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Dynamics of Adatom and Vacancy Islands on Au(111) in Alkaline and Acidic Media
No full text<p>The dataset contains raw STM images and Cyclic Voltammograms that enable the reproduction of the results presented in the study "Dynamics of Adatom and Vacancy Islands on Au(111) in Alkaline and Acidic Media" (https://doi.org/10.1021/acs.jpcc.5c03661).</p>
Graph-based functional and structural resilience assessment of urban drainage networks
No full text<p>This repository contains Python code implementing a physics-guided, graph-based model for resilience assessment of urban drainage networks, as proposed in the paper:</p>
<p><strong><em>“Functional and structural resilience assessment in urban drainage networks: a physics-guided graph-based surrogate model”</em></strong><br><strong>Authors:</strong> Mohammad Rajabi, Mohsen Hajibabaei, Aun Dastgir, Robert Sitzenfrei</p>
<p> <strong>Overview</strong><br>The model computes:</p>
<ul>
<li>
<p>Resilience to <strong>structural</strong> failures</p>
</li>
<li>
<p>Resilience to <strong>functional</strong> failures</p>
</li>
</ul>
<p>It uses a series of <strong>hydraulically informed graph metrics</strong> for flow routing and, finally, computes resilience using graph-based formulas.</p>
<p> </p>
<div>---</div>
<h2> Files and Folders Structure</h2>
<p>├── Functional_resilience.ipynb # Calculate functional resilience under all block rainfall scenarios<br>├── Functional_resilience_dynamic_rain.ipynb # Calculate functional resilience under all Chicago and Euler Type II rainfall scenarios<br>├── Structural_resilience.ipynb # Calculate structural resilience for single-pipe failure scenarios<br>└── input_network/ # Contains the SWMM input file</p>
<p> </p>
<div>---</div>
<div> </div>
<div> </div>
<h2> Contact</h2>
<div>For questions, collaborations, or feedback, please contact: </div>
<div> <a href="mailto:[email protected]">[email protected]</a></div>
<p> </p>
MS_XevoTQXS_2025_January
No full text<table>
<tbody>
<tr>
<td>MAG02AI</td>
</tr>
<tr>
<td>MHO02DH</td>
</tr>
<tr>
<td>RLI01AG</td>
</tr>
<tr>
<td>RLI02AA</td>
</tr>
<tr>
<td>TKI02BC</td>
</tr>
</tbody>
</table>
MS_Eclipse_2025_June
No full text<p><strong>Eclipse + Vanquish Neo </strong> <br>AMP03AC<br>MHO03CS<br>MHO03CT<br>RLI04AB<br>Standard</p>
Graph-based functional and structural resilience assessment of urban drainage networks
No full text<p>This repository contains Python code implementing a physics-guided, graph-based model for resilience assessment of urban drainage networks, as proposed in the paper:</p>
<p><strong><em>“Functional and structural resilience assessment in urban drainage networks: a physics-guided graph-based surrogate model”</em></strong><br><strong>Authors:</strong> Mohammad Rajabi, Mohsen Hajibabaei, Aun Dastgir, Robert Sitzenfrei</p>
<p> <strong>Overview</strong><br>The model computes:</p>
<ul>
<li>
<p>Resilience to <strong>structural</strong> failures</p>
</li>
<li>
<p>Resilience to <strong>functional</strong> failures</p>
</li>
</ul>
<p>It uses a series of <strong>hydraulically informed graph metrics</strong> for flow routing and, finally, computes resilience using graph-based formulas.</p>
<p> </p>
<div>---</div>
<h2> Files and Folders Structure</h2>
<p>├── Functional_resilience.ipynb # Calculate functional resilience under all block rainfall scenarios<br>├── Functional_resilience_dynamic_rain.ipynb # Calculate functional resilience under all Chicago and Euler Type II rainfall scenarios<br>├── Structural_resilience.ipynb # Calculate structural resilience for single-pipe failure scenarios<br>└── input_network/ # Contains the SWMM input file</p>
<p> </p>
<div>---</div>
<div> </div>
<div> </div>
<h2> Contact</h2>
<div>For questions, collaborations, or feedback, please contact: </div>
<div> <a href="mailto:[email protected]">[email protected]</a></div>
<p> </p>
Experimental data of structural tests on non-continuously stiffened steel panels subjected to uniform compression
No full text<p>This dataset consists of data related to the experimental investigations of 50 non-continuously stiffened steel panels subjected to uniform compression (20 open-section, 30 closed-section specimens). The results and discussion associated with this dataset have been published as an open access article in the Journal of Thin-Walled Structures and can be accessed via this link: https://doi.org/10.1016/j.tws.2023.111260</p>
<p> </p>
<p><strong>OVERVIEW:</strong></p>
<ul>
<li>specimen_naming.pdf - Graphical explaination of the the specimen naming</li>
<li><strong>01_overview_results_specimen </strong>- one-page overview of all relevant test results for each specimen</li>
<li><strong>02_tensile_coupon_test</strong> - XLSX-files (including engineering stress-strain and Cauchy stress-strain). <br>
<ul>
<li>open sections: coupon tests for the plates, stiffener webs and stiffener flanges</li>
<li>closed sections: test certificates + random controls (10x plate, 10x stiffener)</li>
</ul>
</li>
<li><strong>03_geometric_imperfection_data</strong> - STL-files (cleaned from outliers and centered to COS).</li>
<li><strong>04_test_result_data </strong>- CSV-files (compare associated paper for placements of LVDTs)</li>
</ul>
MS_Eclipse_2025_May
No full text<p><strong>Eclipse + ICS6000 </strong><br>AMP03AD<br>MHO06AI<br>RLI04AB<br>Standards<br>TKI02BI<br>TKI05AE<br>TKI04BE<br><strong>Eclipse + Vanquish Neo </strong> <br>RLI02AB<br>Standard</p>
Relating thermodynamic quantities of convex-hard-body fluids to the body's shape
No full text<p>Data sets and script files to generate manuscript </p>
<p>Relating thermodynamic quantities of convex-hard-body fluids to the body’s shape</p>
<p>Thomas Franosch, Cristiano De Michele, and Rolf Schilling</p>
<p>Physical Review Research 7, 023260 (2025) </p><p>Make Figures:</p>
<p>Fig1.pdf<br> needs: Fig1.dat <br> gnuplot .\Fig1needs_cut.plt<br> pdflatex .\Fig1needs_cut.tex <br> cut with acrobat professional to Fig1.pdf</p>
<p>Fig2.pdf<br> pdflatex .\Fig2needs_cut.tex<br> cut with acrobat professional to Fig2.pdf</p>
<p>Fig3.pdf <br> needs: <br> Effective_Potential_Final.nb<br> Effective_Potential_Final.dat<br> Effective_Potential_infinity.dat<br> veff_MC_11.dat<br> veff_MC_30.dat<br> gnuplot .\Fig3.plt<br> pdflatex .\Fig3.tex </p>
<p>Fig4.pdf<br> pdflatex .\Fig4needs_cut.tex <br> cut with acrobat professional to Fig4.pdf</p>
<p>Fig5.pdf<br> needs:<br> Z_1D_X11.dat<br> Z_1D_X12.dat<br> Z_1D_X15.dat<br> gnuplot .\Fig5.plt<br> pdflatex .\Fig5.tex</p>
<p>Fig6.pdf<br> needs:<br> ZovZ0_1D_X11.dat<br> ZovZ0_1D_X12.dat<br> ZovZ0_1D_X15.dat<br> gnuplot .\Fig6.plt<br> pdflatex .\Fig6.tex</p>
<p>Fig7.pdf<br> needs:<br> Z_2D_X11.dat<br> Z_2D_X12.dat<br> Z_2D_X15.dat<br> gnuplot .\Fig7.plt<br> pdflatex .\Fig7.tex</p>
<p>Fig8.pdf<br> needs:<br> Derivative_compressibility.dat<br> Z_2D_X11.dat<br> Z_2D_X12.dat<br> Z_2D_X15.dat <br> gnuplot .\Fig8.plt<br> pdflatex .\Fig8.tex</p>
<p>Fig9.pdf<br> needs:<br> ns_vs_X0.dat<br> gnuplot .\Fig9.plt<br> pdflatex .\Fig9.tex</p>
<p>Fig10.pdf<br> needs: <br> Fig10_domain.nb<br> Fig10_domain.pdf<br> pdflatex Fig10_needs_cutting.tex<br> cut with acrobat professional to Fig10.pdf</p>
<p>Fig11.pdf <br> pdflatex Fig11_needs_cutting.tex<br> cut with acrobat professional to Fig10.pdf</p>
<p> </p>
Dataset for Stegner et al. 2025: Differences in freezing dynamics in the tip and base of wheat (Triticum aestivum L.) leaves result in a difference in cold hardiness. Plant Stress
No full textRun-and-Tumble Particles Learning Chemotaxis
No full text<p>Through evolution, bacteria have developed the ability to perform chemotactic motion in order to find nourishment. By adopting a machine learning approach, we aim to understand how this behavior arises. We consider run-and-tumble agents able to tune the instantaneous probability of switching between the run and the tumble phase. When such agents are navigating in an environment characterized by a concentration field pointing towards a circular target, we investigate how a chemotactic strategy may be learned starting from unbiased run-and-tumble dynamics. We compare the learning performances of agents that sense only the instantaneous concentration with those of agents having a short-term memory that allows them to perform temporal comparisons. While both types of learning agents develop successful target-search policies, we demonstrate that those achieved by agents endowed with temporal comparison abilities are significantly more efficient, particularly when the<br>initial distance from the target is large. Finally, we also show that when an additional length scale is imposed, for example by fixing the initial distance to the target, the learning agents can leverage this information to further improve their efficiency in locating the target.</p>
<p>Codes and scripts necessary to reproduce data contained in the manuscript</p>