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26 results for “path modeling”
Individual-based simulation model of annual movement paths for the Darwin's frog (R code and data)
<p>Desprition of the R code</p> <p>I constructed an individual-based simulation model that describes the movement path of an individual<em> Rhinoderma darwinii</em> through 3-month displacement steps. This model was primarily developed to evaluate the age-specific movement behaviour of Darwin's frogs, however, I also used it to provide better estimates (i.e. alleviating for movement censoring) of age-specific annual displacements in the species. I developed several variations of this model through a combination of different random walk sub-models for juveniles and adults: uncorrelated non-stationary random walks (NRW), correlated non-stationary random walks (CRW), and stationary random walks (SRW). The NRW and CRW were modelled as a first-order Markovian process where the location of an individual <em>i</em> in time<em> t</em> depends on its spatial location in <em>t </em>- 1. The NRW is unbiased, i.e., there is no preferred direction in each movement step. In contrast, the CRW includes persistence in the directionality of movement, so there is a correlation between successive step orientations. Finally, the SRW assumes that individuals have an activity centre to which all their spatial locations are related.</p> <p>Related data are provided (y.txt, x.txt and age.txt)</p>
Figure 4. An inside view of the "Maes building along with the Navigation Path" -Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 4 represents the navigation path to the Department of Computer Science inside the Maes building after selecting the option “D.C.S.”, which stands for Department of Computer Science.</p>
Instances of Orbit Slot Allocation Problems modeled as Directed Path Allocation Problems (DPAP)
<p><strong>Contents</strong></p> <p>This repository contains instances of Orbit Slot Allocation Problems modeled as Directed Path Allocation Problems (DPAP), defined in [<a href="roussel2023">1</a>]. These instances represent orbit slot allocation problems encoded as V-DPAP (Vertex-Constrained Directed Path Allocation Problem) or R-DPAP (Resource-Constrained Directed Path Allocation Problem).</p> <p>Each instance is provided as set of files, following the provided schema:</p> <ul> <li>One <code>instance_<i>_Graph_Request_<u>_<r>.dot</code> file per request, representing a graph, using the <a href="https://graphviz.org/doc/info/lang.html">dot language</a>, where <code><i></code> is the instance number, <code><u></code> is the id of the user emitting the request, and <code><r></code> is the unique id of the request/graph. Each such file lists a set of vertices and and set of weighted edges (using the <code>label</code> property of the dot language to set the weights). In the current set of instances, there are 8 requests per instance (2 requests per user, with 4 users).</li> <li>One <code>instance_<i>.inc</code> file, listing the incompabilities between vertices, with one set of incompatible vertices per row.</li> </ul> <p>Files are contained in subdirectories following the <code><n>_<m>_<mode>_<model></code> pattern, where <code><n></code> is the number of satellites per orbit plane (2, 4, 8, 16), <code><m></code> is the number of requests per user (2 only in this dataset), <code><mode></code> satisfaction mode of the request (<code>full</code> or <code>partial</code>), and <code><model></code> is the type of problem model used to encode the orbit slot allocation problems (<code>vdpap</code> or <code>rdpap</code>).</p> <p>We redirect to the referenced paper for more details on the DPAP, V-DPAP and R-DPAP models.</p> <p><strong>Acknowledgements</strong></p> <p>This work has been performed with the support of the French government in the context of the "Programme d'Invertissements d'Avenir", namely by the BPI PSPC LiChIE project (Lion Chaine Image Elargie), coordinated by Airbus Defence and Space.</p> <p><strong>References</strong></p> <p>[1] S. Roussel, G. Picard, C. Pralet and S. Maqrot. Conflicting Bundle Allocation with Preferences in Weighted Directed Acyclic Graphs: Application to Orbit Slot Allocation Problems, in MDPI <em>Systems</em>, Special Issue on Frontiers in Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems, 2023.</p>
A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy (dataset)
<p>Data cleaned and used for the path analysis model estimated in the article</p> <h1>A Path Model of the Intention to Adopt Variable Rate Irrigation in Northeast Italy</h1> <p>https://doi.org/10.3390/su13041879</p>
GIS data - Model přirozené komunikační sítě pro území ČR | GIS data - Model of Natural Path Network for the Czech Republic
<p>GeoTIFF layer (8 x 8 m) containing a model of natural routes through the landscape of the Czech Republic. For a detailed description of layers, see https://doi.org/10.5281/zenodo.3367296</p>
Transfer-Path-Based Hardware-Reuse Strong PUF Achieving Modeling Attack Resilience With >200 Million Training CRPs [DATASET]
<p>This is the CRP dataset for the IEEE TIFS paper titled "Transfer-Path-Based Hardware-Reuse Strong PUF Achieving Modeling Attack Resilience With >200 Million Training CRPs". It contains 2 packages with details as below:</p> <p>Package 1: "CRPs_14k_2048TPs_bit5_bit6.zip"<br> Description: It consists of 2048*3 files, which contains all the CRPs extracted from 2048 TPs with DOA disable. Using these data, we can train each ANN model for each TP(i,j), and the corresponding prediction results are shown in Fig. 14 and Fig. 15 in the paper.</p> <p>- "tdc_stim_i_j.csv" is the 64-bit challenges of TP(i,j).<br> - "tdc_resp_F1_i_j_bit5.csv" is the responses of TP(i,j) with bit(5) of TDC used as PUF response<br> - "tdc_resp_F1_i_j_bit6.csv" is the responses of TP(i,j) with bit(6) of TDC used as PUF response</p> <p>Package 2: "CRPs_DOA_330818x1024_bit5_bit6.zip"<br> Description: It consists of 3 files. Using the dataset, we trained each ANN model for each bit in RSobf, and the prediction results of all the ANN models are shown in Fig.17. Notice that the length of RS is dependent on the specific challenge. Here, only those RS consisting of ≥1024 responses are selected and only the first 1024 bits in the selected RS are used for authentication accordingly.</p> <p>- "chal_330818.csv" is the 330818 64-bit challenges.<br> - "resp_330818x1024_bit5.csv" is the 330818 1024-bit obfuscated response streams (RSobf) extracted from 2048 TPs using bit(5) of TDC.<br> - "resp_330818x1024_bit6.csv" is the 330818 1024-bit RSobf extracted from 2048 TPs using bit(6) of TDC.<br> </p>
The Path from Nasal Tissue to Nasal Mucosa on Chip Part 2- Advanced Microfluidic Nasal In Vitro Model for Drug Absorption Testing
<p><strong>Abstract: </strong>The nasal mucosa, being accessible and highly vascularized, opens up new opportunities for the systemic administration of drugs. However, due to several protective functions like the mucociliary clearance, this physiological barrier is a difficult obstacle for drug candidates to overcome. For this reason, effective testing procedures are required in the preclinical phase of pharmaceutical development. Based on a recently reported immortalized porcine nasal epithelial cell line, we developed a test platform based on a tissue-compatible microfluidic chip. A biomimetic glass chip equipped with a controlled bidirectional airflow to induce a physiologically relevant wall shear stress on the epithelial cell layer was microfabricated. By developing a membrane transfer technique, the epithelial cell layer could be pre-cultivated in a static holder prior to cultivation in the microfluidic environment. The dynamic cultivation within the chip showed a homogenous distribution of the mucus film on top of the cell layer and a significant increase of cilia formation compared to static cultivation condition. In addition, the recording of the ciliary transport mechanism by micro particle image velocimetry was successful. Using FITC-dextran 4000 as an example, it was shown that this nasal mucosa on a chip is suitable for permeation studies. The obtained permeation coefficient was in the range of values determined by means of other established in vitro and in vivo models. The novel nasal mucosa on chip can in future be automated and used as a substitute for animal testing.</p>
Supplementary material for "Learning Conceptual Modeling: Structuring Overview, Research Themes and Paths for Future Research"
<p>This material containing a list of the bibliographical data of the final sample and the raw data set of 345 publications before excluding publications outside of our focus supplements the following literature review:</p> <p>Rosenthal, Kristina; Ternes, Benjamin; Strecker, Stefan, (2019). “Learning Conceptual Modeling: Structuring Overview, Research Themes and Paths for Future Research”. In: Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm and Uppsala, Sweden, June 8–14, 2019.</p>
Drivers of metacommunity dynamics in river-floodplain fish: A path modeling approach
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Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>
Example Dataset for the Paper "lavaangui: A Web-Based Graphical Interface for Specifying lavaan Models by Drawing Path Diagrams"
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Compendium of R code and data for Mycenaean roads in the Peloponnese, Greece: least-cost path modelling using R and Movecost
<p><strong>Abstract</strong></p> <p><em>This study evaluates multiple methodologies and their variants for Least Cost Path (LCP) modelling, applied in combination with different Digital Elevation Models (DEMs), to explore the broader applicability of the Movecost package, using the Mycenaean road networks of the Peloponnese (Greece) as case studies. Using a geographic information system (GIS) and the R programming environment, this paper employs the Movecost package for the R statistical package to simulate ancient routes based on existing road segments. By integrating a variety of functions and parameters, this study evaluates their effectiveness across different DEMs, including both Shuttle Radar Topography Mission DEM (SRTM-DEM) and Copernicus DEM (COP-DEM) at 30 m spatial resolution. The study also examines how varying these parameters can lead to different modelling outcomes, underscoring the necessity of calibrating least-cost analysis to specific regional contexts. The road segments around Nichoria (Messenia), Ayios Ioannis Kazarma (Argolis), and in the Berbati Valley (Argolis), provide a historical canvas against which these methodological innovations are tested with the ultimate aim of exploring the capabilities of the Movecost package and how different combinations of DEM, function, parameter, and path points can effectively model the route through the existing road remains, highlighting the variability and context-specific nature of LCP modelling. The results suggest that the 'Wheeled-vehicle critical cost function' (WCS) was effective in modelling the roads through the extant remains based on start and endpoints suggested by previous research and posited by this paper. These results further suggest that Mycenaean roads likely served as key infrastructure links between major centres and ports or harbours, underscoring their role in facilitating regional trade and communication. However, this outcome represents one of several possible results, as the appropriateness of functions and the parameters tested depend on the specific landscape and archaeological context. This underscores the importance of careful parameter selection, providing insights into the economic and social landscapes of Mycenaean Greece, while also highlighting the potential of integrating spatial data with robust computational tools to enhance our understanding of ancient infrastructure.</em></p>
Data for "Simulating and analysing seabird flyways: an approach combining least-cost path modelling and machine learning"
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Refinement of an Attacker Model for Attack Paths Generation | Test Dataset
<p>This repository contains the test models and associated tests of the evaluation of the bachelor thesis "Refinement of an Attacker Model for Attack Paths Generation" / German: "Verfeinerung des Angreifermodells und Fähigkeiten in einer Angriffspfadgenerierung". The basis for the model used is the TravelPlaner CaseStudy. The tests for measuring the performance delta are also included.</p> <p>Palladio Bench 5, Java 11 and the following GitHub repositories are used for execution:<br> Analysis: https://github.com/Patrick-Spiesberger/Palladio-Addons-ContextConfidentialityAnalysis_Mitigation<br> Meta model: https://github.com/Patrick-Spiesberger/Palladio-Addons-ContextConfidentialityMetamodell_Mitigation<br> <br> Environment installation is included in the README files. See also the previous work by Maximilian Walter:<br> Analysis: https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Analysis<br> Meta Model: https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Metamodel</p>
Soda Spring, Seigler Spring, Wilbur Spring fluid speciation and reaction path models
<p>The software code EQ3/6 was used to calculate the equilibrium speciation of CO2-rich spring fluids and reaction of heated CO2-rich fluids with ultramafic rocks. The software code EQ3/6 is freely available at https://www.osti.gov/biblio/1231666 (Wolery, T. J., and USDOE. <em>EQ3/6 A Software Package for Geochemical Modeling</em>. Computer software. <em>https://www.osti.gov//servlets/purl/1231666</em>. USDOE. 13 Dec. 2010. Web. doi:10.11578/dc.20210416.44.). The compositions of fluids are from Barnes, I., O’Neil, J.R., Rapp, J.B., and White, D.E., 1973, Silica-carbonate alteration of serpentinite: wall rock alteration in mercury deposits of the California Coastal Ranges: Economic Geology, v. 68, p. 388–398</p> <p> </p> <p>File name identifiers:</p> <p>Speciation of Soda Spring fluid: sodaspr.3x</p> <p>Heating of Soda Spring fluid to 400 degrees Celcius: sodasp1.6x</p> <p>Reaction path model of heated Soda Spring fluid with ultramafic rock at 100 degrees Celcius: sodas4.6x</p> <p>Reaction path model of heated Soda Spring fluid with ultramafic rock at 200 degrees Celcius: sodas.6x</p> <p>Speciation of Seigler 1 Spring fluid: seigler_1.3i, seigler_.3x</p> <p>Heating of Seigler 1 Spring fluid to 400 degrees Celcius: seigler_.6x</p> <p>Speciation of Seigler 2 Spring fluid: seigler2.3x</p> <p>Heating of Seigler 2 Spring fluid to 400 degrees Celcius: seigler2_.6x</p> <p>Speciation of Wilbur Spring fluid: wilbur.3x</p> <p>Heating of Seigler 2 Spring fluid to 400 degrees Celcius: wilbur.6x</p> <p> </p> <p> </p> <p> </p>
Topologies, Checkpoints, and Configurations for the paper "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models"
<p>This repository consists of the <strong>files</strong> related to the <strong>paper</strong> "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models". In particular, this repository contains tensorboard logs, topologies, checkpoints, seeds, and results to ensure reproducibility of the results of the paper.</p> <p>The results included are related to the training/validation and hyper-parameters optimization of the outcome multi-label classifier, the GVI-RL agent, and the world model.<br>The data folder contains also the topologies used in the study, the vulnerabilities data used to generate them and the dataset for multi-label classification.</p> <p>The README.md file describes the folders' structure.</p>
Parkinson’s disease propagation using MRI biomarkers and partial least squares path modeling
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Machine learning based root stress calculation model for gears with progressive curved path of contact
<p><em>A machine learning based model for calculation of root stress in gears with a progressive curved path of contact is proposed. The prediction model is used as a surrogate model to the finite element analysis simulations, from which the data was collected. The results of the validation of all the methods are presented. Further validation was done with new simulations.</em></p>
Causality analysis, path analyses and models between Chlorophyll a and environmental factors
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Data for Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents
<p>Results data for the manuscript "Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.