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83 results for “Computational methods”
Bibliographic Data from the Computational Methods Applied to Earthen Historical Structures Review
<p>This database contains all the bibliographic information about the 293 records found after applying the Search Strategy used for the Computational Methods Applied to Earthen Historical Structures Review. Such strategy consisted on using relevant keywords grouped into three different search queries within ”TITLE-ABS-KEY”, for the years 2019-2023:</p> <ol> <li>(”earthen heritage” OR ”earthen historical building*” OR ”earthen historical structure*” OR ”earthen architect*” OR ”earthen monument*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (”computational method*” OR ”numerical analy*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (fem OR dem OR la OR ”finite element” OR ”discrete element” OR ”limit analysis”).</li> </ol> <p>The search was conducted on April 7, 2023.</p>
Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model
<p>This repository contains supplementary data for the journal paper:</p> <blockquote> <p>Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi: 10.3389/fnbot.2023.1179224</p> </blockquote> <p>It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive <strong>EMG_model_configs.zip</strong>.</p> <p><br> The files <strong>IP_tracking_opt_res.json</strong><a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and <strong>IP_tracking_opt_res.pkl</strong> contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored.</p> <p>For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.</p> <p> </p> <p>[1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152</p>
Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data
<p>These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).</p>
Optimizing the design of a bioabsorbable metal stent using computer simulation methods: Supporting Data
<p>Data including UMATs and Abaqus input files related to the paper 'Optimizing the design of a bioabsorbable metal stent using computer simulation methods' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.biomaterials.2013.07.010" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.biomaterials.2013.07.010</span></a></p> <p> </p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 3. Sharing and merging effect on successful allocation
<p>Fig (3) shows the effect of merging and sharing resources by auctioneer in term of success<br> rate of allocation. As shown in it, these factors improve successful allocation rate especially in<br> higher system load.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 2. bid value for time factor
<p>Each consumer is looking for utilizing its requested service with minimum price before its<br> deadline. To utilize a service all required resources should be allocated before deadline and<br> otherwise service failed to utilize and consumer must pay penalty to providers for all other<br> resources which is allocated to it. So Consumer should adjust its bid price rapidly to the acceptable<br> price of the market. Since consumers are generally sensitive to deadline in acquiring requested<br> service, it is intuitive to consider deadline time when formulating the bid price. Consumer agent<br> time dependent bid price formula is determined in.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 5. Comparison on successful allocation
<p>Figure 5 illustrates comparison of successful allocation rate between ICDA and other<br> methods. In the proposed method, intelligent allocation and also time consideration enable<br> consumers to acquire more resources before the deadline and as a result, the number of successful<br> allocation is higher than other methods.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 4. Sharing and tradeoff factor effect on resource utilization
<p>In Figure 4 we consider tradeoff and sharing factors in providers. The result illustrates that<br> by using these factors providers improve resource utilization. Higher resource utilization motivates<br> more providers to participate in the cloud and also enables the cloud market to handle more<br> consumers which influences market efficiency.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 1. Resource allocation schema in proposed method
<p>We assume that the resources allocation satisfies the following conditions:<br> • The quantity of a resource can be measured in arbitrary units (e.g. 60 units of resource<br> A).<br> • A resource can be divided into an arbitrary fraction (e.g. a resource of 60 units is divided<br> into 20 units for consumer 1 and 40 units for consumer 2).<br> • A resource request of a service can be divided into sub-requests and acquired from<br> multiple providers (e.g. a resource request of 40 units utilized as 10 units from provider<br> 1 and 30 units from provider 2).<br> Figure 1 shows a cloud computing environment with the proposed mechanism.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 19. Accuracy of classification using the three methods: KNN, SVM and our method for MCI subjects
<p>Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 20. The accuracy of classification using the three methods, KNN, SVM and our method, for AD subjects
<p>Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 18. Accuracy of classification using the three methods, KNN, SVM and our method, for normal subjects
<p>We present three figures representing the accuracy of the classification using the three methods, KNN, SVM and our method for normal, MCI and Alzheimer subjects. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus</p> <p> </p>
Survey and Interview Data from Mixed-Method Survey of Serverless Computing and Function-as-a-Service Software Development in Industrial Practice
<p>This dataset contains the almost-raw data resulting from two out of the three methods chosen by the researchers for their namesake study «A Mixed-Method Empirical Study of Function-as-a-Service Software Development in Industrial Practice». Among the files are web survey questions, anonymised survey results, and interview guidelines. We encourage other researchers to perform open coding and other analysis techniques on the data to verify our claims and to generate new insights.</p>
Syntheses of aircraft noise obtained by computational methods
<p>In ANIMA WP4, where focus is put on toolset development, a benchmark on three partners’ auralization tools was performed.</p> <p>These tools are used to reproduce the sound of an aircraft flyover from either physical modelling of noise, a prediction based on measurement or a combination of both. As the chosen methodologies and the modelling hypotheses are different between partners, a benchmark was performed to assess the impact of these strategies on the produced sound synthesis.</p> <p>The realism of each auralization was evaluated through comparison to experimental recordings. For propriety reasons, only the synthesized sounds are available here, and can be compared between each other.</p> <p>Two of the three tools were further used in the WP3 task dedicated to Virtual Reality, see "<a href="https://doi.org/10.5281/zenodo.5517218">Virtual reality simulated aircraft flyovers: Influence of the landscape on the overall pleasantness of the environment</a>"</p> <p>The sounds represent three flight configurations, one landing, and two take-offs with different engine speeds. Two aircraft are considered, one single-aisle and one double-aisle aircraft. The synthesis is performed at a receiver position below the aircraft trajectory.</p> <p>For more information, please contact:</p> <ul> <li><a href="mailto:Ingrid.legriffon@onera.fr">Ingrid.legriffon@onera.fr</a> (ONERA)</li> <li><a href="mailto:isabelle.boullet@airbus.com">isabelle.boullet@airbus.com</a> (Airbus Aviation)</li> <li><a href="mailto:jean-michel.boiteux@safrangroup.com">jean-michel.boiteux@safrangroup.com</a> (Safran Aircraft Engine)</li> </ul> <p> </p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment
<p>This open access repository houses the dataset utilized in the research article:</p><p>Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p> </p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p> </p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p> </p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><p> </p>
Output data computed for the solution of Maxwell's equations on a distributed system using the FDTD method
<p>This is an example output dataset from the distributed computation to solve Maxwell's equations using the FDTD method. This is an example from the report describing the implementation of the computer program - Gillan and Fusco (1998). The example is based on the work reported in the book by Kunz and Leubbers on the FDTD method. It consists of scattering a Gaussian pulse from a sphere of dielectric defined with a 3D cube of size 34x34x34 Yee cells. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.