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Preparing TXST for an AI-Driven Future: Enrollment Forecasting & Strategic Program Planning
No full textHow should TXST strategically plan its academic
portfolio when AI threatens to reshape career
prospects across different fields?
This analysis addresses this challenge through risk
assessment, enrollment forecasting, and strategic
recommendations
NSF COLDEX Delay Doppler Radar Profiles of Southern Dome A, Antarctica
No full text
Underway coherent radar data, transformed from the along track space domain to the wavenumber domain to reveal the Doppler response of signals, indicates the direction of returning echoes. This delay-Doppler processing can be used to constrain the degree of scattering or specularity of the target surfaces (Tyler et al. 1992, Michaelides and Schroeder, 2019).
These MARFA 60 MHz radar data have been processed using a delay-Doppler approach to obtain three views of the echo data: one pointing 11° ahead of the aircraft, one pointing 11° behind the aircraft, and one directly below the aircraft. The data is organized with individual transects as separate netCDF4 files. Delay Doppler processing operated on a 1 m sampled version of the raw data, using a 1000 m aperture. The fast time sampling of the product is 0.2 µsec, and the along track sampling is 500 m.
The maximum value in each delay doppler bin was found. The channels were scaled using the noise floor. The data are processed using only the right aircraft wing high gain channel (MARFA channel 6) to widen the across track beam pattern and reduce the effect of aircraft roll.
X and Y GPS locations of the aircraft in EPSG 3031 (Antarctic Polar Stereographic) coordinates are included.
Browse images in PNG format (*_specularity.gmt) show the following:
An RGB version with the nadir view loaded into blue, and the sum of the fore and aft views loaded into the red and green channels, with the tracked surface and the bottom of the stratigraphic unit traced.
A grayscale view of the sum of the fore and aft views.
A grayscale view of the nadir view.
A tab delimited ASCII table (*_specularity.gmt) is included for each transect with the following columns:
x: easting coordinate in EPSG 3031 projection [meters]
y: northing coordinate in EPSG 3031 projection [meters]
surface[s]: time delay to the surface [microseconds]
base_specular[s]: time delay to base of the specular englacial unit [microseconds]
deep_scattering_zone[s]: time delay to 1 microsecond into scattering basal unit in [microseconds]
slope: slope of the transition the base of the specular englacial unit
slope: srf_spec: signal to noise ratio of the detected surface return [dB]. Note for the high gain channel this will be usually saturated
dsz_power: signal to noise ratio of the echos in the basal unit [dB]
The netCDF4 files are structured as follows:
netcdf TRANSECTNAME_dd_analysis.nc {
group: channels {
dimensions:
fast_time = 320;
direction = 3;
aperture = UNLIMITED;
variables:
double fast_time(fast_time=XXX);
double direction(direction=3);
float normalized_max_in_bin(aperture=XXX, fast_time=320, direction=3);
:_ChunkSizes = 1U, 320U, 3U; // uint
double X(aperture=XXX);
:units = "m";
:_ChunkSizes = XXXU; // uint
double Y(aperture=XXX);
:units = "m";
:_ChunkSizes = XXXU; // uint
}
// global attributes:
:increment = 500L; // long
:aperture = 1000L; // long
:transect = "TRANSECTNAME";
:processor = "denoise1";
:channel = 6L; // long
}
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Fan's attitude towards AI-Assisted Coaching in Sports
No full textThis dataset provides the raw survey data used to examine sports fans’ attitudes toward AI-assisted coaching in sports decision-making, guided by the Heuristic-Systematic Model (HSM). It supports transparency, replication, and further analysis of the study’s findings.
It includes anonymized survey responses from 261 college student sports fans in the United States. Variables measure attitudes toward AI-assisted coaching, perceptions of AI accuracy and objectivity, team performance heuristics, critical thinking about AI, self-reported AI knowledge, and preferences for AI applications in sports. Data consist of Likert-scale items and demographic variables, with no personally identifiable information included.
The dataset focuses on fan perceptions of AI use in college football coaching decisions, particularly AI as a decision-support tool. It enables replication of regression and descriptive analyses and supports future research on AI acceptance and technology use in sports coaching contexts
Source code for "Randomization Times under Quantum Chaotic Hamiltonian Evolution"
No full textMATLAB files to produce data in "Randomization Times under Quantum Chaotic Hamiltonian Evolution", arxiv: 2512.25074
