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470 results for “tool use”
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1st Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1<sup>st</sup> Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels (one panel per stakeholder group, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
[Raw data] Media Coverage of 3D Visual Tools Used in Urban Participatory Planning
<p>Raw information on the articles used for the publication: Media Coverage of 3D Visual Tools Used in Urban Participatory Planning</p>
Confocal stacks from archaeological tools used for harvesting and threshing cereals
<p>Series of confocal stacks obtained with SensoScan, using a S Neox 3D profilometer from Sensofar. Measured surfaces from a series of archaeological tools.</p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Point, polygon, or marker? In search of the best geographic entity for mapping Cultural Ecosystem Services using the online PPGIS tool, "My Green Place."
<p>Excel files include the raw database and the processed data that led to the quadrat analyses. The "Matrix_raw data" file includes the raw data as downloaded from the server. This data was cleaned and organized for its posterior use. "Quadrat analyzes "file includes all the quadrat analyses resulting in each research question in the paper except question four. Question 4 can be seen in the file "Water analysis_Blaarmeersen." All excel files come with a "CODE" tab that describes each of the codes used, their meaning, and ways that were calculated where necessary. Two zip files include all the GIS files. The first one includes the GIS files from which "Matrix_raw data" was built from. The second folder includes the resulting maps from the quadrat analyses. In order to visualize them as in the paper, configure the symbology tab at the GIS software in quantile and the categories number, as shown in the paper.</p> <p>The production of the files in the "GIS_Processed data" folder was done via a repetitive line of commands in ArcGIS pro. The same process was followed for each one of the quadrat analysis described in the paper. Refer to "<a href="https://zenodo.org/api/files/72403e0b-78a9-4bbf-8dca-ed9b8702e3ae/Reproduction%20commands%20and%20parameters.pdf">Reproduction commands and parameters.pdf</a>" for further information.</p>
Datasets used in the Lab Validation of the RADON Verification Tool
<p>This repository contains the datasets that have been used to perform the lab validation of the RADON verification tool. In order to replicate the experiments, please unzip the file "validation-datasets.zip" and run the following command:</p> <pre><code class="language-bash">./run_all.rb {path_to_VT} list.json</code></pre> <p> </p>
Dataset for illustrative examples using the Lagrangian Atmospheric moisTure and heaT trackINg (LATTIN) tool
<p>This dataset provides the FLEXPART outputs for the illustrative examples of LATTIN usage. See the LATTIN GitHub repository (https://github.com/apalarcon/LATTIN) for details.</p><p>It was generated using FLEXPART model v9 fed by ERA-Interim reanalysis at the Environmental Physics Laboratory (EPhysLab) at the University of Vigo. See the list of publications of the EPhysLab research group for details on these simulations (https://ephyslab.uvigo.es/moisturetransport/index.php/Publications). </p>
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
<p>Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments.</p> <p>Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.</p> <p>The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach.</p> <p>The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales.</p> <p>The datasets and scripts provided in this repository allow replicating the results presented in the publication. </p>
Data from: Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)
<p>Although it is well documented that tool use can enable the utilization of novel resources, the fitness benefits associated with this innovative behavior are difficult to test. Using longitudinal data from 196 radio-tagged southern sea otters, we found that individuals, particularly females, with frequent tool use gained access to harder, larger prey items. In turn, the mechanical advantages of tool use during food processing translated to reduced tooth damage in tool users. We also found that tool use diminishes trade-offs between access to different prey types, tooth health, and caloric intake that are highly dependent on the relative availability of prey in the environment. Overall, tool use allows individuals to maintain caloric requirements through the processing of alternative prey that are otherwise inaccessible without the use of tools, indicating that this innovative behavior is a necessity for the survival of southern sea otters in environments with depleted preferred prey.</p>
Demonstrations of witness visualization using the Witness Visualizer Tool
<p>We have three datasets that display visualized <a href="https://sv-comp.sosy-lab.org/">SVCOMP</a> witnesses generated with the help of the <a href="https://github.com/ispras/cv">Witness Visualizer tool</a>. Each dataset comprises two directories: <code>witnesses</code>, which contains the original witnesses provided by SVCOMP tools, and <code>visualization</code>, which contains our visual representations of the respective witnesses in HTML format. The visualization file name contains the prefix <code>error_trace-</code>, for example, <code>error_trace-witness.2ls.html</code> corresponds to a witness named <code>witness.2ls.graphml</code>.</p> <h3>1. Overall thoroughness for all SVCOMP tools (<a href="../records/10988025/files/dataset_1.zip?download=1" target="_blank" rel="noopener">dataset_1.zip</a>)</h3> <p>This dataset includes a single random witness for each SVCOMP tool, accompanied by its corresponding visualization. The visualizations showcase the various witness elements such as function calls, conditions, assumptions, thread specifics, and other operations. Cells marked with <code>+/-</code> indicate that some elements were present in the error trace, but not all of them. All witnesses are presented in the table below:</p> <table> <tbody> <tr> <td>Witness</td> <td>SV-COMP Tool</td> <td>Function calls</td> <td>Threads</td> <td>Assumptions</td> <td>Conditions</td> <td>Link to sources</td> </tr> <tr> <td>witness.2ls.graphml</td> <td>2LS</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.aprove.graphml</td> <td>AProVE (2022)</td> <td>-</td> <td>-</td> <td>-</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.brick.graphml</td> <td>BRICK</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.bubaak.graphml</td> <td>Bubaak</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cbmc.graphml</td> <td>CBMC</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cpa-bam-bnb.graphml</td> <td>CPA-BAM-BnB</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpa-bam-smg.graphml</td> <td>CPA-BAM-SMG</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpalockator.graphml</td> <td>CPALockator</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpachecker.graphml</td> <td>CPAChecker</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.crux.graphml</td> <td>Crux</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cseq.graphml</td> <td>Cseq</td> <td>+</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.dartagnan.graphml</td> <td>Dartagnan</td> <td>-</td> <td>+</td> <td>-</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.deagle.graphml</td> <td>Deagle</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>DIVINE (until 2022)</td> <td>empty</td> </tr> <tr> <td>-</td> <td>EBF</td> <td>empty</td> </tr> <tr> <td>witness.esbmc-incr.graphml</td> <td>ESBMC-incr</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.esbmc-kind.graphml</td> <td>ESBMC-kind</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Frama-C-SV</td> <td>empty</td> </tr> <tr> <td>witness.gazer-theta.graphml</td> <td>Gazer-Theta</td> <td>+</td> <td>-</td> <td>+</td> <td>-</td> <td>wrong path</td> </tr> <tr> <td>witness.gdart.graphml</td> <td>Gdart-LLVM</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Goblint</td> <td>empty</td> </tr> <tr> <td>witness.graves_cpa.graphml</td> <td>Graves-CPA</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.graves_par.graphml</td> <td>Graves-Par</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>-</td> <td>Infer</td> <td>empty</td> </tr> <tr> <td>witness.korn.graphml</td> <td>Korn</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.lart.graphml</td> <td>LART (2022)</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.lazy-cseq.graphml</td> <td>Lazy-CSeq</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.lfchecker.graphml</td> <td>LF-checker</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Locksmith</td> <td>empty</td> </tr> <tr> <td>-</td> <td>Mopsa</td> <td>empty</td> </tr> <tr> <td>witness.pesco_cpa.graphml</td> <td>PeSCo-CPA</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.pichecker.graphml</td> <td>PIChecker</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.pinaka.graphml</td> <td>Pinaka</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.predator.graphml</td> <td>PredatorHP</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>SESL (2022)</td> <td>empty</td> </tr> <tr> <td>witness.smack.graphml</td> <td>SMACK (until 2022)</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.symbiotic.graphml</td> <td>Symbiotic</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.theta.graphml</td> <td>Theta</td> <td>different format</td> </tr> <tr> <td>witness.uatomozer.graphml</td> <td>UAutomizer</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.ucutter.graphml</td> <td>UgemCutter</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.ukojak.graphml</td> <td>UKojak</td> <td>+/-</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.utaipan.graphml</td> <td>UTaipan</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.veriabs.graphml</td> <td>VeriAbs</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>wrong path</td> </tr> <tr> <td>witness.veriabsl.graphml</td> <td>VeriAbsL</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>wrong path</td> </tr> <tr> <td>witness.verifuzz.graphml</td> <td>VeriFuzz</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.verioover.graphml</td> <td>VeriOover</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> </tbody> </table> <h3>2. Thoroughness by property (<a href="../records/10988025/files/dataset_2.zip?download=1" target="_blank" rel="noopener">dataset_2.zip</a>)</h3> <p>This dataset comprises a selected witness for each SVCOMP property (ReachSafety, MemSafety, Termination, NoOverflow, ConcurrencySafety). The witnesses are presented in the following table:</p> <p> </p> <table> <tbody> <tr> <td>Witness</td> <td>SV-COMP Tool</td> <td>Property</td> <td>Mandatory elements</td> <td>Description</td> </tr> <tr> <td>witness.smg_memory.graphml</td> <td>CPA-BAM-SMG</td> <td>MemSafety</td> <td>Assumptions / conditions, function calls</td> <td>There is a double free operation. Employing function calls <code>append </code>aids in comprehending the structure of the list, while assumptions reveal which branch was chosen.</td> </tr> <tr> <td>witness.graves_overflow.graphml</td> <td>Graves-CPA</td> <td>NoOverflow</td> <td>Assumptions / conditions</td> <td>The witness showcases an explicit (<code>-2147483648</code>, which represents the minimal value for the <code>int</code> type), which has the potential to cause overflow in specific program.</td> </tr> <tr> <td>witness.cpachecker_termination.graphml</td> <td>CPAChecker</td> <td>NoTermination</td> <td>Assumptions / conditions</td> <td>There is a condition leading to an infinite loop.</td> </tr> <tr> <td>witness.cpachecker_unreach.graphml</td> <td>CPAChecker</td> <td>ReachSafety</td> <td>Function calls</td> <td>The error trace indicates a potential scenario where a <code>mutex</code> was unlocked<br>without the corresponding <code>mutex_unlock</code> operation.</td> </tr> <tr> <td>witness.cpachecker_conc.graphml</td> <td>CPAChecker</td> <td>ConcurrencySafety</td> <td>Function calls, thread operations</td> <td>The error trace illustrates the creation of threads and highlights the assignments made within each thread that ultimately resulted in the violation of the property.</td> </tr> </tbody> </table> <div> <h3>3. Known bug (<a href="../records/10988025/files/dataset_2.zip?download=1" target="_blank" rel="noopener">dataset_3.zip</a>)</h3> <p>This dataset contains witnesses for a known bug from SVCOMP (<code>linux-3.14--drivers--usb--misc--adutux.ko.cil.i</code>) involving a data race on <code>dev->udev</code>, where concurrent writes occur without corresponding locks. Only two tools were able to solve the corresponding verification task: ESBMC-kind and CPALockator. The ESBMC error trace (<code>witness.esbmc_2020.graphml</code>) includes only thread specifics and assumptions, while the CPALockator witness (<code>witness.lockator.graphml</code>) comprises all witness elements and is presented in a human-readable format.</p> <h3>4. Comparison with the validation rate</h3> <p>This section presents a comparison between witness thoroughness and the actual validation rate for each property. We considered all tools that participated in the respective category and generated at least 10 error traces, then calculated the validation rate. This comparison demonstrates how effectively thoroughness can approximate the validation rate. The following tables provide details for each property, with the relevant elements used to calculate thoroughness highlighted:</p> <ul> <li>MemSafety property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td><strong>Function calls</strong></td> <td>Threads</td> <td><strong>Assumptions</strong></td> <td><strong>Conditions</strong></td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>Bubaak</td> <td><strong>0</strong></td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>33.33</td> <td>64</td> <td>67.19</td> </tr> <tr> <td>CBMC</td> <td><strong>0</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>33.33</td> <td>27</td> <td>11.11</td> </tr> <tr> <td>CPA-BAM-SMG</td> <td><strong>1</strong></td> <td>0</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>46</td> <td>78.26</td> </tr> <tr> <td>CPAChecker</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>37</td> <td>67.57</td> </tr> <tr> <td>ESBMC-kind</td> <td><strong>0</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>33.33</td> <td>25</td> <td>20</td> </tr> <tr> <td>Graves-CPA</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>44</td> <td>56.82</td> </tr> <tr> <td>Graves-Par</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>18</td> <td>77.78</td> </tr> <tr> <td>PeSCo-CPA</td> <td><strong>1</strong></td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>37</td> <td>67.57</td> </tr> </tbody> </table> <ul> <li>NoOverflow property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td>Function calls</td> <td>Threads</td> <td><strong>Assumptions</strong></td> <td>Conditions</td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>2LS</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>2071</td> <td>95.7</td> </tr> <tr> <td>Bubaak</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>2233</td> <td>94.67</td> </tr> <tr> <td>CBMC</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>3296</td> <td>62.14</td> </tr> <tr> <td>CPAChecker</td> <td>1</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>196</td> <td>100</td> </tr> <tr> <td>Crux</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>222</td> <td>95.05</td> </tr> <tr> <td>ESBMC-kind</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>3296</td> <td>66.69</td> </tr> <tr> <td>Frama-C-SV</td> <td>0</td> <td>0</td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>676</td> <td>0</td> </tr> <tr> <td>Graves-Par</td> <td>1</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>750</td> <td>2</td> </tr> <tr> <td>Infer</td> <td>0</td> <td>0</td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>583</td> <td>0</td> </tr> <tr> <td>Pinaka</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>2232</td> <td>100</td> </tr> <tr> <td>Symbiotic</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>1418</td> <td>100</td> </tr> <tr> <td>UAutomizer</td> <td>0.5</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>2222</td> <td>100</td> </tr> <tr> <td>UKojak</td> <td>0.5</td> <td>0</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>168</td> <td>100</td> </tr> <tr> <td>UTaipan</td> <td>0.5</td> <td>1</td> <td><strong>1</strong></td> <td>1</td> <td>100</td> <td>0</td> <td>100</td> </tr> <tr> <td>VeriFuzz</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td>0</td> <td>100</td> <td>185</td> <td>90.81</td> </tr> </tbody> </table> <ul> <li>NoTermination property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td>Function calls</td> <td>Threads</td> <td><strong>Assumptions</strong></td> <td><strong>Conditions</strong></td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>2LS</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>663</td> <td>69.08</td> </tr> <tr> <td>Bubaak</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>578</td> <td>34.78</td> </tr> <tr> <td>CPAChecker</td> <td>1</td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>501</td> <td>97.01</td> </tr> <tr> <td>Symbiotic</td> <td>0</td> <td>1</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>591</td> <td>52.96</td> </tr> <tr> <td>UAutomizer</td> <td>0.5</td> <td>1</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>100</td> <td>512</td> <td>98.24</td> </tr> <tr> <td>VeriFuzz</td> <td>0</td> <td>0</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>50</td> <td>492</td> <td>71.34</td> </tr> </tbody> </table> <ul> <li>ReachSafety property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td><strong>Function calls</strong></td> <td>Threads</td> <td>Assumptions</td> <td>Conditions</td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>Bubaak</td> <td><strong>0</strong></td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> <td>24</td> <td>54.17</td> </tr> <tr> <td>CBMC</td> <td><strong>0</strong></td> <td>1</td> <td>1</td> <td>0</td> <td>0</td> <td>392</td> <td>1.28</td> </tr> <tr> <td>CPA-BAM-BnB</td> <td><strong>1</strong></td> <td>0</td> <td>1</td> <td>1</td> <td>100</td> <td>69</td> <td>85.51</td> </tr> <tr> <td>CPA-BAM-SMG</td> <td><strong>1</strong></td> <td>0</td> <td>1</td> <td>1</td> <td>100</td> <td>67</td> <td>85.07</td> </tr> <tr> <td>CPAChecker</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>45</td> <td>88.89</td> </tr> <tr> <td>Crux</td> <td><strong>0</strong></td> <td>0</td> <td>1</td> <td>0</td> <td>0</td> <td>1572</td> <td>0.13</td> </tr> <tr> <td>ESBMC-kind</td> <td><strong>0</strong></td> <td>1</td> <td>1</td> <td>0</td> <td>0</td> <td>64</td> <td>21.88</td> </tr> <tr> <td>Graves-CPA</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>66</td> <td>87.88</td> </tr> <tr> <td>Graves-Par</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>24</td> <td>58.33</td> </tr> <tr> <td>PeSCo-CPA</td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>1</td> <td>100</td> <td>63</td> <td>85.71</td> </tr> </tbody> </table> <ul> <li>ConcurrencySafety property:</li> </ul> <table> <tbody> <tr> <td>SV-COMP Tool</td> <td><strong>Function calls</strong></td> <td><strong>Threads</strong></td> <td>Assumptions</td> <td>Conditions</td> <td>Thoroughness</td> <td>Error traces</td> <td>Validation rate</td> </tr> <tr> <td>CBMC</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>277</td> <td>87</td> </tr> <tr> <td>CPA-Lockator</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>83</td> <td>26.51</td> </tr> <tr> <td>CPAChecker</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>257</td> <td>100</td> </tr> <tr> <td>Cseq</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>100</td> <td>277</td> <td>94.58</td> </tr> <tr> <td>Dartagnan</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>0</td> <td>0</td> <td>50</td> <td>281</td> <td>92.17</td> </tr> <tr> <td>Deagle</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>280</td> <td>96.07</td> </tr> <tr> <td>DIVINE</td> <td><strong>0</strong></td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>0</td> <td>230</td> <td>80.87</td> </tr> <tr> <td>EBF</td> <td><strong>0</strong></td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>0</td> <td>282</td> <td>89.01</td> </tr> <tr> <td>ESBMC-incr</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>68</td> <td>79.41</td> </tr> <tr> <td>ESBMC-kind</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>263</td> <td>89.73</td> </tr> <tr> <td>Graves-CPA</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>261</td> <td>99.23</td> </tr> <tr> <td>Graves-Par</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>28</td> <td>100</td> </tr> <tr> <td>Infer</td> <td><strong>0</strong></td> <td><strong>0</strong></td> <td>0</td> <td>0</td> <td>0</td> <td>634</td> <td>0</td> </tr> <tr> <td>Lazy-CSeq</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>274</td> <td>94.89</td> </tr> <tr> <td>LF-checker</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>286</td> <td>85.31</td> </tr> <tr> <td>PeSCo-CPA</td> <td><strong>1</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>100</td> <td>256</td> <td>100</td> </tr> <tr> <td>PIChecker</td> <td><strong>1</strong></td> <td><strong>0</strong></td> <td>1</td> <td>1</td> <td>50</td> <td>269</td> <td>98.14</td> </tr> <tr> <td>Symbiotic</td> <td><strong>0</strong></td> <td><strong>1</strong></td> <td>1</td> <td>0</td> <td>50</td> <td>110</td> <td>92.73</td> </tr> <tr> <td>UAutomizer</td> <td><strong>0.5</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>75</td> <td>297</td> <td>94.95</td> </tr> <tr> <td>UgemCutter</td> <td><strong>0.5</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>75</td> <td>283</td> <td>96.47</td> </tr> <tr> <td>UTaipan</td> <td><strong>0.5</strong></td> <td><strong>1</strong></td> <td>1</td> <td>1</td> <td>75</td> <td>293</td> <td>96.25</td> </tr> </tbody> </table> <h3>5. Overall distance for all possible combinations of elements for thoroughness</h3> <p>This section presents the overall difference (i.e., the sum of differences between witness thoroughness and validation rates for each tool) when thoroughness is calculated based on all possible combinations of witness elements (assumptions, conditions, thread specifics, and function calls). The set of witnesses is the same as in the previous section. The following tables provide details for each property, with the minimum difference highlighted:</p> <ul> <li>MemSafety property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>250.3</td> </tr> <tr> <td>Thread specifics</td> <td>444.6</td> </tr> <tr> <td>Assumptions</td> <td>353.7</td> </tr> <tr> <td>Conditions</td> <td>250.3</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>294.6</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>238.08</td> </tr> <tr> <td>Conditions, Function calls</td> <td>250.3</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>344.6</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>294.6</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>238.08</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>277.94</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>244.59</td> </tr> <tr> <td><strong>Assumptions, Conditions, Function calls</strong></td> <td><strong>221.41</strong></td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>277.94</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>244.6</td> </tr> </tbody> </table> <ul> <li>NoOverflow property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>953.06</td> </tr> <tr> <td>Thread specifics</td> <td>745.4</td> </tr> <tr> <td><strong>Assumptions</strong></td> <td><strong>192.94</strong></td> </tr> <tr> <td>Conditions</td> <td>803.06</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>778.06</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>478.06</td> </tr> <tr> <td>Conditions, Function calls</td> <td>878.06</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>445.4</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>703.06</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>403.06</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>528.8</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>786.41</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>586.43</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>478.79</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>590.56</td> </tr> </tbody> </table> <ul> <li>NoTermination property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>279.39</td> </tr> <tr> <td>Thread specifics</td> <td>226.99</td> </tr> <tr> <td>Assumptions</td> <td>176.59</td> </tr> <tr> <td>Conditions</td> <td>232.91</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>204.39</td> </tr> <tr> <td><strong>Assumptions, Function calls</strong></td> <td><strong>84.83</strong></td> </tr> <tr> <td>Conditions, Function calls</td> <td>254.39</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>107.43</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>182.91</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>63.35</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>106.82</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>212.73</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>112.74</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>93.67</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>116.89</td> </tr> </tbody> </table> <ul> <li>ReachSafety property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td><strong>Function calls</strong></td> <td><strong>212.74</strong></td> </tr> <tr> <td>Thread specifics</td> <td>607.58</td> </tr> <tr> <td>Assumptions</td> <td>557.82</td> </tr> <tr> <td>Conditions</td> <td>312.74</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>357.58</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>316.16</td> </tr> <tr> <td>Conditions, Function calls</td> <td>262.74</td> </tr> <tr> <td>Assumptions, Thread specifics</td> <td>507.32</td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>407.58</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>366.16</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>373.98</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>307.56</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>299.48</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>407.32</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>332.32</td> </tr> </tbody> </table> <ul> <li>ConcurrencySafety property:</li> </ul> <table> <tbody> <tr> <td>Combination</td> <td>Overall difference</td> </tr> <tr> <td>Function calls</td> <td>1016.62</td> </tr> <tr> <td>Thread specifics</td> <td>442.72</td> </tr> <tr> <td>Assumptions</td> <td>430.78</td> </tr> <tr> <td>Conditions</td> <td>980.44</td> </tr> <tr> <td>Function calls, Thread specifics</td> <td>637.9</td> </tr> <tr> <td>Assumptions, Function calls</td> <td>641.62</td> </tr> <tr> <td>Conditions, Function calls</td> <td>980.78</td> </tr> <tr> <td><strong>Assumptions, Thread specifics</strong></td> <td><strong>427.06</strong></td> </tr> <tr> <td>Conditions, Thread specifics</td> <td>626.72</td> </tr> <tr> <td>Assumptions, Conditions</td> <td>630.44</td> </tr> <tr> <td>Assumptions, Function calls, Thread specifics</td> <td>512.89</td> </tr> <tr> <td>Conditions, Function calls, Thread specifics</td> <td>735.42</td> </tr> <tr> <td>Assumptions, Conditions, Function calls</td> <td>739.14</td> </tr> <tr> <td>Assumptions, Conditions, Thread specifics</td> <td>510.03</td> </tr> <tr> <td>Assumptions, Conditions, Function calls, Thread specifics</td> <td>614.56</td> </tr> </tbody> </table> </div>
FIG. 2 in Finite element analysis of the proximal phalanx of the thumb in Hominoidea during simulated stone tool use
FIG. 2. — Free-body diagram of the phalanx in the different scenarios. This figure depicts the boundary conditions, areas of insertion of muscles, and direction of forces. For all loading configurations, joint reaction forces resulted from the rigid boundary constraints that were fixed at the distal joint in X, Y and Z-axes (light blue area), and at the proximal joint in the X-axis (dark blue area). The hammer reaction force (HRF) was applied to the entire palmar surface of the bone. 3.29 N for the HRF was simulated for Sc 1 and 3, and 7.65 N for Sc 2 and 4. Phalanges are shown in palmar (right) and radial (left) views.
FIG. 5 in Finite element analysis of the proximal phalanx of the thumb in Hominoidea during simulated stone tool use
FIG. 5. — Box-plots of von Mises stress (MPa) distribution for all species under different scenarios, until Q95 (Sc 1 in grey, Sc 2 in yellow, Sc 3 in green and Sc 4 in red). The first row shows stress distribution of the models using the extant human as a reference to scale muscular forces in all other specimens, whereas the second one shows the results when the chimpanzee is used as a reference. Species are ordered from higher to lower peak stresses.
FIG. 1 in Finite element analysis of the proximal phalanx of the thumb in Hominoidea during simulated stone tool use
FIG. 1. — Biomechanical model of hammerstone use: B, corresponds to a zoom in palmar view of the area of interest during A, the grip of a human individual (based on Marzke et al. 1998). B, shows the angles of the muscular forces acting on the PP1. HRF is in 90° relative to the horizontal line for scenarios 1 and 2 and in 45° for scenarios 2 and 4. This force was applied on the entire palmar surface of the PP1 except in the joint areas and is represented with a hatched rectangle. Angles of the muscle forces are shown relative to the horizontal line. Abbreviations: FAP, Adductor Pollicis Force; FAPB, Abductor Pollicis Brevis Force; FFPB, Flexor Pollicis Brevis Force; EPB, direction force was applied in 16.7° and is not showed here as it attached on the dorsal surface of the PP1. Grey rectangles represent the origin areas of the muscles.
FIG. 3 in Finite element analysis of the proximal phalanx of the thumb in Hominoidea during simulated stone tool use
FIG. 3. — Von Mises stress maps for all analyzed species under different loading scenarios using the extant human as reference to scale the simulated muscular forces in all other specimens. Species are ordered from higher to lower peak stresses values. Phalanges are shown at the same length. MPa bar is set at 12 MPa.
FIG. 4 in Finite element analysis of the proximal phalanx of the thumb in Hominoidea during simulated stone tool use
FIG. 4. — Von Mises stress maps for all analyzed species under different loading scenarios using the chimpanzee as reference to scale the simulated muscular forces in all other specimens. Species are ordered from higher to lower peak stresses values. Images are not scaled. MPa is set at 25 MPa.
TABLE 2 in Finite element analysis of the proximal phalanx of the thumb in Hominoidea during simulated stone tool use
<p>TABLE 2. — Percentage of main locomotor behavoir of the non-human sample, according to Hunt (2004).</p><table><tbody><tr><th><b>Taxon</b></th><th><b>Climb</b></th><th><b>Braquiate</b></th><th><b>Clamber</b></th><th><b>Walk</b></th></tr></tbody><tbody><tr><th>Chimpanzee</th><td>6.5</td><td>0.8</td><td>0.0</td><td>89.9</td></tr><tr><th>Gorilla</th><td>19.7</td><td>3.6</td><td>0.0</td><td>64.4</td></tr><tr><th>Orangutan</th><td>31.3</td><td>15.5</td><td>40.7</td><td>12.0</td></tr><tr><th>Gibbon</th><td>34.2</td><td>51.2</td><td>0.0</td><td>0.0</td></tr></tbody></table>
Using species distribution models and decision tools to direct surveys and identify potential translocation sites for a critically endangered species
<p>Aim: Occurrence records for cryptic species are typically limited or highly uncertain, leaving their distributions poorly resolved and hampering conservation. This can apply to well‐studied species, and increased survey effort and/or novel methods are required to improve distribution data. Here, we paired species distribution modelling (SDM) with decision tools to direct surveys for the critically endangered Leadbeater's possum (Gymnobelideus leadbeateri) outside its current restricted range. We also assessed survey areas for their suitability to host translocations.</p> <p>Location: Victoria, Australia.</p> <p>Method: We used both recent and historic records (now out of range and spatially uncertain) of Leadbeater's possum to build SDMs using MaxEnt. The SDMs informed an initial multi‐criteria decision analysis (MCDA) that enabled prioritization of 80 survey sites across seven forest patches (13–145 km outside the known range), which we surveyed using camera traps. Site and vegetation data were used in a post‐survey MCDA to rank their potential translocation suitability.</p> <p>Results: The SDM predictions were consistent with the species' ecology, identifying cold areas with high rainfall that had not recently burnt as suitable. The spatial uncertainty of records did not exert a strong influence on either model predictions or the ranking of patches for surveys. Camera trap surveys yielded records of 19 native species, with Leadbeater's possum detected in only one survey patch, 13 km outside of its previously known range. The post‐survey MCDA identified three forest patches as potentially suitable for conservation translocations, and these priorities were not sensitive to the decision criteria used.</p> <p>Main conclusions: The approach outlined here prioritized survey effort over a large area, resulting in detection of Leadbeater's possum in one new patch. The potential translocation sites identified could present an important risk‐spreading measure for the species given the threat posed by bushfire. Combining SDMs and decision tools can help target surveys and guide subsequent conservation strategies.</p>
A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool
<p>Datasets relating to the Wellcome Open Research publication of the same name.</p>
Code and data used in "A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies"
<p>Data and code for Tool for Air Pollution Scenarios (TAPS v1.0) as submitted to Geoscientific Model Development for publication. See the enclosed README and full user manual (https://github.com/watkin-mit/TAPS/wiki) for more information. </p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
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Allen Brain Atlas
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