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514 results for “principles”
Data for: Susceptibility of domain experts to color manipula-tion indicate a need for design principles in data visualization (PLoS one)
<p>This data set accompanies the paper "<strong>Susceptibility of domain experts to color manipulation indicate a need for design principles in data visualization</strong>" by Markus Christen, Peter Brugger<span> </span>and Sara Irina Fabrikant, revision submitted to PLoS One. The paper will be open access, further information will be available there.</p>
How dopants limit the ultrahigh thermal conductivity of boron arsenide: a first principles study
<p>The dataset contains the necessary information to reproduce the phonon-defect scattering rates and the phonon thermal conductivity of cubic boron arsenide (BAs) upon doping, via the almaBTE software.</p> <p>Input files contain:</p> <p>1) Interatomic force constants for the pristine BAs, required to extract the phonon band structure and the intrinsic scattering processes;</p> <p>2) Unit cell POSCAR;</p> <p>3) Interatomic force constants for the C, Ge and Si impurities, required to compute the phonon-defect scattering rates beyond the mass-only approximation.</p> <p>Output files contain:</p> <p>1) Phonon-defect scattering rates (mass-only approximation, bond-only approximation, total) for charged and neutral impurities;</p> <p>2) Thermal conductivity at 300 K as function of the impurity concentration.</p> <p> </p>
Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases
Glycosyltransferases (GTs) are prevalent across the tree of life and regulate nearly all aspects of cellular functions. The evolutionary basis for their complex and diverse modes of catalytic functions remain enigmatic. Here, based on deep mining of over half million GT-A fold sequences, we define a minimal core component shared among functionally diverse enzymes. We find that variations in the common core and emergence of hypervariable loops extending from the core contributed to GT-A diversity. We provide a phylogenetic framework relating diverse GT-A fold families for the first time and show that inverting and retaining mechanisms emerged multiple times independently during evolution. Using evolutionary information encoded in primary sequences, we trained a machine learning classifier to predict donor specificity with nearly 90% accuracy and deployed it for the annotation of understudied GTs. Our studies provide an evolutionary framework for investigating complex relationships connecting GT-A fold sequence, structure, function and regulation.
Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures
<p><br /> This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z-direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.</p> <p>Files:</p> <p>figure_excel_files.zip:</p> <p>Excel files for figures in the publication, and excel files of main data tables for FCC and HCP vacancy formation energies and vacancy migration energies.</p> <p>fcc_hvf_hvm.tar.gz and hcp_hvf_hvm.tar.gz:</p> <p>Raw VASP files corresponding to FCC and HCP vacancy formation energies and vacancy migration energies.<br /> <br /> bulk_modulus.tar.gz:</p> <p>Raw VASP files corresponding to FCC bulk modulus calculations.<br /> </p>
Data from: Competing polar and antipolar phases in n=2 Ruddlesden-Popper niobates and tantalates from first principles
<p><span>This dataset contains the optimized structures from our density functional theory (DFT) calculations presented in our manuscript "Competing polar and antipolar phases in n=2 Ruddlesden-Popper niobates and tantalates from first principles" Input files for the calculations are also are provided.</span></p>
Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: A case study of the MIMIC-IV emergency department module
<p><strong>Objective</strong> <br>To assess the use of Health Level Seven Fast Healthcare Interoperability Resources (FHIR<sup>®</sup>) for implementing the Findable, Accessible, Interoperable, and Reusable guiding principles for scientific data (FAIR). Additionally, present a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR. <br><br><strong>Material and Methods</strong> <br>A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department dataset (MIMIC-ED), a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators. <br><br><strong>Results</strong> <br>The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified. <br><br><strong>Discussion</strong> <br>Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR. <br><br><strong>Conclusion</strong> <br>To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures. Keywords: FAIR Guiding Principles, HL7 FHIR, Reusable Data, MIMIC-IV</p>
First-principles simulations of exciton transfer between N-heterocyclic carbene iridium (III) complexes in blue organic light-emitting diodes
<p>N-heterocyclic carbene (NHC) iridium (III) complexes are promising for the use as blue emitters in organic light-emitting diodes. Exciton transfer between such organometallic complexes is investigated using time-dependent density functional theory calculations. Casida's equation is solved to study absorption and emission of the neutral and charged complexes using the ORCA package. The Sternheimer equation implemented in the Octopus code is extended to take into account spin-orbit coupling and is applied to investigate triplet excitations. Real-time propagation as implemented in the Octopus code is used to simulate exciton dynamics in an emitter dimer and to extract the exciton coupling via explicit integration of transition densities.</p>
Mechanistic principles of hydrogen evolution in the membrane-bound hydrogenase
<p>Optimized coordinates of DFT models of the [NiFe] active-site from the membrane-bound hydrogenase</p> <p>Table of contents<br>1. Ni-SIa state<br>2. Ni-L and Ni-C state<br>3. Ni-R state<br>4. Ni-SIa state (with His75+)<br>5. Ni-L and Ni-C state (with His75+)<br>6. Ni-R state (with His75+)<br>7. [NiFe] active-site from DvMF in the Ni-R state (Geometries optimized using various DFT functional)</p>
Evaluation of different Open Science projects based on Vienna principles.
<p>Evaluation of different Open Science projects based on Vienna principles.</p>
Principles of RNA recruitment to viral ribonucleoprotein condensates in a segmented dsRNA virus
<p><strong>Rotaviruses transcribe eleven distinct protein-coding RNAs that must be stoichiometrically co-packaged prior to their replication to make an infectious virion. During infection, </strong><strong>rotavirus transcripts accumulate in cytoplasmic ribonucleoprotein (RNP) condensates, termed viroplasms. </strong><strong>Understanding the mechanisms of viroplasm assembly and RNA enrichment within is crucial to gaining greater insight into their function and stoichiometric assortment of individual transcripts.</strong> <strong>We analysed the subcellular distribution of individual RV transcripts and viroplasm transcriptome by combining multiplexed DNA-barcoded single-molecule RNA FISH of infected cells. Using DNA-PAINT microscopy, we provide evidence of the early onset of viral transcript oligomerisation that occurs prior to the formation of viroplasms. We demonstrate that viral sequences lacking the conserved terminal regions fail to undergo enrichment in rotavirus RNP condensates. We show that individual viral transcripts exhibit variable propensities to partition into viroplasms, irrespective of their absolute numbers in cells, suggesting a selective RNA enrichment mechanism distinct from other known cellular RNP granules. </strong><strong>We suggest that rotavirus replication factories represent unique RNP condensates enriched in eleven types of cognate transcripts that may facilitate the assembly of a multi-segmented RNA genome.</strong></p>
Connectomes across development reveal principles of brain maturation
<p>These data sets belong to the following publication:</p> <p>Witvliet, D., Mulcahy, B., Mitchell, J.K. <em>et al.</em> Connectomes across development reveal principles of brain maturation. <em>Nature</em> <strong>596, </strong>257–261 (2021). https://doi.org/10.1038/s41586-021-03778-8</p> <p>Please read the README.md file before using these data sets.</p>
Research Space and DMPTool - Streamlining the research lifecycle and enhancing FAIR principles with a series of interoperable tools
<p>A video demonstration of the integration between the Electronic Lab Notebook, RSpace, and the DMPTool. Presented at the FORCE11 annual conference on December 7, 2021.</p>
Data publication for "First-principles derivation and properties of density-functional average-atom models"
<p>Data for the pre-print "First-principles derivation and properties of density-functional average-atom models", https://arxiv.org/abs/2103.09928.</p> <p>Each data folder is named according to the corresponding figure in the paper. For any questions, please contact the authors.</p>
Dataset for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study"
<p># Data and code for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study," M. Kuisma, B. Rousseaux, K.M. Czajkowski, T.P. Rossi, T. Shegai, P. Erhart, T.J. Antosiewicz, ACS Photonics, doi:10.1021/acsphotonics.2c00066 (2022).</p> <p><br> ## Contents</p> <p>* *data-{type}/*: reproducible data<br> * *src/*: input scripts</p> <p><br> ## Description of the data</p> <p>The data are stored in directories *data-{type}/*. The contents of the directories<br> can be reproduced with the included input scripts.</p> <p>The data are organized in subdirectories *data-{type}/{system}/* corresponding to<br> the considered nanoparticle-molecule systems and simulation type:</p> <p>* data-fd: free energy calculations done with the finite difference mode<br> * data-lcao: strong coupling calculations done with LCAO mode<br> * data-d3: DFT-D3 calculations</p> <p>The contents of each subdirectory are:</p> <p>* *data-{fd,lcao,d3}/{system}/structure.xyz*: physical atomic structure<br> * *data-lcao/{system}/td-x/dm.dat*: delta-kick-induced time-dependent dipole moment<br> * *data-lcao/{system}/td-x/dm_abs_Lorentz_0.100.dat*: photoabsorption spectrum</p> <p>The spectrum plots in the article correspond to the first (x values) and<br> second (y values) columns of the spectrum files.</p> <p>## Reproduction of the data</p> <p>The data was produced using the Python scripts in *src/*,<br> Python version 3.7.3, GPAW version 20.1.0, libxc version 4.3.4,<br> ASE version 3.20.0, NumPy version 1.16.2, and SciPy version 1.2.1.</p> <p>The calculation of the data of a system consists of<br> the following steps (in *bash* shell with, e.g., system=rlx-ico-Al147`):</p> <p>1. Ground-state calculation:<br> * Copy the gs folder to a data-lcao/{system} folder<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Select the Poisson Solver in the settings.py file by commenting out / uncommenting:<br> * for single particles or molecules use poissonsolver = PoissonSolver(eps=eps, remove_moment=9)<br> * otherwise comment out the above line and uncomment the last 8 lines<br> * Submit the gs.py calculation as appropriate for the particular system<br> 2. Time-propagation calculation:<br> * Requires finished ground-state calculation<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Submit the td.py calculation as appropriate for the particular system<br> 3. Spectrum calculation:<br> * Requires finished time-propagation calculation (30 fs propagation)<br> * Run the `$ python spec.py` script</p> <p>Note that the example python scripts use variables STARTTIME and WALLTIME to define<br> allocated compuing time in HPC environments. The `WALLTIME` and `STARTTIME` environment<br> variables defined in *submit.sbatch* are required for a clean exit of the calculation<br> within the allocated time.</p> <p>If the ground-state or time-propagation calculations do not finish within the<br> allocated time, the same *gsc.py* or *tdc.py* scripts can be (re)run to continue<br> the calculation.</p>
Applying Cognitive Principles to Model-Finding Output: The Positive Value of Negative Information (artifact)
<p>This is the artifact associated with the OOPSLA 2022 paper titled "Applying Cognitive Principles to Model-Finding Output: The Positive Value of Negative Information".</p> <p>It includes:</p> <ul> <li>A README file with in-depth instructions for accessing and using all artifact components.</li> <li>The raw and anonymized data for each participant in the quantitative studies.</li> <li>The raw and anonymized audio transcripts for each participant in the qualitative studies.</li> <li>Working versions of every user interface used in the experiments.</li> <li>An experimental model-finder, which is a modified version of the <a href="http://alloytools.org/">Alloy</a> model finder, which demonstrates the "2+1-" visualization mode from the paper.</li> </ul>
Data from: Compression principle and Zipf's law of brevity in infochemical communication
<p>Compression has been presented as a general principle of animal communication. Zipf's law of brevity is a manifestation of this postulate and can be generalised as the tendency of more frequent communicative elements to be shorter. Previous works supported this claim, showing evidence of Zipf's law of brevity in animal acoustical communication and human language. However, a significant part of the communicative effort in biological systems is carried out in other transmission channels, such as those based on infochemicals. To fill this gap, we seek, for the first time, shreds of evidence of this principle in infochemical communication by analysing the statistical tendency of more frequent infochemicals to be chemically shorter and lighter. We analyse data from the largest and most comprehensive open-access infochemical database known as Pherobase, recovering Zipf's law of brevity in interspecific communication (allelochemicals) but not in intraspecific communication (pheromones). Moreover, these results are robust even when addressing different magnitudes of study or mathematical approaches. Therefore, different dynamics from the compression principle would dominate intraspecific chemical communication, defying the universality of Zipf's law of brevity. To conclude, we discuss the exception found for pheromones in the light of other potential communicative paradigms such as pressures on successful communication or the Handicap principle.</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data
<p>This dataset contains the raw image data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data - calibration images
<p>This dataset contains the images used for channel calibration for the single molecule data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Design principles a heterogeneously catalyzed autothermal reactor with enhanced heat and mass transfer for hydrogen production
<p><strong>Design principles a</strong> <strong>heterogeneously catalyzed autothermal reactor with enhanced heat and mass transfer for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Catalysis, in chemistry, is the modification of the rate of a chemical reaction, usually an acceleration, by addition of a substance not consumed during the reaction. The rates of chemical reactions, that is, the velocities at which they occur, depend upon a number of factors, including the chemical nature of the reacting species and the external conditions to which they are exposed. A particular phenomenon associated with the rates of chemical reactions that is of great theoretical and practical interest is catalysis, the acceleration of chemical reactions by substances not consumed in the reactions themselves, substances known as catalysts. The study of catalysis is of interest theoretically because of what it reveals about the fundamental nature of chemical reactions; in practice, the study of catalysis is important because many industrial processes depend upon catalysts for their success. In a catalyzed reaction, the catalyst generally enters into chemical combination with the reactants but is ultimately regenerated, so the amount of catalyst remains unchanged. Since the catalyst is not consumed, each catalyst molecule may induce the transformation of many molecules of reactants. For an active catalyst, the number of molecules transformed per minute by one molecule of catalyst may be as large as several million. Where a given substance or a combination of substances undergoes two or more simultaneous reactions that yield different products, the distribution of products may be influenced by the use of a catalyst that selectively accelerates one reaction relative to the other(s). By choosing the appropriate catalyst, a particular reaction can be made to occur to the extent of practically excluding another. Many important applications of catalysis are based on selectivity of this kind.</p> <p>Streamwise distance (millimeters), Reforming channel centerline temperature (degrees kelvin)</p> <p>0 373</p> <p>0.00025 373.048742</p> <p>0.0005 373.2382942</p> <p>0.00075 373.7354626</p> <p>0.001 374.7081362</p> <p>0.00125 376.2548823</p> <p>0.0015 378.3951976</p> <p>0.00175 381.0814232</p> <p>0.002 384.2247405</p> <p>0.00225 387.7222496</p> <p>0.0025 391.465635</p> <p>0.00275 395.3617454</p> <p>0.003 399.3347598</p> <p>0.00325 403.3218552</p> <p>0.0035 407.2775391</p> <p>0.00375 411.1628179</p> <p>0.004 414.9538621</p> <p>0.00425 418.6311751</p> <p>0.0045 422.1828419</p> <p>0.00475 425.6012807</p> <p>0.005 428.8821587</p> <p>0.00525 432.025476</p> <p>0.0055 435.0312325</p> <p>0.00575 437.9015946</p> <p>0.006 440.6398117</p> <p>0.00625 443.2502165</p> <p>0.0065 445.7371415</p> <p>0.00675 448.1049195</p> <p>0.007 450.3600492</p> <p>0.00725 452.5057803</p> <p>0.0075 454.5475284</p> <p>0.00775 456.4896262</p> <p>0.008 458.3374895</p> <p>0.00825 460.0954509</p> <p>0.0085 461.7689261</p> <p>0.00875 463.3600816</p> <p>0.009 464.874333</p> <p>0.00925 466.3149298</p> <p>0.0095 467.6862047</p> <p>0.00975 468.9924902</p> <p>0.01 470.2348696</p> <p>0.01025 471.4176754</p> <p>0.0105 472.5441571</p> <p>0.01075 473.6175642</p> <p>0.011 474.6389798</p> <p>0.01125 475.6138198</p> <p>0.0115 476.5431672</p> <p>0.01175 477.4302716</p> <p>0.012 478.276216</p> <p>0.01225 479.0831669</p> <p>0.0125 479.8543736</p> <p>0.01275 480.5909193</p> <p>0.013 481.2938872</p> <p>0.01325 481.9665268</p> <p>0.0135 482.6099212</p> <p>0.01375 483.2251535</p> <p>0.014 483.8143901</p> <p>0.01425 484.377631</p> <p>0.0145 484.9170424</p> <p>0.01475 485.4347907</p> <p>0.015 485.930876</p> <p>0.01525 486.4063812</p> <p>0.0155 486.8623897</p> <p>0.01575 487.3010677</p> <p>0.016 487.721332</p> <p>0.01625 488.1264322</p> <p>0.0165 488.515285</p> <p>0.01675 488.8889736</p> <p>0.017 489.2496644</p> <p>0.01725 489.5962742</p> <p>0.0175 489.9309692</p> <p>0.01775 490.2526664</p> <p>0.018 490.5646152</p> <p>0.01825 490.8646493</p> <p>0.0185 491.1560181</p> <p>0.01875 491.4365554</p> <p>0.019 491.7073443</p> <p>0.01925 491.970551</p> <p>0.0195 492.2250926</p> <p>0.01975 492.472052</p> <p>0.02 492.7114294</p> <p>0.02025 492.9432247</p> <p>0.0205 493.1696042</p> <p>0.02075 493.3884016</p> <p>0.021 493.6017832</p> <p>0.02125 493.8086659</p> <p>0.0215 494.0101329</p> <p>0.02175 494.2072672</p> <p>0.022 494.3989857</p> <p>0.02225 494.5863716</p> <p>0.0225 494.7683417</p> <p>0.02275 494.9470624</p> <p>0.023 495.1214504</p> <p>0.02325 495.2915058</p> <p>0.0235 495.4583118</p> <p>0.02375 495.6218682</p> <p>0.024 495.7810921</p> <p>0.02425 495.9381497</p> <p>0.0245 496.0919577</p> <p>0.02475 496.2425163</p> <p>0.025 496.3898255</p> <p>0.02525 496.5360515</p> <p>0.0255 496.679028</p> <p>0.02575 496.8187551</p> <p>0.026 496.9563158</p> <p>0.02625 497.0927934</p> <p>0.0265 497.2260215</p> <p>0.02675 497.3570834</p> <p>0.027 497.4859789</p> <p>0.02725 497.6137912</p> <p>0.0275 497.7383541</p> <p>0.02775 497.8618338</p> <p>0.028 497.9831472</p> <p>0.02825 498.1033775</p> <p>0.0285 498.2203583</p> <p>0.02875 498.3362559</p> <p>0.029 498.4499873</p> <p>0.02925 498.5637186</p> <p>0.0295 498.6666184</p> <p>0.02975 498.796597</p> <p>0.03 498.88</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Design principles an autothermal chemical reactor with enhanced momentum transport for hydrogen production
<p><strong>Design principles an autothermal chemical reactor with enhanced momentum transport for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Chemical engineering is the development of processes and the design and operation of plants in which materials undergo changes in their physical or chemical state. Applied throughout the process industries, it is founded on the principles of chemistry, physics, and mathematics. The laws of physical chemistry and physics govern the practicability and efficiency of chemical engineering operations. Energy changes, deriving from thermodynamic considerations, are particularly important. Mathematics is a basic tool in optimization and modeling. Optimization means arranging materials, facilities, and energy to yield as productive and economical an operation as possible. Modeling is the construction of theoretical mathematical prototypes of complex process systems, commonly with the aid of computers. Study of the fundamental phenomena upon which chemical engineering is based has necessitated their description in mathematical form and has led to more sophisticated mathematical techniques. The advent of digital computers has allowed laborious design calculations to be performed rapidly, opening the way to accurate optimization of industrial processes. Variations due to different parameters, such as energy source used, plant layout, and environmental factors, can be predicted accurately and quickly so that the best combination can be chosen.</p> <p>Streamwise distance (millimeters), Reforming channel centerline temperature (degrees kelvin)</p> <p>0 373.004</p> <p>0.00025 373.049</p> <p>0.0005 373.224</p> <p>0.00075 373.683</p> <p>0.001 374.581</p> <p>0.00125 376.009</p> <p>0.0015 377.985</p> <p>0.00175 380.465</p> <p>0.002 383.367</p> <p>0.00225 386.596</p> <p>0.0025 390.052</p> <p>0.00275 393.649</p> <p>0.003 397.317</p> <p>0.00325 400.998</p> <p>0.0035 404.65</p> <p>0.00375 408.237</p> <p>0.004 411.737</p> <p>0.00425 415.132</p> <p>0.0045 418.411</p> <p>0.00475 421.567</p> <p>0.005 424.596</p> <p>0.00525 427.498</p> <p>0.0055 430.273</p> <p>0.00575 432.923</p> <p>0.006 435.451</p> <p>0.00625 437.861</p> <p>0.0065 440.157</p> <p>0.00675 442.343</p> <p>0.007 444.425</p> <p>0.00725 446.406</p> <p>0.0075 448.291</p> <p>0.00775 450.084</p> <p>0.008 451.79</p> <p>0.00825 453.413</p> <p>0.0085 454.958</p> <p>0.00875 456.427</p> <p>0.009 457.825</p> <p>0.00925 459.155</p> <p>0.0095 460.421</p> <p>0.00975 461.627</p> <p>0.01 462.774</p> <p>0.01025 463.866</p> <p>0.0105 464.906</p> <p>0.01075 465.897</p> <p>0.011 466.84</p> <p>0.01125 467.74</p> <p>0.0115 468.598</p> <p>0.01175 469.417</p> <p>0.012 470.198</p> <p>0.01225 470.943</p> <p>0.0125 471.655</p> <p>0.01275 472.335</p> <p>0.013 472.984</p> <p>0.01325 473.605</p> <p>0.0135 474.199</p> <p>0.01375 474.767</p> <p>0.014 475.311</p> <p>0.01425 475.831</p> <p>0.0145 476.329</p> <p>0.01475 476.807</p> <p>0.015 477.265</p> <p>0.01525 477.704</p> <p>0.0155 478.125</p> <p>0.01575 478.53</p> <p>0.016 478.918</p> <p>0.01625 479.292</p> <p>0.0165 479.651</p> <p>0.01675 479.996</p> <p>0.017 480.329</p> <p>0.01725 480.649</p> <p>0.0175 480.958</p> <p>0.01775 481.255</p> <p>0.018 481.543</p> <p>0.01825 481.82</p> <p>0.0185 482.089</p> <p>0.01875 482.348</p> <p>0.019 482.598</p> <p>0.01925 482.841</p> <p>0.0195 483.076</p> <p>0.01975 483.304</p> <p>0.02 483.525</p> <p>0.02025 483.739</p> <p>0.0205 483.948</p> <p>0.02075 484.15</p> <p>0.021 484.347</p> <p>0.02125 484.538</p> <p>0.0215 484.724</p> <p>0.02175 484.906</p> <p>0.022 485.083</p> <p>0.02225 485.256</p> <p>0.0225 485.424</p> <p>0.02275 485.589</p> <p>0.023 485.75</p> <p>0.02325 485.907</p> <p>0.0235 486.061</p> <p>0.02375 486.212</p> <p>0.024 486.359</p> <p>0.02425 486.504</p> <p>0.0245 486.646</p> <p>0.02475 486.785</p> <p>0.025 486.921</p> <p>0.02525 487.056</p> <p>0.0255 487.188</p> <p>0.02575 487.317</p> <p>0.026 487.444</p> <p>0.02625 487.57</p> <p>0.0265 487.693</p> <p>0.02675 487.814</p> <p>0.027 487.933</p> <p>0.02725 488.051</p> <p>0.0275 488.166</p> <p>0.02775 488.28</p> <p>0.028 488.392</p> <p>0.02825 488.503</p> <p>0.0285 488.611</p> <p>0.02875 488.718</p> <p>0.029 488.823</p> <p>0.02925 488.928</p> <p>0.0295 489.023</p> <p>0.02975 489.143</p> <p>0.03 489.22</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
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