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Replication Package for a Systematic Literature Mapping of Agility in Safety-Critical Software Development within the Aerospace Industry
<p>This file collection package facilitates the replication of a Systematic Literature Mapping (SLM) focused on Agility in Safety-Critical Software Development within the Aerospace Industry. Authored by J. Eduardo Ferreira Ribeiro, João Gabriel Silva, and Ademar Aguiar, this dataset is dedicated to improving transparency and reproducibility in this field of study and future research.</p> <p>Specifically, the package includes:</p> <ul> <li><a href="https://github.com/zemacedo99/Replication-Package-Builder/releases/tag/v1.0.2">Replication Package Builder Version 1.0.2</a></li> <li>A list of terms (both inclusion and exclusion) used to construct the research string.</li> <li>A list of venues unrelated to the research topic, to be excluded from the results.</li> <li>The inclusion and exclusion criteria applied during the study.</li> <li>Lists of publication results from indexing services like Scopus, IEEE Xplore, Science Direct, HAL Open Science, Springer Nature, and the ACM Digital Library are all provided in CSV file format.</li> <li>A list of all publications in CSV format, compiled after the automated exclusion phase using the established inclusion and exclusion criteria.</li> <li>Finally, a complete list of all publications, including those from Snowball sampling, in XLSX format was compiled after the manual exclusion phase using the established inclusion and exclusion criteria.</li> </ul> <p>Compiled and published on Saturday, September 14, 2024, this dataset is crucial for researchers seeking to replicate or extend the SLM's findings.</p> <p>Lastly, we thank J. Antonio Dantas Macedo for contributing to developing and providing this <a href="https://github.com/zemacedo99/Replication-Package-Builder">replication package builder</a>.</p>
Business Model Development_Video 1
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From Tech Feasibility to Product Develop_Video 1
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From Tech Feasibility to Product Develop_Video 3
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From Tech Feasibility to Product Develop_Video 2
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Business Model Development_Video 3
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Business Model Development_Video 2
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SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
<p>The SynthSOD dataset contains more than 47 hours of multitrack music obtained by synthesizing orchestra and ensemble pieces from the <a href="https://qsdfo.github.io/LOP/database.html" target="_blank" rel="noopener">Symbolic Orchestral Database (SOD)</a> using Spitfire BBC Symphony Orchestra Professional Library. To synthesize the MIDI files from the SOD, we needed to fix the original files into the General MIDI standard, select a subsect of files that fitted into our requirements (e.g., containing only instruments that we could synthesize), and develop a new system to generate musically-motivated random annotations about tempo, dynamic, and articulation. The code to replicate this process is available in <a href="https://github.com/repertorium/HQ-SOD-generator" target="_blank" rel="noopener">our repository</a> and all the details can be read in <a href="https://doi.org/10.1109/OJSP.2025.3528361" target="_blank" rel="noopener">our paper</a>. We have also published the code to train and evaluate the baseline and the pre-trained models in a <a href="https://github.com/repertorium/SynthSOD-Baseline" target="_blank" rel="noopener">GitHub repository</a>.</p> <p>We have also published the aligned score information for most of the pieces <a href="https://doi.org/10.5281/zenodo.14971533">here</a>.</p>
Data Set for the Development and Testing of the MC23 Nonclassical-Energy Functional
<p>This dataset contains files used to train and test the Multi-Configuration 23 (MC23) functional and to compare the results to other methods. It includes files to carry out electronic structure calculations. These include molecular geometries in xyz format, <em>OpenMolcas</em> input files for CASSCF calculations, converged CASSCF natural orbitals, <em>OpenMolcas</em> basis set files, and <em>Gaussian 16</em> formatted checkpoint files for KS-DFT calculations. It also includes data used for data processing such as stoichiometries, absolute energies, and reference energies.</p> <p>Each file in this dataset is a .tar.xz archive. One can extract them by the following command:</p> <pre>tar -xJf name_of_archive.tar.xz</pre> <p>Below is a description of the content of each archive.</p> <p><strong>gaussian_16_fchk.tar.xz</strong> contains <em>Gaussian 16</em> formatted checkpoint files for all KS-DFT calculations used in this work. The files in the archive are named as <em>functional</em>/<em>database</em>/<em>system</em>.fchk</p> <p><strong>openmolcas_basis_set.tar.xz</strong> contains <em>OpenMolcas</em> basis set files used for multireference calculations. To reproduce the results in this work, the basis set files should be placed in the “basis_library” directory in the <em>OpenMolcas</em> installation location.</p> <p><strong>openmolcas_wave_function.tar.xz</strong> contains files needed by <em>OpenMolcas</em> to reproduce the CASSCF wave function used in this work. The files in the archive are named <em>database</em>/<em>system</em>.*.</p> <ul> <li>The file <em>system</em>.xyz contains the Cartesian coordinates. Note that for Data Set 2, the coordinates are in the input files <em>system</em>.inp.</li> <li>The file <em>system</em>.inp contains the <em>OpenMolca</em>s input file to perform CASSCF calculations.</li> <li>The files <em>system</em>.RasOrb, <em>system</em>.rasscf.h5, and <em>system</em>.rasscf.molden contain the converged CASSCF natural orbitals.</li> </ul> <p><strong>gaussian_16_stoichiometry_energy.tar.xz</strong> and <strong>openmolcas_stoichiometry_energy.tar.xz</strong> contain files used for data processing.</p> <ul> <li>Files with names like <em>database</em>.ref contain information used to calculate the final energies and errors. They are tab-delimited files. Each row represents an energy difference (e.g. atomization energy, barrier height, etc.). The first column contains the name of the energy difference (note: spaces may be present in this column). This is followed by the file names of each electronic structure calculation and the stoichiometries used to calculate the energy difference from the absolute energies. Each name or stoichiometry occupies one column. The second from the last column contains the reference value in kcal/mol. The reference values contain spin–orbit coupling. The last column contains the factor by which the final energy should be divided. This factor usually equals 1, but it can be greater than 1 for databases calculating atomization energies per bond or per atom.</li> <li>Files with names like <em>method</em>.elist contain the absolute energies of each electronic structure calculation. They are tab-delimited files. Each row represents an electronic structure calculation, and each row always contains two columns. The first column is the file name of the calculation in the format <em>database</em>/<em>system</em>. The second column is the absolute energy in atomic units extracted from the output file of electronic structure programs.</li> <li>The file named SOC.dat contains the spin–orbit coupling term in kcal/mol to be added to each electronic structure calculation prior to calculating energy differences. It has the same format as files with names like <em>method</em>.elist.</li> </ul> <p>The database names in the directory names use a slightly different convention than the ones in the article describing MC23. A prefix DS2_ or DS3_ is used to indicate the data set to which a database belongs, and the number of data points is removed from the database name. For example, the MR-MGN-BE8 database from Data Set 2 has a file name DS2_MR-MGN-BE.</p>
Table 3 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
<p><i>Table 3.</i> Gestation and brain size. The predicted gestation period was derived by applying the Sacher and Staffeldt formula and using our brain mass data. Sheep (<i>O. aries</i>), cows (<i>B. taurus</i>), giraffes (<i>G. camelopardalis</i>), and hippopotamuses (<i>H. amphibius</i>) were included in the table to compare cetaceans to other members of the Cetartiodactyla taxonomic order. Humans (<i>H. sapiens</i>) were also included for comparison. Cetaceans appear to have similar neonatal/adult brain mass ratios compared to other animals of the Cetartiodactlya order. Sources for the published gestation durations and cetacean brain masses can be found in Table S1.</p><table><thead><tr><th></th><th></th><th></th><th></th><th>Published</th><th>Predicted</th></tr></thead><tbody><tr><th>Taxonomic family</th><td>Neonatal</td><td>Adult brain</td><td>Neonate/</td><td>gestation</td><td>gestation</td></tr><tr><th>Genus species</th><td>brain mass (g)</td><td>mass (g)</td><td>adult (%)</td><td>(days)</td><td>(days)</td></tr><tr><th colspan="6">Delphinidae</th></tr><tr><th><i>C. commersonii</i></th><td>370</td><td>783</td><td>47.3</td><td>334</td><td>324</td></tr><tr><th><i>D. delphis</i></th><td>430</td><td>715</td><td>60.2</td><td>363</td><td>359</td></tr><tr><th><i>G. griseus</i></th><td>796</td><td>2,132</td><td>37.3</td><td>410</td><td>386</td></tr><tr><th><i>L. acutus</i></th><td>733</td><td>1,285</td><td>57</td><td>365</td><td>401</td></tr><tr><th><i>L. obliquidens</i></th><td>523</td><td>1,198</td><td>43.6</td><td>356</td><td>352</td></tr><tr><th><i>O. orca S. attenuata S. longirostris</i></th><td>3,006 353 247</td><td>6,642 711 541</td><td>45.3 49.6 45.6</td><td>553 — —</td><td>566 304a 286a</td></tr><tr><th><i>S. bredanensis</i></th><td>706</td><td>1,454</td><td>48.6</td><td>378</td><td>388</td></tr><tr><th><i>T. truncatus</i></th><td>685</td><td>1,550</td><td>44.2</td><td>376</td><td>377</td></tr><tr><th colspan="6">Monodontidae</th></tr><tr><th><i>D. leucas</i></th><td>938</td><td>2,087</td><td>44.9</td><td>456</td><td>414</td></tr><tr><th colspan="6">Phocoenidae</th></tr><tr><th><i>P. phocoena</i></th><td>242</td><td>506</td><td>47.7</td><td>316</td><td>266</td></tr><tr><th><i>P. dalli</i></th><td>270</td><td>803</td><td>33.6</td><td>334</td><td>282</td></tr><tr><th colspan="6">Physeteridae</th></tr><tr><th><i>P. macrocephalus</i></th><td>3,308</td><td>7,693</td><td>43</td><td>547</td><td>582</td></tr><tr><th colspan="6">Pontoporiidae</th></tr><tr><th><i>P. blainvillei</i></th><td>154.9</td><td>223.9</td><td>69.2</td><td>319</td><td>271</td></tr><tr><th colspan="6">Ziphiidae</th></tr><tr><th><i>M. europaeus</i></th><td>971</td><td>1,680</td><td>57.8</td><td>—</td><td>—</td></tr><tr><th colspan="6">Balaenopteridae</th></tr><tr><th><i>B. physalus</i></th><td>2,640</td><td>6,718</td><td>39.3</td><td>342</td><td>537</td></tr><tr><th>Bovidae <i>B. taurus O. aries</i></th><td>199b 69</td><td>456b 130d</td><td>43.6 53</td><td>278c 150e</td><td>270 208</td></tr><tr><th>Giraffidae <i>G. camelopardalis</i></th><td>428f</td><td>537f</td><td>79.7</td><td>459c</td><td>363</td></tr><tr><th>Hippopotamidae <i>H. amphibius</i></th><td>195b</td><td>590b</td><td>33.1</td><td>240e</td><td>258</td></tr><tr><th>Hominidae <i>H. sapiens</i></th><td>380g</td><td>1,400b</td><td>27</td><td>280e</td><td>324</td></tr></tbody></table><p><sup>a</sup> Perrin <i>et al.</i> (1977).</p><p><sup>b</sup> Sacher and Staffeldt (1974).</p><p><sup>c</sup> Kiltie (1982).</p><p><sup>d</sup> Minervini <i>et al.</i> (2016).</p><p><sup>e</sup> Hayssen <i>et al.</i> (1993).</p><p><sup>f</sup> <i>Gra¨ıc et al.</i> (2017).</p><p><sup>g</sup> Blinkov and Glezer (1968).</p>
Table 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
<p><i>Table 2.</i> Comparison of seven terrestrial cetartiodactyls (and the African elephant) with eight aquatic cetartiodactyls on brain and body mass for neonates and adults. ABoM = adult body mass; ABrM = adult brain mass; NBoM = neonatal body mass; NBrM = neonatal brain mass. All brain and body mass data for the aquatic species come from Table S1.</p><table><thead><tr><th></th><th></th><th>ABoM</th><th>ABrM</th><th>NboM</th><th>NBrM</th><th>Aquatic</th><th></th><th>AboM</th><th>ABrM</th><th>NboM</th><th>NBrM</th></tr></thead><tbody><tr><th>Terrestrial species</th><td>Common name</td><td>(kg)</td><td>(g)</td><td>(kg)</td><td>(g)</td><td>species</td><td>Common name</td><td>(kg)</td><td>(g)</td><td>(kg)</td><td>(g)</td></tr><tr><th><i>D. dorcas phillipsi S. scrofa</i></th><td>Blesbok antelope Wild boar</td><td>60a 149b</td><td>155a 133b</td><td>— —</td><td>— —</td><td><i>D. delphis L. acutus</i></td><td>Common dolphin Atlantic white-sided</td><td>68 156</td><td>715 1,285</td><td>11 28</td><td>430 733</td></tr><tr><th><i>T. strepsiceros G. camelopardalis C. bactrianus</i></th><td>Greater kudu Giraffe Bactrian camel</td><td>218a 470c 594d</td><td>307a 537c 518d</td><td>— 150c —</td><td>— 428c —</td><td><i>T. truncatus G. griseus G. macrorhynchus</i></td><td>dolphin Bottlenose dolphin Risso’s dolphin Short-finned pilot</td><td>190 301 654</td><td>1,550 2,132 2,679</td><td>18 85 —</td><td>685 796 —</td></tr><tr><th><i>B. taurus H. amphibius</i></th><td>Cow Hippopotamus</td><td>598e 1,351f</td><td>492e 720f</td><td>25g 40g</td><td>199g 195g</td><td><i>D. leucas G. melas</i></td><td>whale Beluga Long-finned pilot</td><td>560 1,369</td><td>2,087 3,499</td><td>50 —</td><td>938 —</td></tr><tr><th><i>L. africana</i></th><td>African elephant</td><td>5,000a</td><td>4,619a</td><td>—</td><td>1,724h</td><td><i>O. orca</i></td><td>whale Killer whale</td><td>3,723</td><td>6,642</td><td>171</td><td>3,006</td></tr></tbody></table><p><sup>a</sup> Herculano-Houzel (2015).</p><p><sup>b</sup> Minervini <i>et al</i>. (2016).</p><p><sup>c</sup> <i>Gra¨ıc et al</i>. (2017).</p><p><sup>d</sup> Xie <i>et al.</i> (2011).</p><p><sup>e</sup> Ballarin <i>et al</i>. (2016).</p><p><sup>f</sup> Silva and Downing (1995).</p><p><sup>g</sup> Sacher and Staffeldt (1974).</p><p><sup>h</sup> Shoshani <i>et al.</i> (2006).</p>
Ideas development sketchbook
<p>A sketchbook that was produced by the artist Garry Barker whilst undertaking several ceramics based projects as well as research into the visualisation of interoception. The ideas move back and forth, sometimes being to do with the realisation of 'fetish' concepts and at other times to help realise thoughts related to the loss of migrants at sea, an often reoccuring idea.. The sketchbook is A3 in size and the paper slightly pourous, which enables for a certain 'roughness' in the quality of the image making, something that helps keep the imagery 'live' and not too 'finished'. </p>
Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Database of indicators of socio-economic development of the Great Altai regions in the post-Soviet period
<p>As part of the research work "Altai vector of Eurasian economic integration: cross-border challenges, effects, strategic objectives and priorities for the Altai Krai" (FZMW-2023-0015), the task was to develop a database of indicators of development of cross-border regions of Russia, Kazakhstan, Mongolia and China for the period 1990-2022. To solve the task, statistical data from official sources were used. The database contains 68 indicators of socio-economic development of the Great Altai region, grouped into 10 structural blocks. The temporal resolution of the data is 1 year. The data were preliminarily analyzed for outliers and inconsistencies. On the basis of econometric methods, processing was performed to restore short-term gaps in the time series of the data. The generated database represents a unique set of socio-economic and climatic indicators for the transboundary study area, allowing to solve the problems of modeling and analysis of agricultural production dynamics in the post-Soviet period.</p>
Data on the Digital Index (DiGiX), the E-Government Development Index (EGDI), and the Global Talent Competitiveness Index (GTCI)
<p>This dataset contains the measurement of the Digital Index (DiGiX), the E-Government Development Index (EGDI), and the Global Talent Competitiveness Index (GTCI) in 2022.</p> <p> </p> <p>References:</p> <p>The Global Talent Competitiveness Index 2023 report: https://www.insead.edu/system/files/2023-11/gtci-2023-report.pdf</p> <p>UN E-Government Knowledgebase: https://publicadministration.un.org/egovkb/Data-Center</p> <p>Camara, N., (2022), DiGiX 2022 Update: A Multidimensional Index of Digitization, https://www.bbvaresearch.com/wp-content/uploads/2022/06/DiGiX_2022_Update_A_Multidimensional_Index_of_Digitization.pdf</p>
Assessment of Simulations in Faust and Tascar for the Development of Audio Algorithms in Acoustic Environments - Code and Data
<p>Developing and testing audio algorithms with hard real-time constraints can be a complex task, requiring certain programming skills and/or<br>specialized equipment. However, many things can be tested in simulations on an ordinary computer, using <a href="https://tascar.org/" target="_blank" rel="noopener">TASCAR</a> for acoustic scene creation and<br><a href="https://faust.grame.fr/" target="_blank" rel="noopener">FAUST</a> for signal processing. Their capability are evaluated and compared to measurements using an FxLMS algorithm for active noise control as<br>example. This repository contains code and measured data of the publication “Assessment of simulations in FAUST and TASCAR for the development of<br>audio algorithms in acoustic environments”, presented at the International Faust Conference 2024 in Turin, Italy.</p>
Design values of the detectors developed in the project PrimA-LTD
<p>Design values and expected energy resolution at <em>T</em> = 20 mK of all detectors developed for PrimA-LTD. The detectors are designed to have a signal rise time of 1 μs and a signal decay time of 1 ms. A SQUID white noise level of 0.15 μPhi0 ∕√Hz and SQUID 1/f noise level at 1 Hz of 3 μPhi0 ∕√Hz is assumed.</p>
Figures 13-18. Phyllocnistis citrella pupae. 13 in Histology and histochemistry of Phyllocnistis citrella Stainton (Lepidoptera: Gracillariidae) fat body during the post embrionary development
Figures 13-18. Phyllocnistis citrella pupae. 13. Sagittal and frontal section pupae. Hematoxilin- Eosin technique (HE). 14. Fat body (FB). Anterior abdominal region with spherical trophocytes (t) and cytoplasm filled with intensely acidophilic granules. (HE). 15. Fat body (FB). Median region with numerous intensely acidophilic granules and numerous strongly basophillous plasmatocytes (p). Oenocytes (o). (HE). 16. Fat body (FB). Median region with PAS-positive granules and big intensely basophillous plasmatocytes (p). Periodic Acid-Schiff technique- Hematoxilin (PAS-H). 17. Fat body (FB). Median region with Sudan III-positive lipidic droplets. Sudan III technique (SIII). 18. Fat body (FB). Median region with increase in the lipidic acid concentration related to neutral lipids. (NB).
Figures 1-6. Phyllocnistis citrella larvae. 1. Sagittal and frontal section larval instar I, II in Histology and histochemistry of Phyllocnistis citrella Stainton (Lepidoptera: Gracillariidae) fat body during the post embrionary development
Figures 1-6. Phyllocnistis citrella larvae. 1. Sagittal and frontal section larval instar I, II, III. Hematoxilin- Eosin technique (HE). 2. Larva I: Parietal fat body (PFB) with trophocytes (t) showing irregular shape and basophilic cytoplasm with few droplets. (HE). 3. Larva II: Parietal fat body (PFB) made up of masses of trophocytes (t) with abundant cytoplasmic droplets. (HE). 4. Larva III: Spongy visceral fat body (VFB) in contact with the silk glands (SG). (HE). 5. Larva III: Fat body (FB) with big cumuli of Sudan Black positive lipidic droplets. Sudan Black technique (SB). 6. Larva III: Fat body (FB) with high concentration of neutral lipidic droplets surrounded by acid lipid granules of different sizes. Nile Blue technique (NB).
Data for "Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations"
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