We have studied the magnetic response of transverse optical phonons in Pb1−xSnxTe films. Polarization-dependent terahertz magnetospectroscopy measurements revealed Zeeman splittings and diamagnetic shifts, demonstrati...
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Mechanical motion can break the symmetry in which sound travels in a medium, but significant nonreciprocity is typically achieved only for large motion speeds. We combine moving media with zero-index acoustic propagat...
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Mechanical motion can break the symmetry in which sound travels in a medium, but significant nonreciprocity is typically achieved only for large motion speeds. We combine moving media with zero-index acoustic propagation, yielding extreme nonreciprocity and induced bianisotropy for modest applied speeds. The metamaterial is formed by an array of waveguides loaded by Helmholtz resonators, and it exhibits opposite signs of the refractive index sustained by asymmetric Willis coupling for propagation in opposite directions. We use this response to induce nonreciprocal positive-to-negative sound refraction, and we propose a nonreciprocal metamaterial lens focusing only with excitation from one side based on asymmetric Willis coupling.
The mechanism of THz generation in ferromagnet/metal (F/M) bilayers has been typically ascribed to the inverse spin Hall effect (ISHE). Here, we fabricated Pt/Fe/Cr/Fe/Pt multilayers containing two back-to-back spintr...
The mechanism of THz generation in ferromagnet/metal (F/M) bilayers has been typically ascribed to the inverse spin Hall effect (ISHE). Here, we fabricated Pt/Fe/Cr/Fe/Pt multilayers containing two back-to-back spintronic THz emitters separated by a thin (tCr≤ 3nm) wedge-shaped Cr spacer. In such an arrangement, magnetization alignment of the two Fe films can be controlled by the interplay between Cr-mediated interlayer exchange coupling (IEC) and an external magnetic field. This in turn results in a strong variation of the THz amplitudeA, withA↑↓reaching up to 14 timesA↑↑(arrows indicate the relative alignment of the magnetization of the two magnetic layers). This observed functionality is ascribed to the interference of THz transients generated by two closely spaced THz emitters. Moreover, the magnetic field dependenceA(H) shows a strong asymmetry that points to an additional performance modulation of the THz emitter via IEC and multilayer design.
This work presents a preliminary study of a LMR-based gas sensor. Results of the device subjected to annealing process show a stable and repetitive response that is required for the utilization in gas sensing applicat...
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Cryptography is the science and art of maintaining the security of messages when messages are sent from one place to another. One of the ways securing the form of text message information is by the encryption process ...
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Background Brain microstructure, as measured by diffusion MR imaging (dMRI), is an early biomarker of Alzheimer’s disease (AD) and has been previously demonstrated to predict whole brain atrophy and cognition. The ne...
Background Brain microstructure, as measured by diffusion MR imaging (dMRI), is an early biomarker of Alzheimer’s disease (AD) and has been previously demonstrated to predict whole brain atrophy and cognition. The network-based neurodegeneration hypothesis posits that the onset of AD occurs within key vulnerable regions, whose network connectivity guides the spread of atrophy into new regions. Graph convolutional network (GCN) models can mimic this spread through its convolution process. Here we present a GCN-based model to predict future individual nodal atrophy and disease progression across the AD spectrum using structural connectome. Method We analyzed 371 participants with baseline dMRI scans (154 cognitively normal (CN), 161 mild cognitive impairment (MCI), 56 AD) and longitudinal T1 scans (2.7±1.2 years) from the ADNI study. To predict annual nodal atrophy rates (126 nodes) from baseline structural connectivity (SC), we constructed a two-layer GCN followed by multilayer perceptron (GCN-MLP). Five-fold nested cross-validation and Optuna were used for hyperparameter tuning. We applied the trained GCN-MLP model to predict future disease progression via transfer learning. GNNExplainer was used to identify the predictive SC features for atrophy or cognition. The GCN-MLP model was validated in another independent dataset from Singapore (MACC) with 226 participants (61 CN, 112 MCI, 53 AD, follow-up period of T1 scans (3.1±1.3 years)) using transfer learning. Result The proposed GCN-MLP model predicted the annual rate of regional atrophy at the individual level in the main (R = 0.5407±0.0480; MAE = 0.0013±0.0001; EV = 0.2930±0.0516) and validation (R = 0.5323±0.0564; MAE = 0.0009±0.0001; EV = 0.2808±0.0559) datasets (Table 1; Figure 1). Graph edge SC of the target network contributed most to the atrophy prediction of its own network (Figure 2A&C). Nodal feature SC in hippocampus, striatum, and temporal network were major predictors of atrophy progression across all 1
Low-symmetry Penta-PdPSe with intrinsic in-plane anisotropy synthesized successfully [P. Li et al., Adv. Mater., 2102541 (2021)]. Motivated by this experimental discovery, we investigate the structural, mechanical, el...
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The use of instant messaging (IM) application is very communal among smartphone users, as it delivers ease in interaction and communication. The speed at which technologies, growth, and expansion of networks infrastru...
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Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull dis...
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