A Configuration Model with Triadic Closure

In this paper we present a configuration model with random triadic closure. This model possesses five fundamental properties: large transitivity, power law degree distributions, short path lengths, non-zero Pearson degree correlation, and the existence of community structures. We analytically derive the Pearson degree correlation and the clustering coefficient of the proposed model. By simulation we also test three well-known community detection algorithms on our model as well as other two benchmark models that are the LFR model and the ABCD model

Sensitivity Analysis for Coupled Structural-Acoustic System with Absorbing Material Using FEM/BEM

Since the acoustic impedance in water cannot be neglected with respect to the mechanical impedance, the acoustic radiation caused by the vibration of structures in the compressible fluid would react to the structure. Therefore, the fluid-structure interaction needs to be considered. The finite element method is used for structure vibration analysis and the boundary element method for acoustic analysis. Sound absorption materials are used to reduce the scattering sound field in the reference region. The sensitivity analysis of a fully coupled structural-acoustic system is proposed. Numerical tests verify the correctness of the proposed algorithm

Degree-degree Correlated Low-density Parity-check Codes Over a Binary Erasure Channel

Most existing works on analyzing the performance of a random ensemble of low-density parity-check (LDPC) codes assume that the degree distributions of the two ends of a randomly selected edge are independent. In the paper, we take one step further and consider ensembles of LDPC codes with degree-degree correlations. For this, we propose two methods to construct an ensemble of degree-degree correlated LDPC codes. We then derive a system of density evolution equations for such degree-degree correlated LDPC codes over a binary erasure channel (BEC). By conducting extensive numerical experiments, we show how the degree-degree correlation affects the performance of LDPC codes. Our numerical results show that LDPC codes with negative degree-degree correlation could improve the maximum tolerable erasure probability. Moreover, increasing the negative degree-degree correlation could lead to better unequal error protection (UEP) design.Comment: accepted by the 2023 IEEE International Symposium on Information Theory (ISIT

Seed- and solvent-free synthesis of ZSM-5 with tuneable Si/Al ratios for biomass hydrogenation

A novel method for the synthesis of MFI zeolites has been developed, which does not require any crystal seeds or solvent. The adaptability of this method was also evidenced; a series of ZSM-5 zeolites with differing Si/Al ratios (18∼∞) were synthesized, which to date, has been a challenge in the field of solvent-free synthesis. The materials were probed by in situ DRIFTS and 2D 27Al–19F HETCOR NMR spectroscopy, the results from which indicated that fluorine-containing species play a crucial role in the crystallization of ZSM-5. During the crystallization process F− anions coordinate with Al3+ cations, resulting in the formation of 6-coordinated “F–Al–O–Si” species. It is these intermediate species which drive the formation of tetrahedral [AlO4]− units in the zeolitic framework. The effectiveness of these materials as catalyst supports was subsequently assessed in the hydrogenation of levulinic acid and glucose, which exhibited a comparable performance to commercial ZSM-5. The simple, efficient and low-cost method presented herein provides an alternative approach for the green scaled-up synthesis of zeolites

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Synthesis of polyurea from 1,6-hexanediamine with CO2 through a two-step polymerization

Activation and transformation of CO2 is one of the important issues in the field of green and sustainable chemistry. Herein, CO2 as a carbon-oxygen resource was converted to CO2-polyurea with 1,6-hexanediamine through a two-step polymerization. The reaction parameters such as temperature, pressure and reaction time were examined; and several kinds of catalysts were screened in the absence and presence of NMP solvent. The formed oligomer and the final polyurea were characterized by FT-IR, VT-DRIFTS, NMR, XRD, AFM and their thermal properties were examined by TGA and DSC. It was confirmed that the final polyurea has a high thermal stability; the melting temperature is 269 °C and the decomposition temperature is above 300 °C. It is a brittle polymer with a tensile strength of 18.35 MPa at break length of 1.64%. The polyurea has a stronger solvent resistance due to the ordered hydrogen bond in structure. The average molecular weight should be enhanced in the post-polymerization as the appearance, hydrogen bond intensity, crystallinity, melting point and the thermal stability changed largely compared to the oligomer. The present work provides a new kind of polyurea, it is expected to have a wide application in the field of polymer materials. Keywords: CO2, Polyurea, Two-step polymerization, Catalysi

A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems

We propose an efficient Monte Carlo simulation method to address the multivariate uncertainties in acoustic–vibration interaction systems. The deep neural network acts as a general surrogate model to enhance the sampling efficiency of Monte Carlo Simulation. Singular Value Decomposition - Radial Basis Functions (SVD-RBF) acts as a bridge between the original full model and the neural network, enabling the training datasets of the neural network to be evaluated rapidly from a reduced-order model. The snapshots of full order models are obtained with isogeometric analysis, in which we couple two numerical schemes for vibro–acoustic interaction problems: the isogeometric finite element method for simulating vibration of Kirchhoff–Love shells and isogeometric boundary element method for exterior acoustic waves. Numerical results show that the proposed algorithm can significantly improve the efficiency of uncertainty analysis