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Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
<p><strong>README</strong></p> <p>All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:<br>1. IBC-pre-post-process-revised.zip which contains:<br> - Radio sounding data used for vertical profile statistical and visual comparison. They are stored as "CHMU-soundings.dat" in the CHMU_soundings directory<br> - code for making the figures for vertical profile comparison between the WRF and PALM model<br> - code for performing the statistical analysis for the vertical profiles of PALM and the WRF model<br> - code for making the scatter plots of PALM and WRF vertical profiles<br> - code for making the heatmaps of the PALM model data</p> <p>2. PALM_code.zip contains the source code for the current version of the PALM model used for this experiment</p> <p>3. palm_inputs.zip contains:<br> - static driver file<br> - dynamic driver file<br> - configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr)<br>for each of the performed simulations</p> <p>4. postproc.zip contains:<br> - the code for performing statistical analysis for minimum (min), average (Avg), and maximum (max) three-day averaged differences for the WRF and PALM model outputs<br> - the code for making figures of the differences between selected pairs of WRF and PALM model outputs</p> <p>5. wrf_namelist.zip contains:<br> - list of files in which the setups/configuration for the WRF ensemble used in this experiment</p> <p><strong>PALM MODEL INSTALLATION AND USAGE GUIDE</strong></p> <p>A. Installation:</p> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <p>B. Usage:</p> <p>After a successful installation, the executables for all packages have been linked into the directory /bin and a default PALM configuration file can be found at /.palm.config.default. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Model, data, and analysis for Negative Niche Construction Favors the Evolution of Cooperation
<p>This repository contains the model, data, and analysis corresponding to <em>Negative Niche Construction Favors the Evolution of Cooperation</em> as submitted for review by Brian D. Connelly, Katherine J. Dickinson, Sarah P. Hammarlund, and Benjamin Kerr. Contents are released to the public domain under the Creative Commons CC0 License.</p>
Figure 7. World model construction and artificial intelligent structure running on the PDA
<p>In this section the AI part of the software is briefly introduced. There are three distinct<br> layers: AI Core, Role Engine and Behavior Engine. AI Core receives the computed field data from<br> World Map Modeling unit and determines the play state according to the ball, opponents and our<br> robots positions.<br> Considering the current game strategy, determination of the play state is done by fuzzy<br> decision-making to avoid undesirable and sudden changes of roles or behaviors.[3-4]</p>
Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition
<ul> <li><strong>Name</strong>: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition</li> <li><strong>Summary</strong>: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.</li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 01/06/2024</li> <li><strong>DOI</strong>: 10.5281/zenodo.11234441</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to build a set of causal diagrams of the .</li> <li><strong>Description:</strong> <br> <ul> <li><strong>Scenarios: </strong>This dataset contains the description of 20 different scenarios used in this research activity. </li> <li><strong>File 1 - individual reasons to be coded<br></strong>This dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets:<br> <ul> <li><strong>Methodology</strong>: Methodology followed by the coders.</li> <li><strong>Help</strong>: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding. </li> <li><strong>Glossary</strong>: Glossary of terms build by the experts coding the answers. </li> <li><strong>Appliances/Flexibility/Buildings/Mobility</strong>: The contributions of each expert, the code provided by the two researchers and the consensus achived. </li> <li><strong>Summary</strong>: Assesment of the results.</li> </ul> </li> <li><strong>File 2 - individual microdata to sort determinants into causal threads from experts</strong>This dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM. </li> <li><strong>File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts</strong> <p>This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears.</p> <ul> <li> <p><strong>Stage 1</strong>: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list:</p> <ul> <li> <p><strong>European panels</strong>: Group – F (8), Group–A (8). Group–FF (8), Group–M (4)</p> </li> <li> <p><strong>Latin America panels</strong>: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7)</p> </li> </ul> </li> </ul> <ul> <li> <p><strong>Stage 2</strong>: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist.</p> </li> <li> <p><strong>Stage3</strong>: In the "<em>Archetypes - Consensus Results</em>" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change).</p> </li> <li> <p><strong>Stage 4</strong>. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables:</p> <ul> <li> <p><em>Table (s)</em>: Difference and Agreements between both context: European & Latin American Archetypes. The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages.</p> </li> <li> <p><em>Table (t)</em>: This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes.</p> </li> <li> <p><em>Table (t.1)</em>: This table shows just the <strong>agreements</strong> (X) between both context: European & Latin American Archetypes.</p> </li> <li> <p><em>Table (t.2)</em>: Show the difference between both context: European (E) & Latin American Archetypes (L).</p> </li> <li> <p><em>Table (t.3)</em>: This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences.</p> </li> </ul> </li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> Data transcription from written documents and oral discussions.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned.</li> <li><strong>Ethics and legal aspects:</strong> Names of the persons involved have been removed. </li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
Construction-Planning Models in Minecraft
<p>Collection of benchmarks for the Minecraft House Construction scenario. </p> <p>Publication: Construction-Planning Models in Minecraft. Julia Wichlacz, Alvaro Torralba, Joerg Hoffmann, Hierarchical Planning Workshop at ICAPS 2019.</p>
Fig. 12 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 12. The same morphospaces as Fig. 11 showing the distribution of 115 species based on measurements.
Fig. 10 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 10. Measurements of the whorl expansion rate (Wc) and width of umbilicus (D). Note that c1 and c2 are defined as the distances from the coiling axis to the centers of the whorls. d1 and d2 are distances of umbilical seams from the coiling axis.
Fig. 8 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 8. Sketches of radial cross sections of ammonoids to illustrate various types of shell forms, such as planorbicone (A, B), serpenticone (C, D), oxycone (E), discocone (F), platycone (G, H), spherocone (I, J, K), and cadicone (L). A. Paraceltites elegans. B. Pseudoclymenia dillensis. C. Tropigastrites lahontanus. D. Pterolytoceras sp. E. Beloceras sp. F. Phylloceras consanguineum Gemmellaro. G. Craspedites sp. H. Tetragonites glabrus. I. Damesites sugata. J. Goniatites multiliratus Gordon. K. Latanarcestes sp. L. Cabrieroceras sp.
Fig. 9 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 9. Theoretical morphospace composed of ae and ar displaying several examples of computer−generated ammonoids that represent observed types illustrated in Fig. 8. A. e = 1.5 and r = 0.27. B. e = 1.75 and r = 0.34. C. e = 2.0 and r = 0.17. D. e = 2.3 and r = 0.05. E. e = 2.0 and r = 0.6. F. e = 1.7 and r = 0.27. G. e = 1.8 and r = 0.24. H. e = 2.3 and r = 0.21. I. e = 2.3 and r = 0.32. J. e = 1.95 and r = 0.28. K. e = 1.8 and r = 0.10. L. e = 1.6 and r = 0.8.
Fig. 7 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 7. Relationship between whorl perimeter (L) and square root of cross−sectional area of the whorl (A0.5) which varies with aand avalues. Dashed line i e r in each diagram represents the A0.5/Lcurve in the case of isometry of E and R (a= a= 1.0). A0.5/Lis generally small when ahas a large value. In each i e r i r model, e = 1.9 and r = 0.24.
Fig. 11 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 11. Results of computer simulations. A–D, theoretical morphospaces based on the piggyback whorls model composed of ae and ar showing values of shape parameters (S, A0.5/L, W, D) of a theoretical model corresponding to each combination of aand a. E, negative correlation between Wand S values i c e r c obtained from theoretical models. F, positive correlation between D and S. The values of S, A0.5/L, Wor D are exhibited by the size of the plots, and × indii c cates that a "forbidden" combinations of ae, ar, e, and r was employed in each simulation resulting in failure of defining form (A–D).
Fig. 6 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 6. Allometric growth of ammonoids. Computer models were generated with ae and ar values of 0.97, 1.0, and 1.3. Each diagram shows ontogenetic change in relationship between whorl height (H) and whorl breadth (B) that are standardized by the perimeter of the last whorl (Lmax). Dashed line in each diagram represents the B/H curve in the case of isometry of E and R (ae = ar = 1.0). Note that B/H decreases with growth in the case of ar = 0.97. In each model, e = 1.9 and r = 0.24.
Fig. 5 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 5. Spectrum of the computer−produced ammonoids with various values of E and R when each of them is fixed throughout growth.
Fig. 4 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 4. Schematic diagram of an ammonoid whorl section. The shape of the whorl (S) is represented by the closed curve illustrated here which is given by the parametric equations indicated above the diagram. Figures in the right show examples of hypothetical whorl shapes with systematically varying the S value.
Fig. 3 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 3. Different shapes of succeeding whorl (gray area) piling up on the hemispherical preceding whorl with same Li, l, and A values.
Fig. 2 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 2. Schematic figure of a cross section of the ammonoid shell showing how to define parameters of the piggyback whorls model. The enlarging rate of the whorl periphery (E) is defined as the ratio of the total perimeter of the newly added whorl section (Li) to that of preceding whorl (Li–1). The proportion of the dorsal whorl (R) is given by the ratio of the circumferential length along the dorsal wall (l) with respect to Li. The shape of the whorl section (S) is represented by the ratio of breadth (B) and height (H) of the whorl.
Fig. 1 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 1. Radial cross sections of ammonoids to illustrate allometric growth. A. Beloceras sp., Devonian; Erfoud, Morocco. B. Phylloceras consanguineum Gemmellaro, 1876, Jurassic; Sakaraha, Madagascar. C. Meekoceras gracilitatis White, 1879, Triassic; Crittenden Spring, Nevada. D. Girtyoceras meslerianum (Girty, 1909), Carboniferous; Jackforth Creek, Oklahoma.
Fig. 13 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 13. Results of same computer simulations as shown in Fig. 11 with an ornamented whorl shape. In order to produce an ornamented closed curve, the following parametric equations were used: x = 0.5Bcoso, y = 0.5H(sino + 0.3|sin4o|Q), where Q = H/B/5 if o Ṥ π, while Q = 5B/H if o> π. Note that simulated S values tend to be large as compared with Fig. 11F.
The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"
<p>The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK".</p> <p>Data collector: Runda Zheng</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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