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openmit-licenseMar 2025View details →

Systems-level design principles of metabolic rewiring in an animal

Description: This repository contains all source codes, necessary input and output data, and raw figures and tables for reproducing all figures and results published in the following study:  Xuhang Li#, Hefei Zhang#, Thomas Hodder, Wen Wang, L. Safak Yilmaz, Chad L. Myers, Albertha J.M. Walhout. Systems-level Worm Perturb-Seq reveals design principles of metabolic rewiring. (2025) Nature, in press (# equal contribution) Files: This repository contains the entire project working directory for this publication. To deposit into Zenodo, we have individually zipped each subfolder of the root directory. To reproduce our study, it is advised that one should download the entire Zenodo repository and unzip every zip file to produce the corresponding subfolders. The "root.zip" should be directly unzipped into the root directory of this project (not your system root!!).  The recovered working directory should be like this:  <ul> <li>10_revision/</li> <li>1_QC_dataCleaning/</li> <li>2_DE/</li> <li>3_imageAnalysis/</li> <li>4_networkAnalysis/</li> <li>5_GSA/</li> <li>7_FBA_modeling/</li> <li>data_and_tables_to_publish/</li> <li>input_data/</li> <li>generate_all_tables_for_publication.R</li> <li>prepareTbls_met12.R</li> <li>prepareTbls_metVali1_OCC.R</li> <li>prepareTbls_metVali2_b12.R</li> <li>prepareTbls_metVali3_detox.R</li> <li>prepareTbls.R</li> <li>prepareTbls_SPECIAL.R</li> <li>REWIRING_FIGURE_LOOKUP.xlsx</li> </ul> Please be advised that this repository contains raw codes and data that are not directly related to a figure in our paper. However, they may be useful to generate input used in the analysis of a figure, or to reproduce tables in our manuscript. It may also contain unpublished analyses and figures, which we did not intentionally delete and kept for records.  Usage:  Please refer to the table in below to locate a specific file for reproducing a figure of interest (also availabe in the REWIRING_FIGURE_LOOKUP.xlsx under the root directory).  <table> <tbody> <tr> <td>Figure</td> <td>File</td> <td>Linesa</td> <td>Notes</td> </tr> <tr> <td>Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-190</td> <td> </td> </tr> <tr> <td>Fig. 1c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-83</td> <td> </td> </tr> <tr> <td>Fig. 1d</td> <td>11_additional analysis/special RNAi.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 1e</td> <td>2_DE/output/metabolic_GRN_layout2.cys</td> <td>open in Cytoscape</td> </tr> <tr> <td>Fig. 1f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-71</td> <td> </td> </tr> <tr> <td>Fig. 1g</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-145</td> <td> </td> </tr> <tr> <td>Fig. 2a</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>1-235</td> <td> </td> </tr> <tr> <td>Fig. 2b</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>232-287</td> <td> </td> </tr> <tr> <td>Fig. 2c</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Fig. 2d</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-181</td> <td> </td> </tr> <tr> <td>Fig. 2e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-338</td> <td> </td> </tr> <tr> <td>Fig. 2f</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 3b</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2539</td> <td> </td> </tr> <tr> <td>Fig. 3c</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Fig. 3d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2183-2235</td> <td> </td> </tr> <tr> <td>Fig. 3e</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>1751-2159</td> <td> </td> </tr> <tr> <td>Fig. 3g</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Fig. 3h</td> <td>11_additional analysis/glycine tracing/glycine RNAi tracing.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Fig. 3j</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Fig. 3k</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>853-881</td> <td>run lines 1-100 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/CR_model_final_run/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Fig. 4b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_gspd_1_example_objHeatmap.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 4c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Fig. 4d</td> <td>7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randFBA.R, 7_FBA_modeling/CR_model_final_run/3_DEG_modeling_edge_direction_test_randGRN.R</td> <td>entire file</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" or "fitted_CR_interaction_diagram_randGRN.pdf" to locate the code for making the figure (two randomizations, respectively), and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all four files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 4f</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-154</td> <td>numbers are in variable "model_explained"</td> </tr> <tr> <td>Fig. 4g (left)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 342-345</td> </tr> <tr> <td>Fig. 4g (right)</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Fig. 5a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Fig. 5b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/3_DEG_modeling_edge_direction_test_randFBA.R</td> <td>entire file and see notes</td> <td>Reproducing this figure is complex because it integrates multiple randomization results; search "fitted_CR_interaction_diagram.pdf" to locate the code for making the figure, and track up for a few lines to load the dependency. To fully reproduce the analysis, follow the instructions in the Github and you will need to execute all two files named "3_DEG_modeling_edge_*.R".</td> </tr> <tr> <td>Fig. 5c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 343-346</td> </tr> <tr> <td>Fig. 5d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_DEG_cutoff_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1a</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>193-219</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1b</td> <td>1_QC_dataCleaning/5_supplementary_figure_rewiring.R</td> <td>1-91</td> <td>the published figure was remade manually for prettier representations, the plotting function for which is not provided in this code. However, the raw data stays the same.</td> </tr> <tr> <td>Extended Data Fig. 1c</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>280-379</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1d</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1e</td> <td>4_networkAnalysis/s2_rewiring_and_network_GPR.R</td> <td>387-555</td> <td>some inputs were generated by "4_networkAnalysis/s2_rewiring_and_network_GPR.m"</td> </tr> <tr> <td>Extended Data Fig. 1f</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-257</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1g</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>170-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1h</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 1i</td> <td>11_additional analysis/Wormsize/plot for publishing.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-61</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-180</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2c</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-152</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2d</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-397</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2e</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>364-383</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2f</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>1-264</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 2g</td> <td>5_GSA/1_DE_annotation_pipeline.R</td> <td>1389-1522</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 3</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>468-765</td> <td>For producing the embedding, see "2_DE/clustering_python/cluster_RNAi_UMAP_DBSCAN.ipynb"</td> </tr> <tr> <td>Extended Data Fig. 4a</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>1-254</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4b</td> <td>4_networkAnalysis/s3_rewiring_and_network_connections.R</td> <td>258-303</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4c</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-283</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4d</td> <td>2_DE/SUPP_extra_figures_for_rewiring.R</td> <td>725-813</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4e</td> <td>2_DE/5_2_coexpression_analysis_direct_tree.R</td> <td>136-589</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 4f</td> <td>11_additional analysis/regulators_nhr_sbp_tor_GEP_mGRN.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5a</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 5b</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-92</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6a</td> <td>2_DE/6_1_biCluster_heatmaps_PCCtree.R (top row); 2_DE/6_2_rewiring_summary_map_PCCtree.R (bottom row)</td> <td>top row: 210-219; 229-238; bottom row: 1-162; 162-229</td> <td>These two scripts produced the last two figures in the first row and the first two figures in the second row. The remaining two figures are repetitive with other main/extended data figures, whose source code were specified there</td> </tr> <tr> <td>Extended Data Fig. 6b</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>1-184</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6c</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>189-319</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 6d</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>326-480</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 7a-e</td> <td>2_DE/6_3_scatter_plot_for_each_DEG_cluster_PCCtree.R</td> <td>1-189</td> <td>The annotation bar plot is in 2_DE/SUPP_extra_figures_for_rewiring.R lines 394-715</td> </tr> <tr> <td>Extended Data Fig. 8a</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-753</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8b</td> <td>2_DE/3_1_DE_network_analysis.R</td> <td>649-785</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8c</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>759-764</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 8d</td> <td>2_DE/4_2_pathway_level_analysis.R</td> <td>2247-2506</td> <td>the published figure uses level 4 and up direction (2_DE/figures/1_pathway_level_analysis/rxn_and_pathway_rewiring_LEVEL4_up.pdf)</td> </tr> <tr> <td>Extended Data Fig. 8e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>803-807</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8f</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>870-874</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 8g</td> <td>11_additional analysis/uPhe tracing/uphe RNAi.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9a</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>1-601</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9b</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>691-714</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9c</td> <td>11_additional analysis/uGlucose tracing/uGlu rewiring.R</td> <td>entire file</td> <td>source data also provided along the paper</td> </tr> <tr> <td>Extended Data Fig. 9d</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>607-629</td> <td>please load the dependency starting from line 1</td> </tr> <tr> <td>Extended Data Fig. 9e</td> <td>4_networkAnalysis/2_visualize_example_rewirings.R</td> <td>636-685</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 9f</td> <td>4_networkAnalysis/1_rewiring_network_modules.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 10a</td> <td>2_DE/6_2_rewiring_summary_map_PCCtree.R</td> <td>entire file</td> <td>This figure is a subset of fig. 2f</td> </tr> <tr> <td>Extended Data Fig. 10c</td> <td>7_FBA_modeling/REVISION/1_cluster_CR_model.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11a</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_excludeMultiObjGenes.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11b</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-225</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 11c</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-340</td> </tr> <tr> <td>Extended Data Fig. 11d</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 352-355</td> </tr> <tr> <td>Extended Data Fig. 11e</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 362-365</td> </tr> <tr> <td>Extended Data Fig. 11f</td> <td>10_revision/2_tissue_expression_analysis.R</td> <td>486-742</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12a</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>300-329</td> <td>run lines 1-101 to load dependency; Euler plot depends on a random seeds; we manually picked one output figure that has best visual representations; see the corresponding Github or "7_FBA_modeling/REVISION/human_perturb_seq/a1_gene_obj_classification.m" for generating the input matrix (core function matrix)</td> </tr> <tr> <td>Extended Data Fig. 12b</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/1_EDA_of_human_perturb_seq.R</td> <td>1-176</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12c</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12d</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_basic_CR_model.R</td> <td>1-297</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12e</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>1-224</td> <td> </td> </tr> <tr> <td>Extended Data Fig. 12f</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 337-341</td> </tr> <tr> <td>Extended Data Fig. 12g</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 353-356</td> </tr> <tr> <td>Extended Data Fig. 12h</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_fused_with_FBA.R</td> <td>entire file</td> <td>corresponding figure produced by lines 363-366</td> </tr> <tr> <td>Extended Data Fig. 12i</td> <td>7_FBA_modeling/REVISION/human_perturb_seq/2_DEG_modeling_supp_parameter_sensitivity_fused_with_FBA.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2a</td> <td>2_DE/5_1_1_DE_similarity_analysis.R</td> <td>467-572</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2b</td> <td>2_DE/6_4_Wald_heatmap_each_RNAi_Cluster.R</td> <td>entire file</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2c</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-389</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2d</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-308</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2e</td> <td>2_DE/5_1_2_DE_cluster_justification.R</td> <td>1-234</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 2f</td> <td>12_FLEX_benchmark/run_FLEX_eval.sh</td> <td>entire file</td> <td>the bash scripts calls corresponding R scripts for reproducing the figures. You will need to install the FLEX package to run these codes. </td> </tr> <tr> <td>Supplementary Data Fig. 3, 4</td> <td>N/A</td> <td>N/A</td> <td>No coding involved as these figures were made manually</td> </tr> <tr> <td>Supplementary Data Fig. 5</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>1-270</td> <td> </td> </tr> <tr> <td>Supplementary Data Fig. 6</td> <td>7_FBA_modeling/CR_model_final_run/2_DEG_modeling_basic_CR_model.R</td> <td>274-578</td> <td>please load the dependency starting from line 1</td> </tr> </tbody> </table> a: The lines indicate the chunk of codes to reproduce the corresponding figure; The figure is reproduced at the end of the referred codes. Please note that you may have to run the codes above the referred chunk (i.e., from the first line) to load dependent variables to execute the referred codes. However, you should be able to reproduce the figure only with the codes within the referred script.  Notice: We advise you download the entire project working directory (including all zip files and unzip them into corresponding folders), for reproducing any analysis. If you only download the zip file relevant to your figure of interest, you may or may not run into issues due to the missing files in another folder.   Contact: For any questions, please contact Xuhang (Hang) Li at Xuhang.Li@umassmed.edu. (we plan to deposit an updated version with better code annotations, but cannot complete that currently due to time restirctions)

opencc-by-4.0Apr 2021View details →

Systematic Evaluation and Usability Analysis of Formal Tools for Railway System Design - Technical Annexes

This package includes additional data for the paper ``Systematic Evaluation and Usability Analysis of Formal Methods Tools for Railway Signalig System Design'', by Alessio Ferrari, Franco Mazzanti, Davide Basile, and Maurice ter Beek, CNR-ISTI, Italy, accepted for publication in the IEEE Transactions on Software Engineering, DOI: 10.1109/TSE.2021.3124677 The paper concerns the systematic evaluation and usability analysis of 14 formal tools for system design, namely CADP (2020-g), FDR4(4.2.7), NuSMV(1.1.1), ProB(1.9.3), Atelier B (4.5.1), Simulink (R2020a), SPIN (6.4.9), UMC (4.8), UPPAAL (4.1.4), mCLR2 (202006.0), SAL (3.3), TLA+ (2) and CPN Tools (4.0). The current package includes the following content: <ol> <li>Tool Evaluation Template and .pdf: a document including the reference evaluation template, and the evaluation sheet of each tool. </li> <li>Tool Evaluation Table.xlsx: a table summarizing the results of the evaluation.</li> <li>System Usability Test - SUS Results.xlsx: an excel file with multiple sheets with all the raw results of the usability test for the tools.</li> </ol>

opencc-by-4.0Jun 2022View details →

Optimal elevated agrivoltaic system design and key performance indicators across Europe based on three crop light levels

Optimal elevated (stilted) agrivoltaic system design (PV coverage ratio) is given on a European gridded level (25km grid and NUTS3 regions) based on three light levels: shade-loving crops (daily light integral (DLI) of 12 mol/m²day), shade-tolerant crops (DLI of 12 mol/m²day) and shade-intolerant crops (DLI of 25 mol/m²day) Estimations of other performance indicators are given: power capacity (kWp/ha), energy production (MWh/ha), levelized cost of electricity (€/MWh) and land equivalent ratio (LER -). The assumptions and methodology of this dataset can be found in the article "Geospatial assessment of elevated agrivoltaics on arable land in Europe to highlight the implications on design, land use and economic level." Interactive maps can be found on https://iiw.kuleuven.be/apps/agrivoltaics/maps.html

opencc-by-4.0Oct 2022View details →

Effect of different system parameters on the design of the EU DEMO vacuum vessel pressure suppression system (dataset)

Set of design maps for the EU DEMO VVPSS, for each: <ul> <li>VVPSS size</li> <li>Number of VVPSS connections</li> <li>Initial VVPSS temperature</li> </ul> reporting peak and equilibrium pressure in the VV following an in-vessel LOCA initiated by a double-ended guillotine break of the largest feeding pipe. Model details reported in publication A. Froio and I. Moscato, Effect of different system parameters on the design of the EU DEMO vacuum vessel pressure suppression system, submitted to Fusion Engineering and Design. The README.txt file has been written according to the Dubline Core Standard for Metadata (https://www.dublincore.org/).

opencc-by-4.0Apr 2024View details →

Dataset for: Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies

Dataset containing electronic, mechical, firmware, and software design files associated with the article:  "Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies" By R. Barmak, D. N. Hofstadler, M. Stefanec, L. Piotet, R. Cherfan, T. Schmickl, F. Mondada, and R. Mills. EPFL, Switzerland and Univeristy of Graz, Austria. IEEE Access, 2024, Vol 12, pp 50849-50871. doi: 10.1109/ACCESS.2024.3385658 <a href="https://doi.org/10.1109/ACCESS.2024.3385658">https://doi.org/10.1109/ACCESS.2024.3385658</a>   <table> <tbody> <tr> <td>File name</td> <td>Description</td> </tr> <tr> <td>1_hw_pcb_schematics_rev2.pdf</td> <td>Electrical schematic of the robotic frame</td> </tr> <tr> <td>2_hw_pcb_stackup_rev2.pdf  </td> <td>Technical specifications for the robotic frame PCB manufacturing</td> </tr> <tr> <td>3_hw_pcb_bom_rev2.pdf </td> <td>Electronics Bill of Materials (BoM)</td> </tr> <tr> <td>4_hw_pcb_gerber_rev2.zip  </td> <td>Robotic frame PCB manufacturing files (gerbers)</td> </tr> <tr> <td>5_hw_pcb_altium_rev2.zip</td> <td>Altium Designer project files</td> </tr> <tr> <td>6_hw_mechanical_rev2.zip</td> <td>DXF and STEP files of the mechanical structure of the robotic frame</td> </tr> <tr> <td>7_sw_firmware_rev2.zip</td> <td>Firmware source code and compiled binaries for STM32 microcontroller</td> </tr> <tr> <td>8_sw_handlers-1.0.1.zip</td> <td>Software for high-level interface to robot from a host device </td> </tr> </tbody> </table>

openmit-licenseSep 2022View details →

Design Methodologies and Engineering Applications for Ecosystem Biomimicry: An Interdisciplinary Review Spanning Cyber, Physical, and Cyber-Physical Systems

This is the data for the interdisciplinary review on ecosystem biomimicry for engineering applications.

opencc-by-4.0Aug 2023View details →

Domain-Driven Design for Microservices Architecture Systems Development: A Systematic Mapping Study

This repository contains all artifacts related to the study: Domain-Driven Design for Microservices Architecture Systems Development: A Systematic Mapping Study

opencc-by-4.0Jan 2017View details →

System Call Logs with Natural Random Faults: Experimental Design and Application

In this work we present a large dataset for use in embedded systems research which has been gathered from a realistic development environment operating in the path of an accelerated neutron beam. The dataset contains traces with events from a real-time operating system as as user events from a safety critical application. All data is carefully timestamped and in human understandable form.

opencc-by-4.0Dec 2023View details →

Supplementary Material for "Aiding the Design of Critical Software Systems by Iterative Exploration of Distinct Requirement Violation Scenarios"

This dataset provides artifacts about an industrial case study of a Steer-by-Wire system. It collects models of the system modeled in the open-source Gamma Statechart Composition Framework. You can find more information about the framework here: <a href="https://github.com/ftsrg/gamma">https://github.com/ftsrg/gamma</a>.

opencc-by-4.0Nov 2021View details →

Dataset for "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"

This package provides all published resources used and produced in the context of the research study leading to the article "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs", presented in ICSA 2022's technical track.

opencc-by-4.0Apr 2010View details →

Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

Figure 4 shows the block diagram of the software which runs in the robot’s main processor. The program consists of 4 main blocks: • Hardware Interface: Contains all low level routines to access hardware of the robot including sensors and actuators. • Vision: Contains image processing algorithms such as recognition of landmarks and other object. Self localization is done using particle filtering. Particles are scored by comparing a simulated image from each particle with the current frame captured by camera. Using “Sampling-Importance Resampling” method, a new distribution of the particles is created after each step. Particles are also updated using a motion model. Final distribution of the particles converges to the real pose of the robot. • Planning: Planning system of the robot is based on a multi layer, and multi thread structure. The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario which runs in parallel with the scenarios in the other layers. A scenario in a higher level can terminate and change the scenario running in the lower level; however it is usually done in synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as stopping the walking motion while one of the feet is still in the air). • Network: Mainly responsible for the wireless communication of the robot with the other robots or the referee box. This is done via WLAN. • Motion Control: manages all the actuators of the robot, and controls locomotion or any other action of the robot according to the requests from Cognition. • Sensor Control: manages other sensors, and interacts with the Sub-Controller.

opencc-by-4.0Apr 2010View details →

Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

The PERSIA Humanoid robot designed for has multipurpose capability. This robot equipped with main board for motion control, vision sensor, other balancing sensors, servo motors and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control system.

opencc-by-4.0Nov 2011View details →

Figure 7. The outcomes of the Monitoring and Control of the Greenhouse soil and climate Conditions for tomato crops-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

In the past generation greenhouses it was enough to have one cabled measurement point in the middle to provide the information to the greenhouse automation system. The system itself was usually simple without opportunities to control locally heating, lights, ventilation or some other activity, which was affecting the greenhouse interior climate. The optimal greenhouse climate and soil adjustment can enable us to improve productivity and to achieve remarkable energy savings. In this paper we proposed a multi-agent methodology for integrated management systems in greenhouses. In this regards wireless sensor networks play a vital role to monitor greenhouse and environment parameters. Each controlled process of the greenhouse environment is modeled as an autonomous agent with its own inputs, its own outputs and its own interactions with the other agents. Each agent acts autonomously, as it knows a priori the desired environmental set-points. In this way, any possible conflicting decisions of conventional environmental control methodologies are resolved through negotiations between the agents so that the possible optimal integrated solution is achieved. The developed system is simple, cost effective, and easily installable.

Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The complete agents “environment” is shown in figure 6. Each agent receives the necessary environmental or plant condition measurements. While each agent keeps its autonomy and has its own “personal” goals, it interacts with the other agents through some specific communication language, in the “Agent discussion area”, where the overall goal is taken into account. This interaction takes place under the presence of information from the existing models and the target goals. At that point, each agent has decided on its best strategy.

Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

Figure 2 illustrates how the sensor nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a better understanding of the microclimate layers which typically exist in the greenhouse, and to figure out what kind of differences occur in the climate between lower and upper flora.

Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The negotiations between agents are subject to optimization based on “knowledge” that is derived from complete production models, yield models or even sparse models as expressed in fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models provide additional information useful to the design of a successful strategy for optimal management [15] (illustrated in Figure 5).

Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The multi agent method creates a non-structured environment using agents, these agents, in order to reach the global optimum, must be capable of performing transactions between each other, achieving several “deals”, which is performed though a common vocabulary, a finite set of information exchange, a finite set of possible actions, penalties, etc. The power of each agent depends on the degree of contribution of its represented process to the final output of the entire system. Each agent communicates with the environment and adapts to its own internal state as well as to the state of the entire system. In this systems, for the realization of the multi agent architecture the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].

Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The value of the final product incorporates not only quantity issues but also quality issues, which are difficult to be measured or even estimated. The environment of each agent is defined by the same parameters that define the physical environment in addition to internal states reported by each agent.