Solar-induced chlorophyll fluorescence sheds light on global evapotranspiration
Not provided.
Environmental Sciences & Ecology↗
Engineering topics
Zhou, Kai
Publications and source records attributed to Zhou, Kai.
Defect Engineering of WO 3 by Rapid Flame Reduction for Efficient Photoelectrochemical Conversion of Methane into Liquid Oxygenates
Photoelectrochemical (PEC) conversion is a promising way to use methane (CH 4 ) as a chemical building block without harsh conditions. However, the PEC conversion of CH 4 to value-added chemicals remains challenging due to the thermodynamically favorable overoxidation of CH 4 . Here, we report WO 3 nanotube (NT) photoelectrocatalysts for PEC CH 4 conversion with high liquid product selectivity through defect engineering. By tuning the flame reduction treatment, we carefully controlled the oxygen vacancies of WO 3 NTs. The optimally reduced WO 3 NTs suppressed overoxidation of CH4 showing a high total C1 liquid selectivity of 69.4% and a production rate of 0.174 μmol cm –2 h –1 . Scanning electrochemical microscopy revealed that oxygen vacancies can restrain the production of hydroxyl radicals, which, in excess, could further oxidize C1 intermediates to CO 2 . Additionally, band diagram analysis and computational studies elucidated that oxygen vacancies thermodynamically suppress overoxidation. In conclusion, this work introduces a strategy for understanding and controlling the selectivity of photoelectrocatalysts for direct conversion of CH 4 to liquids.
36 MATERIALS SCIENCE↗
Examination of nucleon distribution with Bayesian imaging for isobar collisions
Relativistic collision of isobaric systems is found to be valuable in differentiating the nucleon distributions for nuclei with the same mass number. In recent contrast experiment of $^{96}_{44}$Ru + $^{96}_{44}$Ru versus $^{96}_{40}$Zr + $^{96}_{40}$Zr collisions at $\sqrt{s_{NN}}$ = 200 GeV, the ratios of multiplicity distribution, elliptic flow, triangular flow, and radial flow are precisely measured and found to be significantly different from unity, indicating the difference in the shapes of the isobar pair. Here, in this work, we investigate the feasibility of nuclear structure reconstruction from heavy-ion collision observables. We perform Bayesian Inference with employing the Monte-Carlo Glauber model as an estimator of the mapping from nuclear structure to the final state observables and to provide the mock data for reconstruction. By varying combination of observables included in the mock data, we find it plausible to infer Woods–Saxon parameters from the observables. We also observe that single-system multiplicity distribution for the isobar system, rather than their ratio, is crucial to simultaneously determine the nuclear structure for the isobar system.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Reconstructing the neutron star equation of state from observational data via automatic differentiation
Neutron star observables like masses, radii, and tidal deformability are direct probes to the dense matter equation of state (EoS). Here, a novel deep learning method that optimizes an EoS in the automatic differentiation framework of solving inverse problems is presented. The trained neural network EoS yields narrow bands for the relationship between the pressure and speed of sound as a function of the mass density. The results are consistent with those obtained from conventional approaches and the observational bound on the tidal deformability inferred from the gravitational wave event, GW170817.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Continuum-extrapolated NNLO valence PDF of the pion at the physical point
Not Available
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Rethinking the ill-posedness of the spectral function reconstruction — Why is it fundamentally hard and how Artificial Neural Networks can help
Reconstructing hadron spectral functions through Euclidean correlation functions are of the important missions in lattice QCD calculations. However, in a Källen–Lehmann (KL) spectral representation, the reconstruction is observed to be ill-posed in practice. It is usually ascribed to the fewer observation points compared to the number of points in the spectral function. In this paper, by solving the eigenvalue problem of continuous KL convolution, we show analytically that the ill-posedness of the inversion is fundamental and it exists even for continuous correlation functions. We discussed how to introduce regulators to alleviate the predicament, in which include the Artificial Neural Networks (ANNs) representations recently proposed by the Authors in another study. The uniqueness of solutions using ANNs representations is manifested analytically and validated numerically. Reconstructed spectral functions using different regularization schemes are also demonstrated, together with their eigen-mode decomposition. We observe that components with large eigenvalues can be reliably reconstructed by all methods, whereas those with low eigenvalues need to be constrained by regulators.
97 MATHEMATICS AND COMPUTING↗
Neural network reconstruction of the dense matter equation of state from neutron star observables
The Equation of State (EoS) of strongly interacting cold and hot ultra-dense QCD matter remains a major challenge in the field of nuclear astrophysics. With the advancements in measurements of neutron star masses, radii, and tidal deformabilities, from electromagnetic and gravitational wave observations, neutron stars play an important role in constraining the ultra-dense QCD matter EoS. Here, in this work, we present a novel method that exploits deep learning techniques to reconstruct the neutron star EoS from mass-radius (M-R) observations. We employ neural networks (NNs) to represent the EoS in a model-independent way, within the range of ~1-7 times the nuclear saturation density. The unsupervised Automatic Differentiation (AD) framework is implemented to optimize the EoS, so as to yield through TOV equations, an M-R curve that best fits the observations. We demonstrate that this method works by rebuilding the EoS on mock data, i.e., mass-radius pairs derived from a randomly generated polytropic EoS. The reconstructed EoS fits the mock data with reasonable accuracy, using just 11 mock M-R pairs observations, close to the current number of actual observations.
79 ASTRONOMY AND ASTROPHYSICS↗
From lattice QCD to in-medium heavy-quark interactions via deep learning
Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks, which can be characterized by real (V R (T, r)) and imaginary (V I (T, r)) potentials, as functions of temperature and spatial separation. Recently, the masses and thermal widths of up to 3S and 2P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network (DNN), here, we obtain model-independent results for V R (T, r) and V I (T, r). The temperature dependence of V R (T, r) was found to be very mild between T ≈ 0 - 330 MeV. Meanwhile, VI(T, r) shows rapid increase with T and r, which is much larger than the perturbation theory based expectations.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A machine learning study on spinodal clumping in heavy ion collisions
Possible observables of baryon number clustering due to the instabilities occurring at a first order QCD phase transition are discussed. The dynamical formation of baryon clusters at a QCD phase transition can be described by numerical fluid dynamics, augmented with a gradient term and an equation of state with a mechanically unstable region. It is shown that the dynamical description of this phase transition, in nuclear collisions, will lead to the formation of dense baryon clusters at the phase boundary. State-of-the-art machine learning methods find that the coordinate space clumping leaves characteristic imprints on the spatial net density distribution in almost every event. On the other hand the momentum distributions do not show any clear event-by-event features. Lastly, it is shown that the 'third order' cumulant, the skewness, shows a peak at the beam energy where the system, created in the heavy ion collision, reaches the deconfinement phase transition.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Identifying the nature of the QCD transition in heavy-ion collisions with deep learning
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade “after-burner”. Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Effect of Nb Content on Microstructures and Mechanical Properties of Ti-xNb-2Fe Alloys
β-Type Ti-Nb-based alloys exhibit satisfactory biocompatibility and low Young’s modulus for biomedical applications. The microstructure and mechanical properties of a series of Ti-(14, 16, 18, 20, 22, 24)Nb-2Fe alloys fabricated by arc melting were investigated by XRD, optical microscopy, and tensile tests. Both ω and α″ phases existed in the Ti-14Nb-2Fe alloy, while just a single β phase existed in the other alloys. Twinning is an important deformation mechanism that causes work hardening and twinning-induced plasticity. It was found in the Ti-(14, 16, 18, 20)Nb-2Fe alloys and not in the Ti-22Nb-2Fe alloy. The Ti-14Nb-2Fe alloy exhibited the highest tensile strength and the highest Young’s modulus owing to the existence of the ω phase. The tensile strength decreased gradually from 830 MPa (highest) for the Ti-14Nb-2Fe alloy to 540 MPa (lowest) for the Ti-24Nb-2Fe alloy with an increase in the Nb content. The Young’s modulus decreased from 90 GPa for the Ti-14Nb-2Fe alloy to 63 GPa for the Ti-22Nb-2Fe alloy and then increased to 71 GPa for the Ti-24Nb-2Fe alloy. Elongation shows the same trend as the Young’s modulus. The Ti-22Nb-2Fe alloy, with a low Young’s modulus of 63 GPa, tensile strength of 570 MPa, and 15% elongation, was found suitable for biomedical applications. The Ti-20Nb-2Fe alloy also exhibits a high tensile strength, a Young’s modulus ratio of 9.24 × 10{sup −3}, and 18% elongation and is thus considered another valuable Ti alloy for biomedical applications.
36 MATERIALS SCIENCE↗
Classify QCD phase transition with deep learning
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.
74 ATOMIC AND MOLECULAR PHYSICS↗
GALNT3 inhibits NF-κB signaling during influenza A virus infection
Highlights: • A direct role of GANLT3 in regulating host responses during IAV infection. • First report that GALNT3 inhibits NF-κB signaling by preventing the translocation of p-P65 into nucleus. • Reveal the direct involvement of GALNT3 during viral infections. Protein glycosylation, attaching glycans covalently onto amino acid side chains of protein by various glycosyltransferase, is the most common post-translational modification. The UDP-GalNAc transferase 3 (GANLT3), encoded by Galnt3, transfers N-acetyl-d-galactosamine to hydroxyl groups of the side chains of Ser/Thr residues, initiating mucin type O-glycosylation of proteins. Most researches as yet focus on the involvement and abnormal expression of GALNT3 in various tumors. In this study, we found that GALNT3 was significantly decreased in the lungs after influenza A virus (IAV) infection in mice. Overexpression of GALNT3 in cell lines markedly inhibited IAV replication. Further experiments demonstrated that GALNT3 inhibited NF-κB signaling by preventing the translocation of phosphorylated P65 into nucleus. Therefore, our results reveal an important role of GALNT3 in regulating host responses during IAV infection, indicating the broad functions of the GALNT family, and the direct involvement of GALNTs during viral infections.
60 APPLIED LIFE SCIENCES↗
An equation-of-state-meter of quantum chromodynamics transition from deep learning
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Finally, such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗