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Sequential Bayesian Inference of Finite-strain Visco-elastic Visco-plastic model parameters of 22-month aged PA12 bulk material printed along different directions

These are the data related to aged PA12 (22 months) following the methodology described in the following publication in which non-aged PA12 has been tested: title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.", journal = "International Journal of Solids and Structures", year = "2023", volume = "283", pages = "112470", doi = "10.1016/j.ijsolstr.2023.112470", author = "Wu, Ling and Anglade, Cyrielle and Cobian, Lucia and Monclus, Miguel and Segurado, Javier and Karayagiz, Fatma and Freitas, Ubiratan and Noels Ludovic" Contrarily to the non-aged material, since high-strain-rate tests are not available, only 5 Maxwell's branches are considered herein. <h1>Description</h1> BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a> The sequential BI is described in [WU23] The experimental protocol is reported in [COB22,COB22b] but is herein applied on aged PA12 To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers: <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: <a href="https://doi.org/10.1016/j.ijsolstr.2023.112470" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.ijsolstr.2023.112470</a></li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: <a href="https://doi.org/10.1016/j.polymertesting.2022.107556" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.polymertesting.2022.107556</a> (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” <a href="http://dx.doi.org/10.5281/zenodo.6136935" target="_blank" rel="nofollow noreferrer noopener">http://dx.doi.org/10.5281/zenodo.6136935</a> (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; <a href="https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008" target="_blank" rel="nofollow noreferrer noopener">https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008</a>, Open access: <a href="https://orbi.uliege.be/handle/2268/197898" target="_blank" rel="nofollow noreferrer noopener">https://orbi.uliege.be/handle/2268/197898</a></li> </ul> <h1>Directories</h1> All the codes and experimental results are in three directories: <ol> <li>Experiment_PA12_AGED: experimental data of aged material, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters 2.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range 2.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI. 2.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py 2.1.3. Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI 2.2. VE_V and VE_H: BI for viscoelastic properties of "V" specimen (VE_V) and "H" specimen (VE_H) 2.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat 2.2.2. WarmStart = True is used to restart an inference 2.2.3. MCMC_VE_....dat in the VE_V and VE_H directories are the BI results 2.3. CheckBayRes: to visualize predictions of a BI sample and experimental curves 2.3.1. MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line 7) 2.3.2. ResKGEmu.py plots the evolution of elastic properties with time 2.3.3. uses as input VE_V/MCMC_VE_....dat or VE_H/MCMC_VE_....dat 2.3.4. uses local ViscoElasticTest.py, line.geo, line. msh as interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code 2.3.5. uses local functions plotExp.py 2.4. ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V and VE_H to call the VEVP model</li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters 3.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges 3.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage. 3.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py 3.1.3. Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI 3.2. VP_V and VP_H: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V) and "H" specimen (VP_H) 3.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat 3.2.2. WarmStart = True is used to restart an inference 3.2.3. It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?1Step.dat (? being H or V) 3.3. CheckBayRes: visualize predictions of a BI sample and experimental curves 3.3.1. MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples (inclusing MAP, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP?/MCMC_VP_?1Step.dat (? being H or V) 3.3.2. plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP?/MCMC_VP_?1Step.dat (? being H or V) 3.3.3. Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP?/MCMC_VP_?_1Step.dat (? being H or V) 3.3.4. ResKGEmu.py plots the evolution of elastic properties with time 3.4. VEVPTest.py: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> <h1>Figures (reference to the number in [WU23] but for aged PA12)</h1> <ul> <li>Fig. 5 (Selected observations): From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,14,18,22,23,24,25]</li> <li>Fig. 8 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 15B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 16B: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 19B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> </ul>   This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015.

Quantum Software Engineering: Roadmap and Challenges Ahead - Supplementary material

This supplementary material corresponds to the study "Quantum Software Engineering: Roadmap and Challenges Ahead" for the Special Issue Roadmap to 2030 by the ACM Transactions on Software Engineering and Methodology (TOSEM). In this supplementary material you will find the papers found in the search for the terms "Quantum Software Engineering" in the bibliographic databases of Scopus and Google Scholar, as well as their evolution since 2004.

Quantification of gamma radiation exposure and radon/thoron exhalation rates in representative building materials in Ireland

Data, models, mapsa nd publication produced during the postdoctoral fellowship: EPSPD/2022/141

Radio and gamma-ray timing of TRAPUM L-band Fermi pulsar survey discoveries - Supplementary material

Timing ephemerides for the redback pulsar J1803-6707 (published in "Radio and gamma-ray timing of TRAPUM L-band \textit{Fermi} pulsar survey discoveries", Burgay et al 2024, A&A) covering 15 years of Fermi data. The ephemeris are provided both in "ORBIFUNC" (to be used with timing software <a href="https://bitbucket.org/psrsoft/tempo2/src/master/" target="_blank" rel="noopener">tempo2</a>) and "ORBWAVES" (with <a href="https://github.com/nanograv/PINT" target="_blank" rel="noopener">PINT</a>) formats. The ORBIFUNC parameters are a time-domain interpolating function describing the orbital phase variations over time, while the ORBWAVES parameters are the Fourier-domain representation of the same function.

Supplementary material for "Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion"

This Zenodo archive contains supplementary data and code for the paper Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion (submitted to Geophysical Journal International). The archive provides scripts and data to reproduce some of the figures and results presented in the paper / supplementary material.

Refractory materials used in the numerical model of an aluminothermic reduction vessel

This constitutes underlying data for a publication titled "Numerical Model of Aluminothermic Reduction Vessel Preheating and Charge Heating with Graphite Heating Rods" by S. Semenov, P. Namy, A. Kale, S. Tsebe.

Refractory materials used in the numerical model of TBRC

This constitutes underlying data for a publication titled "Numerical Model of a Top-Blown Rotary Converter Preheating and Charge Heating with an Oxy-Fuel Burner" by S. Semenov, P. Namy, A. Kale, S. Tsebe.

Supporting Material of D2.1: Analysis of the OSTrails Interoperability Framework Tools Compliance

This folder contains the per-tool analyses, tool flashcards, master table, and tool onboarding document that support D2.1 findings.

Supplementary materials for User Acceptance Factors of Usage-Based Insurance

Overview This is supplementary material for the paper User Acceptance Factors of Usage-Based Insurance. Considering that the term UBI might not be well known to the general public, we added the word “car” to the original term, referring to it in survey, video, and data analysis as Usage-Based Car Insurance (UBCI). What is included: <ul> <li>01-online-survey.pdf - questionnaire used in the user study.</li> <li>02-ubci-video-explanations.mp4 - a video with an explanation about Usage-Based Car Insurance, which we provided to participants in our user study.</li> <li>03-SEM_Iteration-1-standardized.pdf - SEM analysis of the theoretical model, including the standardized coefficient.</li> <li>04-SEM_Iteration-1-unstandardized.pdf - SEM analysis of the theoretical model, including the unstandardized coefficient.</li> <li>05-SEM_Iteration-22-standardized.pdf - SEM analysis of the refined model, including the standardized coefficient.</li> <li>06-SEM_Iteration-22-unstandardized.pdf - SEM analysis of the refined model, including the unstandardized coefficient.</li> <li>07-linear-regression.pdf - moderators analysis.</li> </ul>

Dataset for publication High-capacitance BiPO4 material with monoclinic-hexagonal crystalline phase heterostructure for aqueous asymmetric supercapacitors

The dataset contains all relevant data and figures regarding the manuscript "High-capacitance BiPO4 material with monoclinic/hexagonal crystalline phase heterostructure for aqueous asymmetric supercapacitors". All Figures are in tiff format and all relevant data are in csv formats.  The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.).  Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.

Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"

This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically: <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>

Supplementary material for "Quantitative morphological analysis of skulls of pterosaurs and early birds highlight a functional shift during the Mesozoic" by Gauweiler J. et al.

This data repository contains additional information and supplemental material for the publication "Quantitative morphological analysis of skulls of pterosaurs and early birds highlight a functional shift during the Mesozoic". For information on the authors, see the original publication. The folder "Prehistoric_Ornithodira_BMPs.zip" contains outline images in .bmp format of all skulls used for the analysis. The file "Groups.csv" contains the grouping variables for all data points. The file "PC_Scores.csv" contaisn the principal component scores of the SHAPE analysis for all individuals used in the analysis. The file "Pterosaur_Avian_Script.R" contaisn the R code used for statistical analysis and plotting of the data.

Accompanying data for the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification"

Experimental Data accompanying the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ijimpeng.2024.105083" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijimpeng.2024.105083</a> One (holed and double notched) specimen is dynamically loaded via an hydraulic tensile test machine (MTS-819, 20 kN) at 5 m/s. The specimen were cut from a 0.8 mm-thick DC04 (XES French standards) sheet in the rolling direction provided by ONERA. Reference image is captured using a high definition camera (29 Mpix, Prosilica GT from Stemmer) combined with the same objective lens than the one used for experiments. Deformed sample images are captured using the rotating mirror Ultra-high speed (HR-UHS) Cordin camera Model 580 at 68 kfps with a resolution of 3296 x 2472 pixels. The field of view is 35.8mm x 47.9mm leading to a pixel size of 14.49um. Ufreckles <a href="https://doi.org/10.5281/zenodo.1433776">10.5281/zenodo.1433775</a> is used to perform FE-based DIC using T3P1 linear triangular elements and a Tikhonov regularisation (over 3 elements). Eventually, kinematic data and load measurement  are used to identify stress fields via Data-Driven stress Identification (DDI) method. Are provided: <ul> <li>raw images, camera distortion modes and parameters, load net force and timeline</li> <li>kinematic fields obtained from Digital Image Correlation</li> <li>Stress fields identified using Data-Driven stress Identification</li> </ul> Matlab Codes to produce results (working with Ufreckles) <ul> <li>MultiSensor_DIC_script.m: in /Codes/ is the main script to run DIC</li> <li>Shape functions: in /Codes/shape_functions/ containing Zernike polynomial shape functions and deconvolution algorithm to get effective displacement from total displacement knowing camera distortions</li> </ul>

opencc-by-4.0Aug 2024View details →

An analysis of peak fitting in reference material spectra for calibration of Raman spectroscopy instruments (Dataset)

tbd NeXus format

opencc-by-4.0Jun 2024View details →

Supplementary Material for the paper entitled "Identifying Difficult Environmental Conditions with Scenario-based Hazard and Fault Analysis"

This dataset is a supplementary material for the paper entitled "Identifying Difficult Environmental Conditions with Scenario-based Hazard and Fault Analysis", accepted by SafeComp Workshop SASSUR 2024. The file will be uploaded after a publication process is accomplished. Update: list of triggering conditions is uploaded on 14.10.2024

opencc-by-4.0May 2024View details →

Supplementary Materials S1-S4 to Leipe et al., 2024. Human activities, early farming and natural environment in the north-western Kanto Plain (Central Japan) during the Final Jomon–Early Kofun period (990 cal BCE–330 cal CE) inferred from palynological and archaeobotanical records

This dataset represents the Supplementary Materials S1-S4 to Leipe et al. (2024), QEH 2, 100030. Leipe et al., 2024. Human activities, early farming and natural environment in the north-western Kanto Plain (Central Japan) during the Final Jomon–Early Kofun period (990 cal BCE–330 cal CE) inferred from palynological and archaeobotanical records. Quaternary Environments and Humans 2, 100030 (doi: <a href="https://doi.org/10.1016/j.qeh.2024.100030">https://doi.org/10.1016/j.qeh.2024.100030</a>). Supplementary Material S1: Pollen and non-pollen palynomorph records of a section (490.5-668.5 cm depth) of the MJ20 sediment core from Morinji Marsh, Tatebayashi City, Gunma Prefecture, Kanto region, Central Japan. Supplementary Material S2. Sample-specific total counts of macrobotanical (seeds and fruits) and fungal remains and floated litres of sediment samples from ash pits (Yayoi period) and hearths (Kofun period) of the Ikegami archaeological site, Kumagaya City, Saitama Prefecture, Kanto region, Central Japan. Supplementary Material S3. Photographs of charred remains of Juglans ailantifolia and Aesculus turbinata from the Middle Yayoi period ash pit samples at the Ikegami archaeological site, Kumagaya City, Saitama Prefecture, Kanto region, Central Japan. Supplementary Material S4. Description of the As-B tephra in the MJ20 sediment core from Morinji Marsh, Tatebayashi City, Gunma Prefecture, Kanto region, Central Japan.

Material Suplementar ERES 2024: Especificação de Requisitos de um Software de Apoio à Enfermagem no Tratamento de Lesão por Pressão: Um Relato de Experiência

Material Suplementar ERES 2024: Especificação de Requisitos de um Software de Apoio à Enfermagem no Tratamento de Lesão por Pressão: Um Relato de Experiência.

Modeling toroidal currents in the ionospheric electrojet regions (Supplemental Material)

This is the supplementary material for the paper "Modeling toroidal currents in the ionospheric electrojet regions". A README file inside the archive contains further information.

opencc-by-4.0Mar 2024View details →

Properties of selected alkali-activated materials for sustainable development

The presented research focuses on three selected variants of alkali-activated materials, where the goal is to compare key properties from the point of view of material engineering and structural design. Tests of the mechanical properties of the examined materials are carried out and their durability is compared, namely frost resistance, resistance to chemical and de-icing substances and resistance to elevated temperature.

Supplementary materials for "High-Throughput Discovery of Substrate Peptide Sequences for E3 Ubiquitin Ligases Using a cDNA Display Method."

The next-generation sequencing (NGS) data of the 5th rounds' samples for LX9 library and p53deg library. The csv files contain DNA sequences read out, amino acid sequences and their read counts in descending order.