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The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods. 

Charge transfer is a fundamental interface process that can be harnessed for light detection, photovoltaics, and photosynthesis. Recently, charge transfer was exploited in nanophotonics to alter plasmon polaritons by involving additional non-polaritonic materials to activate the charge transfer. Yet, direct charge transfer between polaritonic materials has not been demonstrated. We report the direct charge transfer in pure polaritonic van der Waals (vdW) heterostructures of α-MoO3/graphene. We extracted the Fermi energy of 0.6 eV for graphene by infrared nano-imaging of charge transfer hyperbolic polaritons in the vdW heterostructure. This unusually high Fermi energy is attributed to the charge transfer between graphene and α-MoO3. Moreover, we have observed charge transfer hyperbolic polaritons in multiple energy–momentum dispersion branches with a wavelength elongation of up to 150%. With the support from the density functional theory calculation, we find that the charge transfer between graphene and α-MoO3, absent in mechanically assembled vdW heterostructures, is attributed to the relatively pristine heterointerface preserved in the epitaxially grown vdW heterostructure. The direct charge transfer and charge transfer hyperbolic polaritons demonstrated in our work hold great promise for developing nano-optical circuits, computational devices, communication systems, and light and energy manipulation devices. 

The modeling of the interaction between brain structure and function using deep learning techniques has yielded remarkable success in identifying potential biomarkers for different clinical phenotypes and brain diseases. However, most existing studies focus on one-way mapping, either projecting brain function to brain structure or inversely. This type of unidirectional mapping approach is limited by the fact that it treats the mapping as a one-way task and neglects the intrinsic unity between these two modalities. Moreover, when dealing with the same biological brain, mapping from structure to function and from function to structure yields dissimilar outcomes, highlighting the likelihood of bias in one-way mapping. To address this issue, we propose a novel bidirectional mapping model, named Bidirectional Mapping with Contrastive Learning (BMCL), to reduce the bias between these two unidirectional mappings via ROI-level contrastive learning. We evaluate our framework on clinical phenotype and neurodegenerative disease predictions using two publicly available datasets (HCP and OASIS). Our results demonstrate the superiority of BMCL compared to several state-of-the-art methods. 

Element isotopes are characterized by distinct atomic masses and nuclear spins, which can significantly influence material properties. Notably, however, isotopes in natural materials are homogenously distributed in space. Here, we propose a method to configure material properties by repositioning isotopes in engineered van der Waals (vdW) isotopic heterostructures. We showcase the properties of hexagonal boron nitride (hBN) isotopic heterostructures in engineering confined photon-lattice waves—hyperbolic phonon polaritons. By varying the composition, stacking order, and thicknesses of h¹⁰BN and h¹¹BN building blocks, hyperbolic phonon polaritons can be engineered into a variety of energy-momentum dispersions. These confined and tailored polaritons are promising for various nanophotonic and thermal functionalities. Due to the universality and importance of isotopes, our vdW isotope heterostructuring method can be applied to engineer the properties of a broad range of materials.

 

We present new radio continuum images and a source catalogue from the MeerKAT survey in the direction of the Small Magellanic Cloud. The observations, at a central frequency of 1.3 GHz across a bandwidth of 0.8 GHz, encompass a field of view ∼7° × 7° and result in images with resolution of 8 arcsec. The median broad-band Stokes I image Root Mean Squared noise value is ∼11 μJy beam−1. The catalogue produced from these images contains 108 330 point sources and 517 compact extended sources. We also describe a UHF (544–1088 MHz) single pointing observation. We report the detection of a new confirmed Supernova Remnant (SNR; MCSNR J0100–7211) with an X-ray magnetar at its centre and 10 new SNR candidates. This is in addition to the detection of 21 previously confirmed SNRs and two previously noted SNR candidates. Our new SNR candidates have typical surface brightness an order of magnitude below those previously known, and on the whole they are larger. The high sensitivity of the MeerKAT survey also enabled us to detect the bright end of the SMC Planetary Nebulae (PNe) sample – point-like radio emission is associated with 38 of 102 optically known PNe, of which 19 are new detections. Lastly, we present the detection of three foreground radio stars amidst 11 circularly polarized sources, and a few examples of morphologically interesting background radio galaxies from which the radio ring galaxy ESO 029–G034 may represent a new type of radio object.

 

This paper examines the learning experiences of undergraduate students who conducted research as part of a multidisciplinary team. The research project involved five undergraduate students with different backgrounds in engineering as well as in arts and sciences, supervised by four architecture and civil engineering faculty and their three PhD students. The research investigates the behavior of new Tessellated Structural-Architectural (TeSA) systems made of repetitive patterns of tiles (tessellations) that are both aesthetically appealing and load bearing. The undergraduate students worked on three tasks: (1) studying the behavior of TeSA shear walls using small scale earthquake simulator tests, (2) studying the shear capacity of reinforced concrete TeSA tiles, and (3) studying the effect of different shapes and interlocking patterns on the performance of small scale TeSA beams. The undergraduate students used hands-on experiments and laboratory testing to study the performance of 3D printed or prefabricated interlocking tessellations. This paper discusses the technical skills, fundamental concepts, and power skills (communicating, writing, presenting, etc.) that the students obtained, as well as the challenges that they encountered. The students found the process of developing and executing hands-on experiments and analyzing experimental results effective for learning new technologies and fundamental concepts. These concepts included 3D printing methods, natural frequency of a structure, and structural response subjected to a shear force. Peer learning, collaboration between students with different backgrounds, and group discussions with all the team members facilitated a deeper understanding and broader perspective on design, performance, and construction of TeSA systems. The project took place during the COVID-19 pandemic, and the students found working and meeting remotely challenging at times. Proper guidance and timely feedback by the project investigators and their PhD students helped with resolving the challenges. 

We study analytically and numerically the minimizers for the Cahn-Hilliard energy functional with a symmetric quartic double-well potential and under a strong anchoring condition(i.e., the Dirichlet condition) on the boundary of an underlying bounded domain. We show a bifurcation phenomenon determined by the boundary value and a parameter that describes the thickness of a transition layer separating two phases of an underlying system of binary mixtures. For the case that the boundary value is exactly the average of the two pure phases, if the bifurcation parameter is larger than or equal to a critical value, then the minimizer is unique and is exactly the homogeneous state. Otherwise, there are exactly two symmetric minimizers. The critical bifurcation value is inversely proportional to the first eigenvalue of the negative Laplace operator with the zero Dirichlet boundary condition. For a boundary value that is larger (or smaller) than that of the average of the two pure phases, the symmetry is broken and there is only one minimizer. We also obtain the bounds and morphological properties of the minimizers under additional assumptions on the domain.Our analysis utilizes the notion of the Nehari manifold and connects it to the eigenvalue problem for the negative Laplacian with the homogeneous boundary condition. We numerically minimize the functional E by solving the gradient-flow equation of E, i.e., the Allen-Cahn equation, with the designated boundary conditions, and with random initial values. We present our numerical simulations and discuss them in the context of our analytical results.