We develop a machine learning model, which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time scales with excellent accuracy and transferability. We use the network transferability to predict dynamic heterogeneity down to the experimental glass transition temperature Tg, where structural relaxation cannot be analyzed using molecular dynamics simulations. The results indicate that the strength, the geometry, and the characteristic length scale of the dynamic heterogeneity evolve much more slowly near Tg compared to their evolution at higher temperatures. Our results show that machine learning techniques can provide physical insights on the nature of the glass transition that cannot be gained using conventional simulation techniques.

We consider the system of two parallel sheets of graphene which are moving with relative parallel velocity $\vec{v}$ and calculate the Casimir energy by using the scattering approach. We analyze in detail the normal (perpendicular to the planes) Casimir force for two systems—graphene/graphene and ideal metal/graphene. In the non-relativistic case v ≪ vF, the relative correction to the Casimir energy $({{ \mathcal E }}_{v}-{{ \mathcal E }}_{0})/{{ \mathcal E }}_{0}$ is proportional to the (v/c)2 (the maximum value is 0.0033 for the gapeless case and v = vF) for the first system, and it is zero up to the Fermi velocity v = vF for system ideal metal/graphene.

Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine ( Vitis vinifera L.). Vineyard’s sustainability is threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key measurements in evaluating vines’ sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of tissue quality and plant health across plant species.

Based on a tight-binding model for the electron system, we investigate the transfer of energy, momentum, and angular momentum mediated by electromagnetic fields among buckminsterfullerene ( C 60 ) and graphene nanostrips. Our nonequilibrium Green's function approach enables calculations away from local thermal equilibrium where the fluctuation-dissipation theorem breaks down. For example, the forces between C 60 and current-carrying nanostrips are predicted. It is found that the presence of current enhances the van der Waals attractive forces. For two current-carrying graphene strips rotated at some angle, the fluctuational force and torque are much stronger at the nanoscale compared to that of the static Biot-Savart law.

The near-field radiative heat transfer (NFRHT) between one-dimensional metamaterials comprised of phonon dielectric multilayers was investigated experimentally. Large-size (1 × 1 cm 2 ) near-field samples were fabricated using Si C , Si O 2 , and Ge layers at a certain gap distance, and the effects of layer-stacking order and phonon-resonance quality on NFRHT were examined. The measured results show good agreement with the theoretical results obtained by employing the transmission-matrix method. Super-Planckian thermal radiation was observed between emitters and receivers with identical structures. The failure of effective-medium theory (EMT) at predicting the near-field heat flux has been evidenced by measurements, particularly in the presence of bounded surface modes, such as the epsilon-near-zero mode. Additionally, analyses have shown that, in specific scenarios, the EMT can offer reasonable physical insights into the underlying coupling process from the perspective of homogenized media. Furthermore, the conditions for applying the EMT in the near-field regime were also touched upon.

I'll review the known results about BPS indices, which encode in particular the entropy of BPS black holes, appearing in string compactifications down to four dimensions with various number of supersymmetries. First, I'll recall the well-known results about BPS states in N=8 and N=4 compactifications, and then present what is known about them in the N=2 case. Depending on time, I hope to cover some recent advances where an important role was played by (mock) modular symmetry.