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Träfflista för sökning "WFRF:(Liu Anqi) srt2:(2024)"

Sökning: WFRF:(Liu Anqi) > (2024)

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1.
  • Chen, Anqi, et al. (författare)
  • Highly Sensitive Graphene Oxide-based Fabry-Perot Low-frequency Acoustic Sensor With Low-coherence Polarized Demodulation Using Three-step Phase-Shifting Arctan Algorithms
  • 2024
  • Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; In Press
  • Tidskriftsartikel (refereegranskat)abstract
    • Developing low-frequency acoustic senor with high sensitivity is crucial for diverse applications, ranging from seismic monitoring, military operations, to pipeline surveillance. Here, we have proposed a high-sensitivity graphene oxide (GO)-based Fabry-Perot low-frequency sensor, in which a 170 nm thick, large-area and uniformly GO film was prepared by a vacuum filtration method. To enhance the accuracy and stability of the sensor, a low-coherence interference system based on birefringent crystal blocks was designed utilizing a three-step phase-shifting arctangent algorithm. Our sensor exhibited a sensitivity of -93.48 dB re 1 rad/μPa at 6-60 Hz with a fluctuation of 0.6 dB. The minimum detectable pressure of the sensor was measured at 0.37 μPa/Hz1/2 @20 Hz with a signal to noise ratio of 135.41 dB. Overall, this sensor offers simplicity in preparation, high sensitivity, low detectable sound pressure, making it a significant asset for low-frequency acoustic applications.
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2.
  • Menkveld, Albert J., et al. (författare)
  • Nonstandard Errors
  • 2024
  • Ingår i: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
  • Tidskriftsartikel (refereegranskat)abstract
    • In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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