76 research outputs found
Ultrasound cavitation induced nucleation in metal solidification: an analytical model and validation by real-time experiments
Microstructural refinement of metallic alloys via ultrasonic melt processing (USMP) is an environmentally friendly and promising method. However, so far there has been no report in open literature on how to predict the solidified microstructures and grain size based on the ultrasound processing parameters.In this paper, an analytical model is developed to calculate the cavitation enhanced undercooling and the USMP refined solidification microstructure and grain size for Al-Cu alloys. Ultrafast synchrotron X-ray imaging and tomography techniques were used to collect the real-time experimental data for validating the model and the calculated results. The comparison between modeling and experiments reveal that there exists an effective ultrasound input power intensity for maximizing the grain refinement effects for the Al-Cu alloys, which is in the range of 20-45 MW/m2. In addition, a monotonous increase in temperature during USMP has negative effect on producing new nuclei, deteriorating the benefit of microstructure refinement due to the application of ultrasound
Galaxy-galaxy weak-lensing measurement from SDSS: II. host halo properties of galaxy groups
As the second paper of a series on studying galaxy-galaxy lensing signals
using the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we present our
measurement and modelling of the lensing signals around groups of galaxies. We
divide the groups into four halo mass bins, and measure the signals around four
different halo-center tracers: brightest central galaxy (BCG),
luminosity-weighted center, number-weighted center and X-ray peak position. For
X-ray and SDSS DR7 cross identified groups, we further split the groups into
low and high X-ray emission subsamples, both of which are assigned with two
halo-center tracers, BCGs and X-ray peak positions. The galaxy-galaxy lensing
signals show that BCGs, among the four candidates, are the best halo-center
tracers. We model the lensing signals using a combination of four
contributions: off-centered NFW host halo profile, sub-halo contribution,
stellar contribution, and projected 2-halo term. We sample the posterior of 5
parameters i.e., halo mass, concentration, off-centering distance, sub halo
mass, and fraction of subhalos via a MCMC package using the galaxy-galaxy
lensing signals. After taking into account the sampling effects (e.g. Eddington
bias), we found the best fit halo masses obtained from lensing signals are
quite consistent with those obtained in the group catalog based on an abundance
matching method, except in the lowest mass bin. Subject headings: (cosmology:)
gravitational lensing, galaxies: clusters: generalComment: 12 pages, 7 figures, submitted to Ap
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?
The rapid development of digital economy has led to the emergence of various
black and shadow internet industries, which pose potential risks that can be
identified and managed through digital risk management (DRM) that uses
different techniques such as machine learning and deep learning. The evolution
of DRM architecture has been driven by changes in data forms. However, the
development of AI-generated content (AIGC) technology, such as ChatGPT and
Stable Diffusion, has given black and shadow industries powerful tools to
personalize data and generate realistic images and conversations for fraudulent
activities. This poses a challenge for DRM systems to control risks from the
source of data generation and to respond quickly to the fast-changing risk
environment. This paper aims to provide a technical analysis of the challenges
and opportunities of AIGC from upstream, midstream, and downstream paths of
black/shadow industries and suggest future directions for improving existing
risk control systems. The paper will explore the new black and shadow
techniques triggered by generative AI technology and provide insights for
building the next-generation DRM system
Robust magnetism against pressure in non-superconducting samples prepared from lutetium foil and H2/N2 gas mixture
Recently, the claim of "near-ambient superconductivity" in a N-doped lutetium
hydride attracted enormous following-up investigations in the community of
condensed matter physics and material sciences. But quite soon, the
experimental results from different groups indicate consistently that no
evidence of near-ambient superconductivity is found in the samples synthesized
by the same method as the reported one, or by the other alternative methods.
From our extended high-pressure heat capacity and magnetic susceptibility
measurements on the samples prepared with the lutetium foil and H2/N2 gas
mixture, we report the finding of a magnetic transition at the temperature
about 56 K. Our results show that this magnetic phase is robust against
pressure up to 4.3 GPa, which covers the critical pressure of boosting the
claimed near room temperature superconductivity.Comment: 14 pages, 4 figure
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Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network
Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties
Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)
The Wide Field Survey Telescope (WFST) is a dedicated photometric survey
facility under construction jointly by the University of Science and Technology
of China and Purple Mountain Observatory. It is equipped with a primary mirror
of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73
Gpix on the main focus plane to achieve high-quality imaging over a field of
view of 6.5 square degrees. The installation of WFST in the Lenghu observing
site is planned to happen in the summer of 2023, and the operation is scheduled
to commence within three months afterward. WFST will scan the northern sky in
four optical bands (u, g, r, and i) at cadences from hourly/daily to
semi-weekly in the deep high-cadence survey (DHS) and the wide field survey
(WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and
22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during
a photometric night, respectively, enabling us to search tremendous amount of
transients in the low-z universe and systematically investigate the variability
of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23
and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate
explorations of energetic transients in demand for high sensitivity, including
the electromagnetic counterparts of gravitational-wave events detected by the
second/third-generation GW detectors, supernovae within a few hours of their
explosions, tidal disruption events and luminous fast optical transients even
beyond a redshift of 1. Meanwhile, the final 6-year co-added images,
anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS,
will be of significant value to general Galactic and extragalactic sciences.
The highly uniform legacy surveys of WFST will also serve as an indispensable
complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP
Model Predictive PI Circulating Current Control for Modular Multilevel Converter
Significant circulating currents in the modular multilevel converter (MMC) increase system losses and complicate heat-sink design. Conventional PI and PR controllers can achieve steady-state error adjustment, but are sensitive to parameter changes and model uncertainty, heavily relying on coordinate transformations and careful design of model parameters. Model predictive control (MPC) has the characteristics of simple design, good robustness, and excellent dynamic response; however, it encountered the complexity of adjusting weighting factors. This paper proposed circulating the current model predictive proportional integral control (MPPIC) method in abc reference frame. This hybrid control solution utilized the predictive model and traditional PI algorithm to combine the advantages of nonlinear and linear control. Compared with existing suppression methods, this method avoided complex mathematical operations and a selection of weight coefficients, was easy to implement, and can effectively suppress circulating currents under different modulation ratios. Simulations were conducted on MATLAB/Simulink to verify the effectiveness of the proposed control strategy. MPPIC can not only distinctly suppress the circulating currents, but also reduce the overall voltage fluctuation of sub-modules capacitors under different modulation ratios, and had almost no any adverse effect on the performance of MMC
A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures
International audienceThis paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed
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