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149 results for “Insolation”
Insolation values for the climate of the last 10 million years (Tables)
<p>Tables published in Berger A. and M. F. Loutre (1991), Insolation values for the climate of the last 10 million years, Quaternary Science Reviews, (10) 297 - 317 doi:10.1016/0277-3791(91)90033-Q and which constitute what is known in the community as the "BER90" solution. </p> <p>Orginal abstract: New values for the astronomical parameters of the Earth's orbit and rotation (eccentricity, obhqulty and precession) are proposed for paleochmatle research related to the Late Miocene, the Pliocene and the Quaternary They have been obtained from a numerical solution of the Lagrangian system of the planetary point masses and from an analytical solution of the Polsson equations of the Earth-Moon system. The analytical expansion developed in this paper allows the direct determination of the main frequencies with their phase and amplitude. Numerical and analytical comparisons with the former astronomical solution BER78 are performed so that the accuracy and the interval of time over which the new solution is valid can be estimated. The corresponding insolation values have also been computed and compared to the former ones. This analysis leads to the conclusion that the new values are expected to be reliable over the last 5 Ma In the time domain and at least over the last 10 Ma in the frequency domain. <br> </p>
Modelling of excitation propagation on computer models of insoles colonised by fungal mycelium. Videos and potential difference recordings.
<p>We used an artistic image of the mycelium network projected onto a $364 \times 985$ nodes grid. <br> The original image $M=(m_{ij})_{1 \leq j \leq n_i, 1 \leq j \leq n_j}$, $m_{ij} \in \{ r_{ij}, g_{ij}, b_{ij} \}$, where $n_i=364$ and $n_j=985$, and $1 \leq r, g, b \leq 255$, was converted to a conductive matrix $C=(m_{ij})_{1 \leq i,j \leq n}$ derived from the image as follows: $m_{ij}=1$ if $r_{ij}>170$, $g_{ij}>170$ and $b_{ij}<200$; a dilution operation was applied to $C$. </p> <p>FitzHugh-Nagumo (FHN) equations is a qualitative approximation of the Hodgkin-Huxley model of electrical activity of living cells:<br> \begin{eqnarray}<br> \frac{\partial v}{\partial t} & = & c_1 u (u-a) (1-u) - c_2 u v + I + D_u \nabla^2 \\<br> \frac{\partial v}{\partial t} & = & b (u - v),<br> \end{eqnarray}<br> where $u$ is a value of a trans-membrane potential, $v$ a variable accountable for a total slow ionic current, or a recovery variable responsible for a slow negative feedback, $I$ {is} a value of an external stimulation current. The current through intra-cellular spaces is approximated by<br> $D_u \nabla^2$, where $D_u$ is a conductance. The term $D_u \nabla^2 u$ governs a passive spread of the current. The terms $c_2 u (u-a) (1-u)$ and $b (u - v)$ describe the ionic currents. The term $u (u-a) (1-u)$ has two stable fixed points $u=0$ and $u=1$ and one unstable point $u=a$, where $a$ is a threshold of an excitation.</p> <p>We integrated the system using the Euler method with the five-node Laplace operator, a time step $\Delta t=0.015$ and a grid point spacing $\Delta x = 2$, while other parameters were $D_u=1$, $a=0.13$, $b=0.013$, $c_1=0.26$. We controlled excitability of the medium by varying $c_2$ from 0.05 (fully excitable) to 0.015 (non excitable). Boundaries are considered to be impermeable: $\partial u/\partial \mathbf{n}=0$, where $\mathbf{n}$ is a vector normal to the boundary. </p> <p>To record dynamics of excitation in the network, as if in laboratory experiments, we simulated electrodes by calculating a potential $p^t_x$ at an electrode location $x$ as $p_x = \sum_{y: |x-y|<2} (u_x - v_x)$. Configuration of electrodes $1, \cdots, 16$ is shown in Fig.~\ref{fig:mycelium}c. Time-lapse snapshots provided in the paper were recorded at every 100\textsuperscript{th} time step, and we display sites with $u >0.04$; videos and figures were produced by saving a frame of the simulation every 100\textsuperscript{th} step of the numerical integration and assembling the saved frames into the video with a play rate of 30 fps. </p> <p>Insole_01: Excitation started at electrode E2</p> <p>Insole_10: Excitation started at electrode E1</p> <p>Insole_11: Excitation started at electrodes E1 and E2</p> <p> </p>
Long-term variations of daily insolation and Quaternary climatic changes
<p>Supplementary material to:</p> <p>Berger A. L. (1978), Long-term variations of daily insolation and Quaternary climatic changes, Journal of the Atmospheric Sciences, (35) 2362-2367 doi:10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2</p> <p>Tables and FORTRAN computer code for the computation of daily mean insolation, according to the solution BER78.</p> <p>This solution is commonly referred to as BER78.</p> <p>Material uploaded to Zenodo by Michel Crucifix on 2022-10-14.</p>
PID4TC (Pressure Insoles Data for Task Classification)
<p>The dataset contains pressure insole data from twenty subjects who performed five tasks, comprising of two common daily activities (standing and walking), and three industry-focussed tasks (manual handling, assembly and pick and place). The speed and order in which a given task was completed was not prescribed. The data pertains to the areas of human factors, ergonomics and occupational health and safety research, among others, and enables an understanding of the force distributions involved in common tasks as well as physical and manufacturing type tasks.</p> <p>This work is licensed under the Creative Commons Attribution 4.0 International License. If you are using our database to evaluate your methods, please cite:</p> <p>O’Sullivan, P., Menolotto, M., Visentin, A., O’Flynn, B. and Komaris, D.S., 2024. AI-based Task Classification with Pressure Insoles for Occupational Safety. IEEE Access.</p>
Data from: Extreme insolation: climatic variation shapes the evolution of thermal tolerance at multiple scales
The climatic variability hypothesis (CVH) is a cornerstone of thermal ecology, predicting the evolution of wider organismal thermal tolerance ranges in more thermally variable environments. Thermal tolerance ranges depend on both upper and lower tolerance limits (critical thermal maxima [CTmax] and critical thermal minima [CTmin]), which may show different responses to environmental gradients. To delineate the relative effects of mean and extreme temperatures on thermal tolerances, we conducted a within-latitude comparative test of CVH predictions for army ants (Dorylinae) at multiple scales: across elevations, in seasonal versus aseasonal forests, and in subterranean versus surface microhabitats. Consistent with the CVH, thermally buffered subterranean species had narrower thermal tolerance ranges. Both CTmin and CTmax decreased with elevation for subterranean species. In contrast, aboveground species (those exposed to insolation) showed a decrease in CTmin but no change in CTmax across elevations. Furthermore, greater seasonal temperature variation in dry forests correlated with increased CTmax but not CTmin. These patterns suggest that CTmax and CTmin respond to different abiotic selective forces: habitat-specific exposure to extreme insolation corresponds to CTmax differences but not to CTmin variation. We predict that increasingly frequent heat spikes associated with climate change will have habitat-specific physiological consequences for ectothermic animals. Models predicting climate change impacts should account for species microhabitat uses and within-latitude differences in temperature seasonality.
Northern Hemisphere summer insolation and ice volume driven variations in hydrological environment in southwest China
<p>This dataset from a stalagmite record from Chongqing, Southwest China, provides a high-resolution trace element record from the Last Glacial-Interglacial (116.09 to 4.07 ka BP) up to the sub-centennial scale. Line scans of Element/Calcium ratios of the speleothem were measured by laser ablation ICPMS. Chronology is based on 58 U-Th ages.</p>
GOES-16 Derived 1-km resolution Daily Solar Insolation (2022)
<p><strong><span>Dataset Description</span></strong></p> <p><span>This is the University of Alabama in Huntsville (UAH) GOES satellite-derived solar insolation dataset. The dataset and method have been described in previous documents, including Jacobs et al. (2008), Paech et al. (2009), Mecikalski et al. (2011, 2018), Diak (2017) and Cheng et al. (2020). The data are in separate files for Julian days 1-365 (or 366). The dataset extends for the period 1 January 1985 through 31 December 2023.</span></p> <p><span>Prior to 2022, the dataset domain was on a 474 x 407 grid at 2 km resolution centered on the State of Florida. From 2022 onward the data domain is on a 1668 x 1668 grid of 1 km resolution that includes several Southeastern U. S. states in addition to Florida. The daily solar insolation is in units of MegaJoules per square meter (MJ/m^2/day), and the data themselves are in compressed ASCII format with the latitude (degrees) and longitude (degrees) of a given point listed. These solar insolation data are used to produce an evapotranspiration (ET) product that covers all watersheds that flow onto the extent of the Floridan aquifer system, which will allow water managers in north Florida to use the dataset when conducting water budget analyses that necessarily cross state borders. The ET data are subsequently used for climatological studies and water resources management.</span></p> <p><span>From 1985 to 2021, the solar insolation data was developed using native 1-km resolution GOES-East visible channel data. For each grid point, an averaging of 4 x 4 1-km resolution pixels was done to define a 2 km resolution product. For the 2022 to current datasets, native 500-meter, 10-minute resolution channel 2 (0.64 µm) data are used rom the GOES-16/-18 satellite's Advanced Baseline Imager. For each grid point from 2022 onward, an averaging of 2 x 2 500-meter resolution pixels occurred to define a 1 km resolution product. The GOES data were gathered from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).</span></p> <p><span>Prior to 2022, a simple climatological or daily model reanalysis based total precipitable water (TPW) correction was implemented to the solar insolation data. From 2022 onward, the TPW correction for solar insolation is performed using the NESDIS Blended Hydrometeorological Product suite (BLENDHYDRO) “blended total precipitable water” data. These data, as obtained from NOAA CLASS, represents a merging of microwave derived TPW products from multiple polar-orbiting and geostationary satellite sensors including: AMU/MHS onboard the NOAA and MetOp satellite series; SSMIS onboard the DMSP satellite series; ATMS onboard S-NPP and NOAA-20; Sounder onboard the GOES satellite series; and GPS Met onboard Orbview-1, and have a spatial resolution of 16 km at the Equator. From 24 hourly files per day, a daily average TPW grid was created and then mapped onto the 1 km x 1 km grid that spans the domain of solar insolation coverage.</span></p> <p><strong><span>Data Quality</span></strong></p> <p><span>For the solar insolation dataset, between 15 and 22 pyranometer stations from across the state of Florida are utilized, with ~30%<span> </span>used for calibration of the satellite-estimated model product and the remaining used for validation of model performance. Every effort was made to screen for data quality, with the highest quality data being reserved for calibration. These data were provided by three State of Florida Water Management District (WMD) weather station networks (South Florida (SF), Saint John’s River (SJR) and Southwest Florida (SWF), the University of Florida (UF) Institute of Food and Agricultural Sciences (IFAS) Florida Automated Weather Network (FAWN), and the United States Geological Survey (USGS) network.</span></p> <p><span>The following uncalibrated and calibrated pyranometer station-averaged statistics were developed for comparison of satellite-estimated and pyranometer-measured daily-integrated insolation at the ten verification pyranometer station locations: mean bias error (MBE), root mean square error (RMSE, and as a percentage of the mean pyranometer-measured value given in parentheses), and coefficient of determination (R2). As an example for 2023, the uncalibrated values are: MBE = –0.59 MJ/m^2/day, RMSE = 1.38 MJ/m^2/day (8%), and R2 = 0.96. The calibrated values are: MBE = 0.19 MJ/m^2/day, RMSE = 1.20 MJ/m^2/day (7%), and R2 = 0.97.</span></p> <p><strong><span>References</span></strong></p> <p><span>Cheng, P., A. Pour-Biazar, R. T. McNider, and J. R. Mecikalski, 2020: Validation of GOES-based surface insolation retrievals and its utility for model evaluation. <em>J. Atmos. Ocean Tech</em>., <strong>37</strong>, 553–571.</span></p> <p><span>Diak, G. R., 2017: Investigations of improvements to an operational GOES-satellite-data-based insolation system using pyranometer data from the U. S. Climate Reference Network (USCRN). <em>Remote Sens. Environ</em>., <strong>195</strong>, 79–95, doi:10.1016/j.rse. 2017.04.002.</span></p> <p><span>Jacobs, J., J. Mecikalski, and S. Paech, 2008: Satellite-based solar radiation, net radiation, and potential and reference evapotranspiration estimates over Florida. Technical Report. July 2008, 138 pp. http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf.</span></p> <p><span>Mecikalski, J. R., W. B. Shoemaker, Q. Wu, M. A. Holmes, S. J. Paech, and D. M. Sumner, 2018: A 20-Year high-resolution GOES insolation–evapotranspiration dataset for water resource management over the State of Florida. <em>J. Irrig. Drain. Eng</em>., <strong>144</strong>(9): 04018025.</span></p> <p><span>Mecikalski, J. R., D. M. Sumner, J. M. Jacobs, C. S. Pathak, S. J. Paech, and E. M. Douglas, 2011: Use of visible Geostationary Operational Meteorological Satellite imagery in mapping reference and potential evapotranspiration over Florida. <em>Evapotranspiration</em>. ISBN 978-953-307-251-7, Editor Leszek Labedzki, Chapter 10, pgs. 229-254.</span></p> <p><span>Paech, S. J., J. R. Mecikalski, D. M. Sumner, C. S. Pathak, Q. Wu, S. Islam, and T. Sangoyomi, 2009: A calibrated, high-resolution GOES satellite solar insolation product for a climatology of Florida evapotranspiration. <em>J. Amer. Water Resources Assoc.</em>, <strong>45</strong>, 1328-1342.</span></p>
Remote insolation forcing of orbital-scale South Asian summer monsoon variability
<p>CESM simulation data for the manuscript "Remote insolation forcing of orbital-scale South Asian summer monsoon variability", which has been submitted to Geophysical Research Letters.</p>
Dataset for "Excellent test-retest reliability of the six-minute walking distance measured by FeetMe insoles during tests conducted with a one-week interval by completely unassisted healthy adults in their homes."
<p>This is the dataset used in the scientific article "Excellent test-retest reliability of the six-minute walking distance measured by FeetMe insoles during tests conducted with a one-week interval by completely unassisted healthy adults in their homes." Participants (n=21) performed two 6MWTs at home while wearing the FeetMe<sup> </sup>insoles, and two 6MWTs at hospital while wearing FeetMe<sup> </sup>insoles and being assessed by a rater. All assessments were performed with a one-week interval between tests, no assistance was provided to the participants at home. Each column represents the 6MWD for each participant at one of the visits and using one of the measurement methods. Columns' headers provide clear description of the corresponding condition.</p>
Comparing Clinical Outcomes Using Two Insole Manufacture Techniques
ClinicalTrials.gov study NCT05444192. IPD Sharing: YES. Countries: 1. Publications: 2.
Eccentric Exercises Versus Heel Insole Lift for Achilles Tendinopathy in Football Players
ClinicalTrials.gov study NCT07360873. IPD Sharing: NO. Countries: 1. Publications: 25.
Multi-layer Insoles for a Patient-specific Approach to Shear and Pressure Reduction in Diabetes-related Foot Ulcer
ClinicalTrials.gov study NCT01844479. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Extreme insolation: climatic variation shapes the evolution of thermal tolerance at multiple scales
Open the record for dataset details and reuse information.
Insolation and CO2 impacts on the spatial differences of the MIS-9 and MIS-11 climate between monsoonal China and central Asia
<p>The model outputs we use are those performed in Yin & Berger (2012) where the climate of the last nine interglacials were simulated with the LOVECLIM model. The model outputs for MIS-11 and MIS-9 are used here to study the climate conditions in mid and eastern Asia.</p>
MRExo 4D Fit: Mass-Radius-Stellar-Mass-Insolation Small Planet
<p>A sample of 182 planets from the NASA Exoplanet Archive queried on 2023 March 6 for planets with masses and radii $> 3\sigma$ precision, planetary radii $<$ 4 \earthradius~ and stellar masses $<$ 1.5 \solmass described in Section 2 of Kanodia et al. 2023.</p> <p>We then perform a 4-D fit to the dataset described above, across planetary masses, radii, insolation fluxes, and stellar masses. We use 40 degrees across all four dimensions, and perform 100 trials for Monte-Carlo and Bootstrap simulations each. This is described in Section 3.1 of Kanodia et al. 2023.</p> <p>For ease of download, we have broken up the fit into three separate .zip files -</p> <p>1. Without the bootstrap or Monte-Carlo results (AllPlanet_RpLt4_StMlt1.5_MRSStM_d40_0MC_0BS.zip)</p> <p>2. With the bootstrap but not Monte-Carlo results (AllPlanet_RpLt4_StMlt1.5_MRSStM_d40_0MC_100BS.zip)</p> <p>3. Without the bootstrap but with the Monte-Carlo results (AllPlanet_RpLt4_StMlt1.5_MRSStM_d40_100MC_0BS.zip)</p> <p><em>Beyond 2-D Mass-Radius Relationships: A Nonparametric and Probabilistic Framework for Characterizing Planetary Samples in Higher Dimensions: Kanodia et al. 2023</em></p> <p> </p>
Home-based Gait Rehabilitation Service Using the Insole-type Gait Analysis Device for Children With Cerebral Palsy
ClinicalTrials.gov study NCT06461923. IPD Sharing: NO. Countries: 1. Publications: 15.
Sensor Glove and Non-Invasive Vibrotactile Feedback Insole to Improve Hand Prostheses Functions and Embodiment
ClinicalTrials.gov study NCT03924310. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of the Insole on Balance Capacity in Chronic Stroke
ClinicalTrials.gov study NCT03194282. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Analyzing Gait Pattern Using Insole-type Gait Analyzer in Patient With Gait Disturbance
ClinicalTrials.gov study NCT06442982. IPD Sharing: NO. Countries: 1. Publications: 8.
Pediatric Pes Planus: Effect of Insoles With and Without SFE
ClinicalTrials.gov study NCT05877482. IPD Sharing: NO. Countries: 1. Publications: 12.
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.