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547 results for “Model Tests”
An integrated framework for analysing, simulating and testing UML models
<h1>Project Overview</h1> <p>This is the artefact associated with the paper "An integrated framework for analysing, simulating and testing UML models", submitted to the 27th Brazilian Symposium on Formal Methods (SBMF 2024).</p> <h2>Directory Structure</h2> <ul> <li><strong>CSP_Validation</strong>: Contains the validation of our mapping rules from UML diagrams to CNL requirements.</li> <li><strong>NAT2TEST_Projects</strong>: Contains the NAT2TEST projects for two case studies: the classical Dijkstra's dining philosophers problem, and a distributed ring-buffer model.</li> <li><strong>UML2CNL</strong>: Contains the implementation of our mapping rules from UML diagrams to CNL requirements.</li> </ul>
LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models - Replication Package
<p>LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models</p> <p>This is the replication package associated with the paper "LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models".</p> <p><strong>Replication Package Contents:</strong></p> <p>This replication package contains all the necessary data and code required to reproduce the results reported in the paper. We provide the results of the Fault Detection Rate (FDR), Total Minimization Time (MT), Time Saving Rate (TSR) , statistical tests for all the minimization budgets (i.e., 25%, 50%, and 75%), results for the preliminary study, results for UniXcoder/Cosine with preprocessed code on 16 projects.</p> <p><strong>Data:</strong></p> <p>We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is the source code of test cases (Java test methods) of 17 projects collected from Defects4J.</p> <p><strong>Code:</strong></p> <p>We provide in the<em> <strong>Code</strong> </em>directory the code (Python) and bash files required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong></p> <p>We provide in the<em> <strong>Results </strong></em>directory the detailed results for our approach (called LTM). We also provide the summarized results of LTM and a baseline (ATM) for comparison purposes. Additional technical details about ATM can be found at https://zenodo.org/record/7455766.</p> <p><strong>_________________________________</strong></p> <p><strong>LTM's Similarity Measurement:</strong></p> <p>The source code of this step is in the <strong><em>Code/LTM/Similarity</em></strong> directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <em><strong>Code/LTM/Similarity/requirements.txt</strong></em> file should be installed, as follows:</p> <p>cd Code/LTM/Similarity</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <ul> <li>Data/LTM/TestMethods</li> </ul> <p><strong>Output:</strong></p> <ul> <li>Data/LTM/similarity_measurements</li> </ul> <p><strong>Running the experiment:</strong></p> <p>To measure the similarity between all pairs of test cases, the following bash script should be executed:</p> <p>bash measure_similarity.sh</p> <p>The source code of test methods of each project in the <strong><em>Data/LTM/TestMethods</em></strong> is parsed to generate pairs of test cases. This steps includes test methods tokenization, test methods embeddings extraction and similarity calculation. Then, all similarity scores are stored in <em><strong>Data/LTM/similarity_measurements</strong></em> folder. Due to the large size of the calculated similarity scores (60 GB), they were not uploaded on Zenodo, but they can be available upon request.</p> <p><strong>LTM's Test Suite Minimization:</strong></p> <p>The source code of this step is in the Code/LTM/Search directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <strong><em>Code/LTM/Search/requirements.txt</em></strong> file should be installed, as follows:</p> <p>cd Code/LTM/Search</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <p>Data/LTM/similarity_measurements</p> <p><strong>Output:</strong></p> <p>Results/LTM/minimization_results</p> <p><strong>Running the experiments:</strong></p> <p>To minimize the test suite for each project version, the following bash script should be executed:</p> <p>bash minimize.sh</p> <p>The similarity scores of all test case pairs per project version are parsed by the search algorithm (Genetic Algorithm). Each experiment runs ten times using three minimization budgets (25%, 50%, and 75%). The results are stored in the <em><strong>Results/LTM/minimization_results</strong></em> directory.</p> <p><strong>LTM's Evaluation:</strong></p> <p>To evaluate the minimization results for each version and each project, the following bash script should be executed:</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_version.sh</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_project.sh</p> <p>This will evaluate the FDR, MT and TSR results for each version and each project for each minimization budget. These results are stored in the <em><strong>Results/LTM</strong></em> directory.</p> <p>Note that for each version, the FDR is either 1 or 0. For each project, the FDR ranges from 0 to 1.</p>
MBES M3 KONGSBERG HF MODEL TEST TANK MARINE DEBRIS DATASET
<p>Dataset created in test tank enviroment for data characterization to infer if marine debris could be detected and classified in the water column. Multiple acoustic frequencies were acquired for the multiple objects. Three points of view were considered for each of the three possible ranges that were possible to acquire data without it being completly compromised by the test tank's walls and bottom, as well as reflections in the water surface.</p>
Data from: A test of the hierarchical model of litter decomposition
Our basic understanding of plant litter decomposition informs the assumptions underlying widely applied soil biogeochemical models, including those embedded in Earth system models. Confidence in projected carbon cycle-climate feedbacks therefore depends on accurate knowledge about the controls regulating the rate at which plant biomass is decomposed into products such as CO2. Here, we test underlying assumptions of the dominant conceptual model of litter decomposition. The model posits that a primary control on the rate of decomposition at regional to global scales is climate (temperature and moisture), with the controlling effects of decomposers negligible at such broad spatial scales. Using a regional-scale litter decomposition experiment at six sites spanning from northern Sweden to southern France – and capturing both within and among site variation in putative controls – we find that contrary to predictions from the hierarchical model, decomposer (microbial) biomass strongly regulates decomposition at regional scales. Further, the size of the microbial biomass dictates the absolute change in decomposition rates with changing climate variables. Our findings suggest the need for revision of the hierarchical model, with decomposers acting as both local- and broad-scale controls on litter decomposition rates, necessitating their explicit consideration in global biogeochemical models.
Data from: Can conflicting selection from pollinators and nectar-robbing antagonists drive adaptive pollen limitation? A conceptual model and empirical test
<p><span><span><span><span><span><span><span><span><span><span><span>Pollen limitation is widespread, despite predictions that it shouldn't be. We propose a novel mechanism generating pollen limitation: conflicting selection by pollinators and antagonists on pollinator attraction traits. We introduce a heuristic model demonstrating antagonist-induced adaptive pollen limitation, and present a field study illustrating its occurrence in a wild population. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>For antagonist-induced adaptive pollen limitation to occur, four criteria must be met: 1) correlated attraction of pollinators and antagonists, 2) greater response by antagonists than pollinators to altered investment in attraction traits, 3) reduced investment in pollinator attraction leading to pollen limitation, and 4) higher fitness for plants with reduced investment in pollinator attraction.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>We surveyed nectar robbery and reproductive output for 109 <i>Odontonema cuspidatum </i>(Acanthaceae) plants in a pollen-limited population<i> </i>over two years and used experimental floral arrays to evaluate how flower number affects pollination and nectar robbery. Both pollinators and nectar robbers preferred larger floral displays, and nectar robbery reduced reproductive output, suggesting conflicting selection. Survey and experimental data agreed closely on the optimum flower number under antagonist-induced pollen limitation; this number was substantially overrepresented in the population. While criteria for antagonist-induced adaptive pollen limitation are restrictive, the necessary conditions may often be realized. Considering interactions beyond the plant-pollinator dyad illuminates previously overlooked mechanisms generating pollen limitation. </span></span></span></span></span></span></span></span></span></span></span></p>
Harbour protection with dual chamber oscillating water column devices (HARBOUR OWC) - 1/25 scale model tank test
Among various devices of wave energy converters (WEC), oscillating water column (OWC) devices are one of the concepts successfully deployed around the world. Hence, the necessity to design and develop the recent concept of dual-chamber OWC device attached to an existing coastal breakwater emerged. Former investigation in a wave flume already characterized the hydrodynamic performance of the device for a 1:25 scale model with frontal wave excitations. From this study, the effect of the incident wave direction is highlighted and the effect of the air compressibility on the device performances can be determined. The enclosed dataset refers to the experimental work developed at LNEC within the project Harbour OWC (PTDC/EME-REN/30866/2017) and considers the 3D effect of the incoming waves with a 14º angle of incidence. Data of free surface elevation along the tank and inside the device along with total pressure are presented.
Pretrained models and test examples of live cell anatomy segmentation
<p>Pretrained models of live cell anatomy segmentation and test examples of the segmentation of 15 structures</p>
Nemo2 (Numbers, fEatures, MOdels, version 2) Testing Files
<p>The publications and research associated with this software is currently under review in the "<em>Journal</em> of <em>Systems</em> and <em>Software</em>".</p> <p>You can find the official Github repository of this dataset in: <a href="https://github.com/danieljmg/Nemo2_models">https://github.com/danieljmg/Nemo2_models</a></p>
U-T training and test data for LayerFault model
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: LayerFault model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</p>
Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators
<p class="MsoNormal">Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used.</p> <p class="MsoNormal">We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to unsampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, and that revisiting previously-studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.</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>
TRACERx PHLEX test dataset, model weights & TRACERx nuclear IMC segmentation dataset
<p>This repository contains three archives pertaining to the TRACERx PHLEX multiplexed imaging pipelines.</p> <ol> <li>Weights for the deep-imcyto neural network models</li> <li>The PHLEX test dataset consisting of two .ome.tiff files of imaging mass cytometry data</li> <li>The TRACERx Nuclear IMC Segmentation dataset, a training dataset of IMC image tiles and ~42,000 expert labelled nuclei from diverse lung, normal and other tissue types.</li> </ol>
Data and Code Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"
<p>Code and Data Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"<br> ===========================================================</p> <p>This directory contains the data and scripts used to create the plots from our publication as well as the source<br> code modifications necessary to run this test case within the CESM and MPAS models.</p> <p>Generating Plots<br> ---------------</p> <p>The `netcdf` directory contains the nominal half-degree runs necessary to generate nearly all of the plots from the paper. The one plot which is not reproducible from these data is the volume-integrated Eddy Kinetic Energy in the Spectral Element model. Storing high-resolution 4D wind fields requires a prohibitive amount of space. These data can be provided by the corresponding author, O.K. Hughes (owhughes@umich.edu). However, because this is several hundred GB of data I would strongly recommend generating these high-resolution runs yourself on your local system if you need them. Using 288 Intel Skylake cores (that is, 8 nodes each with two 18C processors) ran on the order of an hour.</p> <p><em>In order to generate the plots from the paper, you need only install NCL and then run</em> run.bash. Instructions for installing NCL<br> can be found in the `run.bash` script.</p> <p>Source Code Modifications<br> ----------------</p> <p><strong>CESM</strong><br> The `src` subdirectory contains the files `user_nl_cam` and `ic_baroclinic.F90`. Create a case using `--compset=FKESSLER` and `--run-unsupported` options when running `create_newcase`. If your case is located at `${CASE_DIR}`, then from within the directory containing this README, run `cp user_nl_cam ${CASE_DIR}/user_nl_cam`, and then run `cp ic_baroclinic.F90 ${CASE_DIR}/SourceMods/src.cam/`. Then build and run the model using the usual workflow.</p> <p><strong>MPAS</strong></p> <p>The MPAS code was run using a branch of the MPAS model provided by the model developers to the authors. While the source code modifications are provided in the `src` directory, I would strongly recommend contacting the corresponding author if you wish to run this test case in the MPAS codebase.</p>
Supplementary Materials: ImpactX Modeling of Benchmark Tests for Space Charge Validation
<p>Supplementary materials (aka data artifact or data archive) for our HB2023 publication: " ImpactX Modeling of Benchmark Tests for Space Charge Validation" (Paper ID: THBP44).</p> <p>This work was supported by the Director, Office of Science of the U.S. Department of Energy under Contracts No. DE-AC02-05CH11231 and DE-AC02-07CH11359. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Temporal Dynamics of Attitude Decisions: A Test of the Iterative Reprocessing Model using Event-related Potentials
<p>Experimental instructions, stimulus procedures and stimuli, single trial EEG data, decoding program</p>
Trident Test Model
Trident model I made while experamenting in Maya 2023 for 2 hours during my half term break. Any feedback would be greatly appreciated. Source: Objaverse 1.0 / Sketchfab
Excavation test - SD model
Just a simple test with low quality model of an archaeological excavation in Baldwinowice, Poland. Source: Objaverse 1.0 / Sketchfab
Testing an Evidence-Based Supported Employment Model in Autistic Young Adults
ClinicalTrials.gov study NCT06829264. IPD Sharing: NO. Countries: 1. Publications: 5.
VO2 Max: In Vivo Model for Functional Red Cell Testing. Can RECESS be Explained?
ClinicalTrials.gov study NCT02918851. IPD Sharing: NO. Countries: 1. Publications: 1.
Preventing Caries in Preschoolers: Testing a Unique Service Delivery Model in American Indian Head Start Programs
ClinicalTrials.gov study NCT01116739. IPD Sharing: Not stated. Countries: 1. Publications: 9.
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International Brain Laboratory public data
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OpenNeuro
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