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720 results for “TC”
PsPM-TC: SCR, ECG, EMG and respiration measurements in a discriminant trace fear conditioning task with visual CS and electrical US.
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses and keypress response times from 18 healthy unmedicated participants (8 males and 10 females aged 23.89+/-2.52 years) participating in a classical (Pavlovian) discriminant trace fear conditioning task. CS were a red and a blue rectangle presented for 3 seconds. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 10 Hz frequency. SOA between the CS onset and US was 4 s. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
Data for TC Diagnostics
<p>The dataset includes intensity bin composites of column-integrated mosit static energy (MSE) spatial variance budget feedback terms for GCMs, reanalyses, and <em>CloudSat</em> for:</p> <p>Starr, J. C., A. A. Wing, S. J. Camargo, D. Kim, T. Y. Lee, and J. Moon: Using the moist static energy variance budget to evaluate tropical cyclones in climate models against reanalyses and satellite observations. <em>Journal of Climate</em>, <em>In Review.</em></p> <p><strong>Description of Files for GCMs and Reanalyses </strong></p> <p>For each of the GCMs and reanalyses used in this study, there are 4 netcdf files that are saved, 2 for intensity bin composites with maximum wind speed (Vmax) as the binning metric and 2 for minimum mean sea level pressure (MSLP). Considering each of the GCMs and reanalyses have the same file format, the AM4 model will be used as an example for what each file contains and how they are organized. Each reanalysis and GCM will have its own .tar containing the four netcdf files mentioned. </p> <div> <div>AM4_Binned_Composites_V2.nc is the Vmax-binned intensity bin composite means of all the variables. The first dimension of each of these variables within the file are "bin" which represents the bin mean value, for example the first bin value is 1.5 representing the 0-3 m/s bin, then increasing by 3 m/s from there. For the spatial composites, which are 2-dimensional variables, those have dimensions of "lat" and "lon" which range from -5 degrees to 5 degrees as the center of each spatial intensity bin composite of that variable would be 0 degrees, 0 degrees. The azimuthal mean variables have dimension "nr" which represents the radial increments. </div> <div> <div>AM4_Binned_STDEVS_of_BoxAvgs_V2.nc contains the Vmax-binned intensity bin composite standard deviations of the box averaged variables as well as the azimuthal mean feedback variables. This file is used in calculting the 5 to 95% confidence intervals for the azimuthal mean and box average plots. </div> <div> </div> <div>AM4_Binned_Composites_MSLP.nc is the minimum MSLP-binned intensity bin composite means of all the variables. This file is set up the same as the Vmax-binned file, but now the first dimension "bin" represents the bin mean value using minimum MSLP as the intensity metric. For example, the first bin of this dimension is 882.5 hPa which is the mean value of the 880-885 hPa bin. These mean values then increase by 5 hPa to the weakest bin of 1020-1025 hPa. <div> <div>AM4_Binned_STDEVS_of_BoxAvgs_MSLP.nc is set up identically to the Vmax-binned version of the standard deviation file, just now with minimum MSLP as the binning metric.</div> <div> </div> <div>These files contain all the variables that are pertinent to the MSE spatial variance budget, but also some that were not utilized in this study. The variables listed below are those that were utilized in this study.</div> <div> </div> <div>"bincounts": the number of snapshots in each intensity bin</div> <div><strong>3-D Variables (Spatial composites (bin,lat,lon)):</strong></div> <div>"hanom": anomaly of column-integrated MSE from the domain-mean column-integrated MSE</div> <div>"hanom_SEFanom": the surface enthalpy flux (SEF) feedback </div> <div>"hanom_LWanom": the longwave (LW) feedback</div> <div>"hanom_SWanom": the shortwave (SW) feedback</div> <div><strong>2-D Variables (Azimuthal mean composites (bin,nr)):</strong></div> </div> "Azmean_hSEF": Azimuthal mean SEF feedback</div> <div>"Azmean_hLW": Azimuthal mean LW feedback</div> <div>"Azmean_hSW": Azimuthal mean SW feedback</div> <div><strong>1-D Variables (Box-averaged composites (bin)):</strong></div> <div> <div> <div>"new_boxav_hvar": the box-averaged variance of column-integrated MSE</div> <div>"new_boxav_hanom_SEFanom": the box-averaged SEF feedback</div> <div>"new_boxav_hanom_LWanom": the box-averaged LW feedback</div> <div>"new_boxav_hanom_SWanom": the box-averaged SW feedback</div> <div>"new_boxav_norm_hanom_SEFanom": the normalized box-averaged SEF feedback</div> <div>"new_boxav_norm_hanom_LWanom": the normalized box-averaged LW feedback</div> <div>"new_boxav_norm_hanom_SWanom": the normalized box-averaged SW feedback</div> <div> </div> <div>To get the standard deviations of the azimuthal mean and box-averaged feedbacks of each intensity bin, the same variable names are used above in the standard deviation file.</div> </div> </div> <div><strong>Description of File for <em>CloudSat</em></strong></div> <div>This file was provided by work done in:</div> <div> </div> <div>Lee, T.-Y., and A. Wing, 2024: Satellite-based estimation on the role of cloud-radiative interaction in accelerating tropical cyclone development. <em>Journal of the Atmospheric Sciences</em>, <strong>64 (81)</strong>, 959-982, https://doi.org/https://doi.org/10.1175/JAS-D-23-0142.1.</div> <div> </div> <div> <div>CloudSat_Composite_IR_RRTMGclimlab_vi4_IR_Vmax999_000_R3.nc contains the Vmax-binned intensity bin composites of the MSE variance budget feedback variables. Each of the <em>CloudSat</em> variables are provided as radial profiles with dimensions like those in the reanalyses and GCMs of intensity bin and then radius. The variables from this file that were utilized in this study are listed below.</div> <div> </div> <div>"RadFB_LW_ALL_500": radial composite of the LW feedback</div> <div> <div> <div>"RadFB_SWDAY_ALL_500": radial composite of the SW feedback</div> <div>"RadFB_Net_ALL_500": radial composite of the total radiaitive feedback</div> <div>"RadFB_LW_CLEARSKY_500": radial composite of the clear-sky LW feedback</div> <div> <div> <div>"RadFB_SWDAY_CLEARSKY_500": radial composite of the clear-sky SW feedback</div> <div>"RadFB_Net_CLEARSKY_500": radial composite of the clear-sky total radiaitive feedback</div> </div> </div> </div> </div> </div> </div> </div>
CROSSBOW TC_01.05.01 final demonstration results
<p>This data set represents final demonstration results for the Test Case 5.1 from the High Level Use case 1 of the CROSSBOW project. It contains lists of Critical Network Element & Contingency (CNEC) pairs for 17 SEE borders (AL-GR, AL-RS, BA-HR, BA-ME, BA-RS, BG-GR, BG-MK, BG-RO, BG-RS, BG-TR, GR-ALMKBGTR, ME-AL, ME-RS, RS-BAHR, RS-BGRO, RS-HU, RS-MEMKAL) and 4 seasons (spring, summer, autumn and winter), which are used as input data for Net Transmission Capacity (NTC) calculation. For each CNEC pair in the list, Outage Transmission Distribution Factor (OTDF) is presented. Selection of CNEC pairs is determined based on predefined criteria (OTDF>20%) , using algorithm presented in CROSSBOW deliverable: D4.2 CROSSBOW Regional Operation Centre Balancing Cockpit (ROC-BC).</p>
Lateral_melting_TC_2022: Data for sea ice sensitivity to lateral melting, CESM2
<p>CESM2 model data for Smith, M. et al, Arctic sea ice sensitivity to lateral melting representation in a coupled climate model, In The Cryosphere, 2022</p>
Integration of High-Tc Superconductors with High-Q-Factor Oxide Mechanical Resonators (Dataset)
<p>Micro-mechanical resonators are building blocks of a variety of applications in basic science and consumer electronics. This device technology is mainly based on well-established and reproducible silicon-based fabrication processes with outstanding performances in term of mechanical <em>Q</em>-factor and sensitivity to external perturbations. Broadening the functionalities of micro-electro-mechanical systems (MEMS) by the integration of functional materials is a key step for both applied and fundamental science. However, combining functional materials with silicon-based devices is challenging. An alternative approach is directly fabricating MEMS based on compounds inherently showing non-trivial functional properties, such as transition metal oxides. Here, a full-oxide approach is reported, where a high-Tc superconductor YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7</sub> (YBCO) is integrated with high <em>Q</em>-factor micro-bridge resonators made of single-crystal LaAlO<sub>3</sub> (LAO) thin films. LAO resonators are tensile strained, with a stress of about 350 MPa, show a <em>Q</em>-factor above 200k, and have low roughness. YBCO overlayers are grown ex situ by pulsed laser deposition and YBCO/LAO bridges show zero resistance below 78 K and mechanical properties similar to those of bare LAO resonators. These results open new possibilities toward the development of advanced transducers, such as bolometers or magnetic field detectors, as well as experiments in solid state physics, material science, and quantum opto-mechanics.</p>
CROSSBOW TC_01.01.01 final demonstration results
<p>This data set represents final demonstration results for the Test Case 1.1 from the High Level Use case 1 of the CROSSBOW project. It contains information about values: ERIC [MW], TIC [MW], TIF [MW], Loop flow [%], Optimal adequacy transaction [MW] and Net adequacy exchange [MW] for 9 areas (AL, BA, BG, GR, ME; MK, HR, RO and RS) and 366 timestamps (of Week 9 and Week 10 of 2021). </p> <p>All information about RAA methodology and terms used in this data set could be found in document <em>D4.2 CROSSBOW Regional Operation Centre Balancing Cockpit (ROC-BC).</em></p>
WRF 3.5km North Atlantic August - October 2020 (TC tracks and atmospheric composites data)
<p>This zip file contains simulated TC tracks and some atmospheric composites for August to October 2020.</p> <p>- TC tracks information: timing, intensity, and location</p> <p>- Composites: vertical wind shear (200 - 850 hPa), potential intensity, 850 hPa absolute vorticity, and 700 hPa specific humidity.</p>
Dataset: TuanChe Limited (TC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: TC Biopharm (Holdings) Plc (TCBPW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: TC Biopharm (Holdings) Plc (TCBP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: TC Bancshares, Inc. (TCBC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones
<p>Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity. </p> <p><strong>Included data:</strong></p> <ul> <li><strong>TC wind speeds:</strong> Peak 10-min mean hub height (90m) TC wind speed maps</li> <li><strong>Damage states:</strong> Yielding and Buckling probability maps for OSW turbines</li> <li><strong>Geographic coverage:</strong> U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li><strong>Time periods:</strong> Historic (1980-2014) and Future (2066-2100)</li> </ul> <p><strong>Methodology:</strong></p> <ul> <li><strong>Tropical cyclone simulation:</strong> The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li><strong>TC impact metric:</strong> Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li><strong>Fragility functions:</strong> Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li><strong>Damage probability maps:</strong> The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> <p><strong>Potential Uses:</strong></p> <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> <p>For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a></p> <p>This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.</p> <p> </p>
Text-fig. 6. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in endocranial view. a – shaded drawing; b – line drawing with labels. Abbreviations: app – apex partis petrosae, cc – cochlear canaliculus, crp – crista petrosa, cs – cerebral surface, iam – internal acoustic meatus, lji – lateral jugular incisure, me – mastoid exposure, mji – medial jugular incisure, saf – subarcuate fossa, sips – sulcus for inferior petrosal sinus, soev – sulcus for occipital emissary vein, sss – sulcus for sigmoid sinus, tc – transverse crest, to ptc – to posttemporal canal, va – vestibular aqueduct. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 6. Metacheiromys marshi, AMNH 131777, left petrosal isosurface from CT scans in endocranial view. a – shaded drawing; b – line drawing with labels. Abbreviations: app – apex partis petrosae, cc – cochlear canaliculus, crp – crista petrosa, cs – cerebral surface, iam – internal acoustic meatus, lji – lateral jugular incisure, me – mastoid exposure, mji – medial jugular incisure, saf – subarcuate fossa, sips – sulcus for inferior petrosal sinus, soev – sulcus for occipital emissary vein, sss – sulcus for sigmoid sinus, tc – transverse crest, to ptc – to posttemporal canal, va – vestibular aqueduct.
Text-fig. 3. Metacheiromys marshi, USNM-P 452349, coronal sections from CT scans. a – section 590 of 2020 through the anteriormost tympanic cavity showing air spaces in the entotympanic and squamosal; b – section 898 of 2020 at level of the fenestra vestibuli showing the mastoid sinus. Abbreviations: bo – basioccipital, bs – basisphenoid, cp – crista parotica, ec – ectotympanic, en – entotympanic, es – epitympanic sinus of squamosal, fv – fenestra vestibuli, hyf – hypophyseal fossa, m – malleus, ms – mastoid sinus, pr – promontorium, sq – squamosal, tc – tympanic cavity. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 3. Metacheiromys marshi, USNM-P 452349, coronal sections from CT scans. a – section 590 of 2020 through the anteriormost tympanic cavity showing air spaces in the entotympanic and squamosal; b – section 898 of 2020 at level of the fenestra vestibuli showing the mastoid sinus. Abbreviations: bo – basioccipital, bs – basisphenoid, cp – crista parotica, ec – ectotympanic, en – entotympanic, es – epitympanic sinus of squamosal, fv – fenestra vestibuli, hyf – hypophyseal fossa, m – malleus, ms – mastoid sinus, pr – promontorium, sq – squamosal, tc – tympanic cavity.
Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"
<p>Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"</p> <p>Contents of this material</p> <ul> <li>Supplemental Text S1 and Text S2.</li> <li>Figures S1, S2, S2, S4, S5.</li> <li>Tables S1, S2</li> </ul> <p>For any questions email JHF (john.h.fairweaher@gmail.com).</p>
LPS's over Bay of Bengal and TC's over West North Pacific (WNP) simulated in the CESM 1.2 WNP SST warming experiment
<p>This repository contains the dataset as explained below:</p> <p>1) This dataset contains low-pressure systems (LPS) tracks over the Bay of Bengal (BoB) and West North Pacific (WNP) tropical cyclones (TCs) simulated in CESM 1.2 WNP SST warming experiment conducted by Srujan and Sandeep (2023) submitted to Nature Geosciences. The LPS and TCs are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015).</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p>
LPS's over Bay of Bengal and TC's over West North Pacific (WNP) simulated in the CESM 1.2 WNP SST warming experiment
<p>This repository contains the dataset as explained below:</p> <p>1) This dataset contains low-pressure systems (LPS) tracks over the Bay of Bengal (BoB) and West North Pacific (WNP) tropical cyclones (TCs) simulated in CESM 1.2 WNP SST warming experiment conducted by Srujan and Sandeep (2023) submitted to Nature Geosciences. The LPS and TCs are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015). The file names start with "<strong>cyc_</strong>". This will be useful for reproducing Fig. 1 and Fig. A2</p> <p>2) The data helps reproduce the Heating rate due to the moist process (Fig. 2) starts with "<strong>DT_</strong>"</p> <p>3) The data helps reproduce the propagation of rossbywave (Fig. 3) starting with "<strong>regcof_</strong>"</p> <p>4) The data helps reproduce the input SST pattern (Fig. A1) starting with "<strong>sst_</strong>"</p> <p>5) The data helps reproduce the Hovmuller plot (Fig. A3) starting with "<strong>comp_</strong>"</p> <p>6) The data helps reproduce the lag1 mslp anomaly (Fig. A4) starting with "<strong>slpcomp_</strong>"</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p>
TC-RADAR v3j data files
<p>This repository contains netCDF data files from the Tropical Cyclone Radar Archive of Doppler Analyses with Re-centering (TC-RADAR) version v3j. For more information on the creation of the database, refer to Fischer et al. (2022; https://doi.org/10.1175/MWR-D-21-0223.1). This repository also contains a readme for additional information on the variables stored in the "swath" and "merged" data files. The bias-corrected reflectivity data file was created following the methods described in Wadler et al. (2023; <em>Monthly Weather Review</em>, accepted pending minor revision).</p>
TC-RADAR v3k data files
<p>This repository contains netCDF data files from the Tropical Cyclone Radar Archive of Doppler Analyses with Re-centering (TC-RADAR) version v3k. For more information on the creation of the database, refer to Fischer et al. (2022; <a href="https://doi.org/10.1175/MWR-D-21-0223.1">https://doi.org/10.1175/MWR-D-21-0223.1</a>). This repository also contains a readme for additional information on the variables stored in the "swath" and "merged" data files. The bias-corrected reflectivity data file ("<a href="https://zenodo.org/api/records/10014658/draft/files/tc_radar_v3k_corrected_ref.nc/content">tc_radar_v3k_corrected_ref.nc</a>") was created following the methods described in Wadler et al. (2023; <a href="https://doi.org/10.1175/MWR-D-23-0048.1">https://doi.org/10.1175/MWR-D-23-0048.1</a>) and Fischer et al. (2023; <i>Monthly Weather Review</i>, accepted pending minor revision). The storm-centered infrared brightness temperatures ("<a href="https://zenodo.org/api/records/10014658/draft/files/tc_radar_swath_mergIR_v3k.nc/content">tc_radar_swath_mergIR_v3k.nc</a>") were derived from NASA's MergIR data set (<a href="https://disc.gsfc.nasa.gov/datasets/GPM_MERGIR_1/summary">https://disc.gsfc.nasa.gov/datasets/GPM_MERGIR_1/summary</a>).</p>
Data from: TC-GEN: Data-driven tropical cyclone downscaling using machine learning-based high-resolution weather model
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