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More than 100 U.S. institutions of higher education hosted Confucius Institutes (CIs), Chinese government-funded language and culture centers, on campus during the late 2000s and 2010s. While CIs provided a source of funding and other resources that enabled U.S. colleges and universities to build capacity, offer supplemental programming, and engage with the local community, CIs presented an added, legitimate source of risk to host institutions with respect to academic freedom, freedom of expression, and national security. By 2017, deteriorating U.S.-China relations led some U.S. colleges and universities to reconsider the value of having a CI on campus. Sustained interest by Congress and political pressure led numerous U.S.-based CIs to close, especially following the passage of the Fiscal Year 2019 National Defense Authorization Act, which contained a provision that ultimately barred institutions receiving Department of Defense (DOD) critical language flagship funding in Chinese from hosting a CI. While this provision allowed for a waiver process - and several affected colleges and universities applied for waivers in 2018 and 2019 - DOD did not issue any waivers. Today, seven CIs remain on U.S. university and college campuses. At the request of DOD, Confucius Institutes at U.S. Institutions of Higher Education presents a set of findings and recommendations for waiver criteria to potentially permit the continued presence of CIs on U.S. university campuses that also receive DOD funding. [Contributors to this report include the U.S. Science and Innovation Policy and Policy and Global Affairs.]

Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the operation makes the analysis even more challenging, necessitating a rapid characterization. We propose a new method to characterize the thermal properties of particulate composites based on actual micro-images. Our computer-vision-based approach constructs 3D images from stacks of 2D SEM images and then extracts several representative elemental volumes (REVs) from the reconstructed images at random places, which leads to having a range of geometrical features for different REVs. A deep learning algorithm is designed based on convolutional neural nets to take the shape of the geometry and result in the effective conductivity of the REV. The training of the network is performed in two methods: First, based on implementing a coarser grid that uses the average values of conductivities from the fine grid and the resulted effective conductivity from the DNS solution of the fine grid. The other method uses conductivity values on cross sections from each REV in different directions. The results of training based on averaging show that using a coarser grid in the network does not have a meaningful effect on the network error; however, it decreases the training time up to three orders of magnitude. We showed that one general network can make accurate predictions using different types of electrode images, representing the difference in the geometry and constituents. Moreover, training based on averaging is more accurate than training based on cross sections. The study of the robustness of implementing a machine learning technique in predicting the thermal percolation shows the prediction error is almost half of the error from predictions based on the volume fraction.

In this paper, we show that a revised convolutional recurrent neural network (CRNN) can decrease, by orders of magnitude, the time needed for the phase-resolved prediction of waves in a spatiotemporal domain of a nonlinear dispersive wave field. The problem of predicting such waves suffers from two major challenges that have so far hindered analytical or direct computational solutions in real time or faster: (i) the reconstruction problem, that is, how one can calculate from measurable wave amplitude data the state of the wave field (wave components, nonlinear couplings, etc.), and (ii) if such a reconstruction is in hand, how to integrate equations fast enough to be able to predict an upcoming rouge wave in a timely manner. Here, we demonstrate that these two challenges can be overcome at once through advanced machine learning techniques based on spatiotemporal patches of the time history of wave height data in the domain. Specifically, as a benchmark here we consider equations that govern the evolution of weakly nonlinear surface gravity waves such as those propagating on the surface of the oceans. For the case of oceanic surface waves considered here, we demonstrate that the proposed methodology, while maintaining a high accuracy, can make phase-resolved predictions more than two orders of magnitude faster than numerically integrating governing equations.

A temperature-dependent approach involving Green-Kubo equilibrium atomic and spin dynamics (GKEASD) is reported to assess phonon and magnon thermal transport processes accounting for phonon-magnon interactions. Using body-center cubic (BCC) iron as a case study, GKEASD successfully reproduces its characteristic temperature-dependent spiral and lattice thermal conductivities. The non-electronic thermal conductivity, i.e., the sum of phonon and magnon thermal conductivities, calculated using GKEASD for BCC Fe agrees well with experimental measurements. Spectral energy analysis reveals that high-frequency phonon-magnon scattering rates are one order of magnitude larger than those at low frequencies due to energy scattering conservation rules and high densities of states. Higher temperatures further accentuate this phenomenon. This new framework fills existing gaps in simulating thermal transport across the ferro- to para-magnetic transition. Future application of this methodology to phonon- and magnon-dominant insulators and semiconductors will enhance understanding of emerging thermoelectric, spin caloritronic and superconducting materials.

The measured frequencies and intensities of different first- and second- order Raman peaks of suspended graphene are used to show that optical phonons and different acoustic phonon polarizations are driven out of local equilibrium inside a sub-micron laser spot. The experimental results are correlated with a first principles-based multiple temperature model to suggest a considerably lower equivalent local temperature of the flexural phonons than those of other phonon polarizations. The finding reveals weak coupling between the flexural modes with hot electrons and optical phonons. Since the ultrahigh intrinsic thermal conductivity of graphene has been largely attributed to contributions from the flexural phonons, the observed local non-equilibrium phenomena have important implications for understanding energy dissipation processes in graphene-based electronic and optoelectronic devices, as well as in Raman measurements of thermal transport in graphene and other two-dimensional materials., Comment: 10 pages, 5 figures

Using the linearized Boltzmann transport equation and perturbation theory, we analyze the reduction in the intrinsic thermal conductivity of few-layer graphene sheets accounting for all possible three-phonon scattering events. Even with weak coupling between layers, a significant reduction in the thermal conductivity of the out-of-plane acoustic modes is apparent. The main effect of this weak coupling is to open many new three-phonon scattering channels that are otherwise absent in graphene. However, reflection symmetry is only weakly broken with the addition of multiple layers, and ZA phonons still dominate thermal conductivity. We also find that reduction in thermal conductivity is mainly caused by lower contributions of the higher-order overtones of the fundamental out-of-plane acoustic mode. The results compare remarkably well over the entire temperature range with measurements of graphene and graphite.

We report finite-volume simulations of the phonon Boltzmann transport equation (BTE) for heat conduction across the heterogeneous interfaces in SiGe superlattices. The diffuse mismatch model incorporating phonon dispersion and polarization is implemented over a wide range of Knudsen numbers. The results indicate that the thermal conductivity of a Si/Ge superlattice is much lower than that of the constitutive bulk materials for superlattice periods in the submicron regime. We report results for effective thermal conductivity of various material volume fractions and superlattice periods. Details of the non-equilibrium energy exchange between optical and acoustic phonons that originate from the mismatch of phonon spectra in silicon and germanium are delineated for the first time. Conditions are identified for which this effect can produce significantly more thermal resistance than that due to boundary scattering of phonons., Comment: 32 pages, 11 figures, 2 tables. Paper presented at the 2009 ASME/Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems, MEMS and NEMS (InterPACK '09), San Francisco, CA, Jul 19-23, 2009