8,708 research outputs found
Electronic Structures of Graphene Layers on Metal Foil: Effect of Point Defects
Here we report a facile method to generate a high density of point defects in
graphene on metal foil and show how the point defects affect the electronic
structures of graphene layers. Our scanning tunneling microscopy (STM)
measurements, complemented by first principle calculations, reveal that the
point defects result in both the intervalley and intravalley scattering of
graphene. The Fermi velocity is reduced in the vicinity area of the defect due
to the enhanced scattering. Additionally, our analysis further points out that
periodic point defects can tailor the electronic properties of graphene by
introducing a significant bandgap, which opens an avenue towards all-graphene
electronics.Comment: 4 figure
Direct reconstruction of dynamical dark energy from observational Hubble parameter data
Reconstructing the evolution history of the dark energy equation of state
parameter directly from observational data is highly valuable in
cosmology, since it contains substantial clues in understanding the nature of
the accelerated expansion of the Universe. Many works have focused on
reconstructing using Type Ia supernova data, however, only a few studies
pay attention to Hubble parameter data. In the present work, we explore the
merit of Hubble parameter data and make an attempt to reconstruct from
them through the principle component analysis approach. We find that current
Hubble parameter data perform well in reconstructing ; though, when
compared to supernova data, the data are scant and their quality is worse. Both
CDM and evolving models can be constrained within at
redshifts
and even at redshifts 0.1 z 1 by
using simulated data of observational quality.Comment: 25 pages, 11 figure
Hiding Functions within Functions: Steganography by Implicit Neural Representations
Deep steganography utilizes the powerful capabilities of deep neural networks
to embed and extract messages, but its reliance on an additional message
extractor limits its practical use due to the added suspicion it can raise from
steganalyzers. To address this problem, we propose StegaINR, which utilizes
Implicit Neural Representation (INR) to implement steganography. StegaINR
embeds a secret function into a stego function, which serves as both the
message extractor and the stego media for secure transmission on a public
channel. Recipients need only use a shared key to recover the secret function
from the stego function, allowing them to obtain the secret message. Our
approach makes use of continuous functions, enabling it to handle various types
of messages. To our knowledge, this is the first work to introduce INR into
steganography. We performed evaluations on image and climate data to test our
method in different deployment contexts
Development of a trench cutting re-mixing deep wall method model test device
The trench cutting re-mixing deep wall (TRD) is a new type of underground waterproof curtain. Mixing uniformity is the key index affecting the efficiency and quality of this method. However, because of many influencing factors, existing theories cannot be used to express the relationship between various factors and mixing uniformity. By analyzing the cutting and mixing process of the TRD method, the main factors affecting the uniformity of the mixing were obtained. A model test device was designed and manufactured, based on Buckingham's pi theorem. The validity of the model test device was verified through a comparative analysis of model and field test results. The model test device was demonstrated to be able to simulate the mixing process of the TRD method. The results provide guidance for promotion and better application of the TRD method
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