1,177 heart failure patients were analysed.</p> <p><strong>Methods</strong>: Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation and validation groups. Independent risk factors for in-hospital mortality were screened using XGBoost and LASSO regression models in the derivation sample. Multivariable logistic regression analysis was used to build prediction models. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.</p> <p><b>Results:</b> Among the 1,177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (P<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.</p> <p><b>Conclusions</b><b>:</b> Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.</p>
Retrospective Observational Analysis of a Cohort With Heart Failure With Preserved Ejection Fraction
ClinicalTrials.gov study NCT04233086. IPD Sharing: NO. Countries: 1. Publications: 10.
Transcranial Direct Current Stimulation (tDCS) Cost-utility-analysis in Medical Care on Depressive Episode With One Drug Therapy Failure.
ClinicalTrials.gov study NCT03758105. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The DTG-SWITCH Study: Longitudinal Analysis of Virologic Failure and Drug Resistance at and After Switching to Dolutegravir-based First-line ART
ClinicalTrials.gov study NCT04612452. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
Nomogram Analysis for HBV Related Acute-on-chronic Liver Failure
ClinicalTrials.gov study NCT03992898. IPD Sharing: NO. Countries: 1. Publications: 12.
Genetic Analysis of Familial Cases of Premature Ovarian Failure
ClinicalTrials.gov study NCT01177891. IPD Sharing: Not stated. Countries: 1. Publications: 23.
A Retrospective Analysis of Failures of Oral Implants
ClinicalTrials.gov study NCT02369562. IPD Sharing: NO. Countries: 1. Publications: 1.
Chronic Heart Failure Analysis and Registry in the Tohoku District 2 (CHART-2 Study)
ClinicalTrials.gov study NCT00418041. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Heart Failure Treatment Pattern Analysis of HF Patients in HF Centers and Non-HF Centers in China
ClinicalTrials.gov study NCT06099028. IPD Sharing: NO. Countries: 1. Publications: 10.
Analysis of Advanced Physiological Ventilatory Parameters During Spontaneous Breathing Effort in Patients with Acute Hypoxemic Respiratory Failure
ClinicalTrials.gov study NCT06490523. IPD Sharing: NO. Countries: 1. Publications: 17.
Failure Analysis of Patellofemoral Arthroplasty
ClinicalTrials.gov study NCT04772625. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
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OpenNeuro
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