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11 results for “core losses”
Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis: Reproducibility Dataset
<p><strong>Paper</strong></p> <p>This package contains the datasets used in the following paper:</p> <ul> <li><strong>Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis</strong></li> <li><strong>Thomas Guillod, Jenna S. Lee, Haoran Li, Shukai Wang, Minjie Chen, and Charles R. Sullivan</strong></li> <li><strong><a href="https://doi.org/10.1109/APEC43580.2023.10131348">https://doi.org/10.1109/APEC43580.2023.10131348</a></strong></li> <li><strong>IEEE APEC 2023, Orlando, Florida, USA</strong></li> </ul> <p><strong>Datasets</strong></p> <p>The EPCOS TDK N87 datasets used in this paper are part of the MagNet initiative (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>). MagNet is an openly available large-scale dataset including measurements of several core materials under various operating conditions. MagNet is a joint project between Princeton University, Dartmouth College, and Plexim GmbH.</p> <p>This package includes the two datasets used in the paper:</p> <ul> <li>"N87_ambient_temperature" - Loss dataset for EPCOS TDK N87 at ambient temperature (measured on a R22.1X13.7X7.9, 2022-02-01).</li> <li>"N87_variable_temperature" - Loss dataset for EPCOS TDK N87 at different temperatures (measured on a R34.0X20.5X12.5, 2022-07-14).</li> </ul> <p>It should be noted that the datasets contains more measurements than used in the paper:</p> <ul> <li>The measurements where the iGCC can be evaluated without extrapolation are used in the paper.</li> <li>The measurements where the iGCC require an extrapolation of the loss data are not used in the paper.</li> <li>A flag in the dataset indicates in which category a measurement belongs.</li> </ul> <p>More details about the measurement setup can be found on the MagNet website (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>).</p> <p>More details about the iGCC method can be found on GitHub (<a href="https://github.com/otvam/magnet_webinar_eqn_models">https://github.com/otvam/magnet_webinar_eqn_models</a>).</p> <p><strong>File Formats</strong></p> <p>The datasets are available in three different formats:</p> <ul> <li>CSV (text files).</li> <li>MATLAB tables (MAT v7.3 binary files, exported with MATLAB 2021a).</li> <li>Pandas dataframes (HDF5 binary files, exported with Python 3.10.6 and Pandas 1.3.5).</li> </ul> <p>The file "dataset_metadata.csv" contains the description of the different variables.<br>The file "test_matlab.m" is a MATLAB test file for loading the MATLAB tables.<br>The file "test_python.py" is a Python test file for loading the Pandas dataframes.</p>
Core-loss EELS dataset and neural networks for element identification
<p>We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification. </p> <p>The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra.</p> <p>Code on how to <br> - read the simulated data<br> - transform HDF5 format to TFRecord format<br> - train and evaluate neural networks using the simulated data<br> - use the trained networks for automated element identification<br> is available on GitHub at arnoannys/EELS_ID</p> <p>A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at: Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. <em>Sci Rep</em> 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7</p> <p>[1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585<br> [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. __APL Mater__ 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)</p>
Dataset for publication: "Measuring Losses of an Air-Core Shunt Reactor with an Advanced Loss Measuring System"
<p>Dataset for publication: "Measuring Losses of an Air-Core Shunt Reactor with an Advanced Loss Measuring System". Excel file contains the data for Figures 7, 8, 9 and 10.</p>
Data from: Plasticity of promoter-core sequences allows bacteria to compensate for the loss of a key global regulatory gene
Transcription regulatory networks (TRNs) are of central importance for both short-term phenotypic adaptation in response to environmental fluctuations and long-term evolutionary adaptation, with global regulatory genes often being targets of natural selection in laboratory experiments. Here, we combined evolution experiments, whole-genome resequencing, and molecular genetics to investigate the driving forces, genetic constraints, and molecular mechanisms that dictate how bacteria can cope with a drastic perturbation of their TRNs. The crp gene, encoding a major global regulator in Escherichia coli, was deleted in four different genetic backgrounds, all derived from the Long-Term Evolution Experiment (LTEE) but with different TRN architectures. We confirmed that crp deletion had a more deleterious effect on growth rate in the LTEE-adapted genotypes; and we showed that the ptsG gene, which encodes the major glucose-PTS transporter, gained CRP dependence over time in the LTEE. We then further evolved the four crp-deleted genotypes in glucose minimal medium, and we found that they all quickly recovered from their growth defects by increasing glucose uptake. We showed that this recovery was specific to the selective environment and consistently relied on mutations in the cis regulatory region of ptsG, regardless of the initial genotype. These mutations affected the interplay of transcription factors acting at the promoters, changed the intrinsic properties of the existing promoters, or produced new transcription initiation sites. Therefore, the plasticity of even a single promoter region can compensate by three different mechanisms for the loss of a key regulatory hub in the E. coli TRN.
Data from: Plasticity of promoter-core sequences allows bacteria to compensate for the loss of a key global regulatory gene
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Loss of PRC2 enforces a mesenchymal neural crest stem cell phenotype in NF1-deficient cancer through activation of core transcription factors [RNA-seq]
GEO Series GSE183307. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.
Loss of PRC2 enforces a mesenchymal neural crest stem cell phenotype in NF1-deficient cancer through activation of core transcription factors
GEO Series GSE183309. Homo sapiens. 95 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Loss of PRC2 enforces a mesenchymal neural crest stem cell phenotype in NF1-deficient cancer through activation of core transcription factors [single-cell RNA-seq]
GEO Series GSE183308. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
Loss of PRC2 enforces a mesenchymal neural crest stem cell phenotype in NF1-deficient cancer through activation of core transcription factors [CUT&RUN]
GEO Series GSE202671. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Loss of PRC2 enforces a mesenchymal neural crest stem cell phenotype in NF1-deficient cancer through activation of core transcription factors [ChIP-seq]
GEO Series GSE183306. Homo sapiens. 34 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Loss of PRC2 enforces a mesenchymal neural crest stem cell phenotype in NF1-deficient cancer through activation of core transcription factors [ATAC-seq]
GEO Series GSE183305. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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