Exports required model results as text files, for reading into Matlab. Must be run from command line (after loading Abaqus module) thus: abaqus python ExportModelEndState02a.py Output databases... QUT Research Data Respository Dataset Resource available for download

Rofiyev Matlab Manobovich Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

PIVlab is a time-resolved particle image velocimetry (PIV) software that does not only calculate the velocity distribution within particle image pairs, but can also be used to derive, display and export multiple parameters of the flow pattern. A user-friendly graphical user interface (GUI) makes PIV analyses and data post-processing fast and efficient.Video about the tool:https://www.youtube.com/watch?v=Sp3Ounq07QcExample analyses & videos can be found on the PIVlab website:http://PIVlab.blogspot.com Please ask your questions in the PIVlab forum: http://pivlab.blogspot.de/p/forum.html

Peer reviewed paper on PIVlab: Thielicke, W and Stamhuis, E.J. 2014. PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB. Journal of Open Research Software 2(1):e30, DOI: http://dx.doi.org/10.5334/jors.bl

Main features:* completely GUI based PIV tool* multi-pass, multi grid window deformation technique* import bmp/ tiff/ jpeg image pairs/ series* image sequencing styles A-B, C-D, ... or A-B, B-C, ...* individual image masking and region of interest (ROI) selection* image pre-processing (contrast enhancement, highpass, intensity capping)* two different sub-pixel estimators* multiple vector validation methods* magnitude/ vorticity/ divergence/ shear / ...* data smoothing, vector field highpass* multiple colormaps* streamlines* extensive data extraction tools/ integration via poly lines/ circles/ area* statistics (histograms, scatterplot, mean & stdev)* precise particle image pair generation with user-defined parameters and several flow simulations (synthetic PIV image generator)* data export (matlab, ascii, movie file, image, Paraview, ...)* main features accessible via comand line scripting

Dilmurodova Matlab Tadjiyevna Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

We provide MATLAB binary files (.mat) and comma separated values files of data collected from a pilot study of a plug load management system that allows for the metering and control of individual electrical plug loads. The study included 15 power strips, each containing 4 channels (receptacles), which wirelessly transmitted power consumption data approximately once per second to 3 bridges. The bridges were connected to a building local area network which relayed data to a cloud-based service. Data were archived once per minute with the minimum, mean, and maximum power draw over each one minute interval recorded. The uncontrolled portion of the testing spanned approximately five weeks and established a baseline energy consumption. The controlled portion of the testing employed schedule-based rules for turning off selected loads during non-business hours; it also modified the energy saver policies for certain devices. Three folders are provided: “matFilesAllChOneDate” provides a MAT-file for each date, each file has all channels; “matFilesOneChAllDates” provides a MAT-file for each channel, each file has all dates; “csvFiles” provides comma separated values files for each date (note that because of data export size limitations, there are 10 csv files for each date). Each folder has the same data; there is no practical difference in content, only the way in which it is organized.

The “Final_matrices” excel file contains research output related to the paper “From exports to value added to income: Accounting for bilateral income transfers”. Details on how the data are assembled can be found in the paper and in its online appendix. Replication files (R-files and Matlab-codes) as well as the raw data needed for replication of all empirical results in the paper are available upon request from the author.

We provide MATLAB binary files (.mat) and comma separated values files of data collected from a pilot study of a plug load management system that allows for the metering and control of individual electrical plug loads. The study included 15 power strips, each containing 4 channels (receptacles), which wirelessly transmitted power consumption data approximately once per second to 3 bridges. The bridges were connected to a building local area network which relayed data to a cloud-based service. Data were archived once per minute with the minimum, mean, and maximum power draw over each one minute interval recorded. The uncontrolled portion of the testing spanned approximately five weeks and established a baseline energy consumption. The controlled portion of the testing employed schedule-based rules for turning off selected loads during non-business hours; it also modified the energy saver policies for certain devices. Three folders are provided: “matFilesAllChOneDate” provides a MAT-file for each date, each file has all channels; “matFilesOneChAllDates” provides a MAT-file for each channel, each file has all dates; “csvFiles” provides comma separated values files for each date (note that because of data export size limitations, there are 10 csv files for each date). Each folder has the same data; there is no practical difference in content, only the way in which it is organized.

Build a trapezoidal PCHE two channel model using FLUNT software as a unit for processing, simulate by changing different input conditions, obtain corresponding results, export them as CSV files, use Python for data processing, remove unnecessary information columns, and combine certain information from each file to form a snapshot matrix CSV file. After processing the snapshot matrix CSV file in Python, import it into MATLAB for prediction, and finally export the MATLAB results as a result CSV file.

Research topic: Development of upper-ocean aggregation models useful for interpreting and predicting carbon fluxes

Understanding the mechanisms and rates of carbon removal from surface waters remains an important goal of the Joint Global Oceanographic Flux Study (JGOFS). Particle formation and sinking is an important process for such removal. Much of the particulate fraction in surface waters is in the form of small cells having slow sinking rates. For these cells to sink more rapidly, they need to be packaged into larger particles. Fecal pellet production by animals provides one way of doing this; aggregate formation another. Because aggregates are the dominant form of sedimenting particles, understanding the processes that form and destroy aggregates is crucial for JGOFS to achieve its goal.

This proposal seeks to obtain support to develop models that will increase our understanding of the processes affecting organic matter export from the surface mixed layer. To this end, the models will combine particle aggregation models with plankton food web models. We propose to use data sets from the JGOFS process and time-series studies to determine and refine the ability of the models to predict carbon export.

The approach will be to combine the techniques we have refined in modeling algal blooms with food web models of the surface mixed layer to understand the effect that aggregation has on carbon export flux. We will work with a two dimensional particle size spectrum that will allow us to differentiate the effects of collisions with a marine snow particle from those with fecal pellet of the same mass. We expect to determine the key parameters governing the vertical particle flux from the mixed layer.

We will use data collected during the JGOFS field programs to refine the models. Combining the simulation results with JGOFS field data will increase our understanding of the processes affecting vertical export fluxes and improve the accuracy of flux predictions made using the models. The results of this work will increase significantly our ability to accurately describe the movement of organic material from the surface to the deeper parts of the ocean.

ABSTRACT. The Genes Software is useful for analyzing and processing phenotypic and molecular data using different biometric models. In the current version we dispose routines to integrate it with three other softwares: the R, Matlab and Selegen. This version allows in plant and animal breeding complementary analyzes in several breeding research fields as genomic selection, prediction of genetic values, use of neural networks and Fuzzy logic. The Genes is important to estimate parameters for understanding biological phenomena necessary to make decisions and predict the success and viability of strategic selection. The original programme can be downloaded in Portuguese, English or Spanish with the specific literature from (http://www.livraria.ufv.br/) and the user guide from (http://www.ufv.br/dbg/genes/genes.htm and http://www.ufv.br/dbg/biodata.htm). The user has also support in the address www.facebook.com/ GenesNews. The Genes is also integrated in the application softwares MS Word, MS Excel and Paint to efficiently import data and export results as numbers and figures.

This repository contains data accompanying the publication "Effects of low-level electric vehicle noise on attention, electrodermal activity, workload, and annoyance", submitted for review to the Journal of the Acoustical Society of America (JASA).

The dataset contains:

• stimuli.zip: Stimuli recordings, both indoor at participant position and outdoor in front of the Window. All sounds were exported as calibrated 32-bit float wav files; i.e., reading the files into Matlab with audioread results in calibrated Pa values.
  • Sound pressure of all evaluated stimuli, binaurally recorded at participant position
    • indoor_twotone.wav
    • indoor_multitone.wav
    • indoor_noise.wav
    • indoor_silence.wav
  • Sound pressure of traffic stimuli, recorded in front of the outdoor side of the window (without window Rw compensation filter)
    • outdoor_twotone.wav
    • outdoor_multitone.wav
    • outdoor_noise.wav
• evaluation.zip: Data and Matlab routines used for statistical evaluation.
  • preProcessResults.m: script to pre-process the raw results and create one large table with all data.
  • analyzeResults.m: script to perform statistical evaluation on pre-processed data, generate plots, and export a detailed statistics report.
  • statisticalEvaluation.pdf: detailed statistics report, generated by analyzeResults.m
  • data: Folder with raw data of attention test, workload and annoyance questionnaire, EDA recordings, as well as processed data table generated by preProcessResults.m.
    
    • rawAttentionData.csv
    • rawQuestionnaireData.csv
    • rawEdaData.mat
    • processedData.csv
  • figures: Folder with all figures generated by analyzeResults.m

This project presents tests for sterilized intervention effectiveness evaluation for small open commodity exporting economy. The time series used in the estimation includes 618 daily observations from 6 November 2014 to 20 April 2017. The oil price is the price of the OPEC basket in US dollars per barrel. The forex rate is the average spot rate (with delivery TODAY) on the Moscow Interbank Currency Exchange (MICEX) measured in Russian rubles per US dollar. The calculation of the sterilized intervention shock is based on the results of repo auctions which were related to average daily export of oil, gaz and oil product taken from Russian Balance of Payments. The data are stored in the data_daily_till_20_04_17.mat file which is in the Dynare_Matlab.zip archive. They are also contained in JMulti project (VECM_Mendeley.zip archive). In both sources the data include information about repo auctions Bank of Russia (BoR) conducted in 2014-2017. Repo maturity: 7 days (series dz7, dzpos7, dzneg7 in Matlab; series Z7, Zpos7, Zneg7 in JMulti) 28 days (series dz28, dzpos28, dzneg28 in Matlab; series Z28, Zpos28, Zneg28 in JMulti) 1 year (series dz1y, dzpos1y, dzneg1y in Matlab; series Z1y, Zpos1y, Zneg1y in JMulti) In all estimation procedures I use aggregated series: (series dz, dzpos, dzneg in Matlab; series Z, Zpos, Zneg in JMulti). "pos" means positive interventions (more USD distributed by BoR) "neg" means negative interventions (less USD distributed by BoR)

The project includes 1. Estimation of theoretical general equilibrium model constructed for exchange rate determination in small open commodity exporting economy. It is performed in Dynare/Matlab software. 2. Estimation of vector error correction model. It is performed in JMulti software.

Both models are used for evaluation of sterilized intervention effectiveness. It means that stable relationship between exchange rate and interventions is explored. To get more information about the model used and about results I report see my working paper: https://ideas.repec.org/p/hig/wpaper/170-ec-2017.html

Raw data export with EthoVision XT 11.5 and data analyses with MATLAB.

INTRODUCTION: The dataset of the full raw electrophoretic data, named Expected Protein Profile workbook, is treated to be imported to MATLAB. Please refer to https://doi.org/10.5281/zenodo.7054406 to have access to the full dataset and to the preliminary methodology ("EPPStrategyDataExport") used in the electrophoretic data. This treated electrophoretogram was named "ExportForMOdeLINGVis" and can also be consulted at https://tinyurl.com/ExportForMOdeLINGVis.

METHODOLOGY: The electrophoretic dataset was treated and imported to MATLAB. First, the section "Present in the following subphenomes" was added, which comprised binary variables (present/absent) to identify in which subgroup the Molecular Bands were present. Secondly, the previously obtained preliminary triple entry table was added to the Molecular Bands Summary for each Subject (ex: D01309). Lastly, the database was implemented into the MODeLING.Vis toolbox and the variables were imported into arrays. Those arrays indexed a linear matrix of the variables: Molecular Bands Subgroups (kDa) and Concentration (ng/µl) of each sample (subject). For the full methodology, please consult https://doi.org/10.5281/zenodo.7041477

This repository contains datasets and figure-generation scripts supporting "Dam-driven phosphorus sinks reversed the anticipated increase in phosphorus export from the Yangtze River Basin":"figure_code_data" contains MATLAB source code and the source data used to generate all figures."Observation_data" contains observations of site-level riverine total phosphorus flux, reservoir-specific sediment TP concentrations and deposition rates, and lake-specific sediment TP concentrations compiled from published studies.