Brillouin Klein space and half-turn space in three-dimensional acoustic crystals.

The Bloch band theory and Brillouin zone (BZ) that characterize wave-like behaviors in periodic mediums are two cornerstones of contemporary physics, ranging from condensed matter to topological physics. Recent theoretical breakthrough revealed that, under the projective symmetry algebra enforced by artificial gauge fields, the usual two-dimensional (2D) BZ (orientable Brillouin two-torus) can be fundamentally modified to a non-orientable Brillouin Klein bottle with radically distinct manifold topology. However, the physical consequence of artificial gauge fields on the more general three-dimensional (3D) BZ (orientable Brillouin three-torus) was so far missing. Here, we theoretically discovered and experimentally observed that the fundamental domain and topology of the usual 3D BZ can be reduced to a non-orientable Brillouin Klein space or an orientable Brillouin half-turn space in a 3D acoustic crystal with artificial gauge fields. We experimentally identify peculiar 3D momentum-space non-symmorphic screw rotation and glide reflection symmetries in the measured band structures. Moreover, we experimentally demonstrate a novel stacked weak Klein bottle insulator featuring a nonzero Z2 topological invariant and self-collimated topological surface states at two opposite surfaces related by a nonlocal twist, radically distinct from all previous 3D topological insulators. Our discovery not only fundamentally modifies the fundamental domain and topology of 3D BZ, but also opens the door towards a wealth of previously overlooked momentum-space multidimensional manifold topologies and novel gauge-symmetry-enriched topological physics and robust acoustic wave manipulations beyond the existing paradigms.

Investigation of singlet oxygen and superoxide radical produced from vortex-based hydrodynamic cavitation: Mechanism and its relation to cavitation intensity.

Presently, the hydroxyl radical oxidation mechanism is widely acknowledged for the degradation of organic pollutants based on hydrodynamic cavitation technology. The presence and production mechanism of other potential reactive oxygen species (ROS) in the cavitation systems are still unclear. In this paper, singlet oxygen (1O2) and superoxide radical (·O2-) were selected as the target ROS, and their generation rules and mechanism in vortex-based hydrodynamic cavitation (VBHC) were analyzed. Computational fluid dynamics (CFD) were used to simulate and analyze the intensity characteristics of VBHC, and the relationship between the generation of ROS and cavitation intensity was thoroughly revealed. The results show that the operating conditions of the device have a significant and complicated influence on the generation of 1O2 and ·O2-. When the inlet pressure reaches to 4.5 bar, it is more favorable for the generation of 1O2 and ·O2- comparing with those lower pressure. However, higher temperature (45 °C) and aeration rate (15 (L/min)/L) do not always have positive effect on the 1O2 and ·O2- productions, and their optimal parameters need to be analyzed in combination with the inlet pressure. Through quenching experiments, it is found that 1O2 is completely transformed from ·O2-, and ·O2- comes from the transformation of hydroxyl radicals and dissolved oxygen. Higher cavitation intensity is captured and shown more disperse in the vortex cavitation region, which is consistent with the larger production and stronger diffusion of 1O2 and ·O2-. This paper shed light to the generation mechanism of 1O2 and ·O2- in VBHC reactors and the relationship with cavitation intensity. The conclusion provides new ideas for the research of effective ROS in hydrodynamic cavitation process.

Transformer Based Cross-Subject Mental Workload Classification Using FNIRS for Real-World Application.

Mental state monitoring is a hot topic especially in neurorehabilitation, skill training, etc, for which the functional near-infrared spectroscopy (fNIRS) has been suggested to be used, and fewer detection channels and cross-subject performance are usually required for real-world application. To this goal, we propose a transformer-based method for cross-subject mental workload classification using fewer channels of fNIRS. Firstly, the input fNIRS signals in a window are divided into patches in the temporal order and transformed into embeddings, to which a classification token and learnable position embeddings are added. Then, a transformer encoder is used to learn the long-range dependencies among the embeddings, of which the output classification token is sent to a multilayer perceptron (MLP) head. Mental workload classification results can be represented by the outputs of the MLP head. Finally, comparison experiments were conducted on the open-access fNIRS2MW dataset. The results show that, the proposed method can outperform previous methods in cross-subject classification accuracy, and relatively efficient computation can be obtained.

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection.

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multi-dimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.