Advancements in brain-machine interfaces for application in the metaverse

In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audio-visual media interaction has become an urgent issue. The use of brain-machine interfaces for sensory simulation is one of the proposed solutions. Currently, brain-machine interfaces have demonstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scenarios: generative art in the metaverse, serious gaming for healthcare in metaverse medicine, and brain-machine interface applications for facial expression synthesis in the virtual society of the metaverse. It investigates existing commercial products and patents (such as MindWave Mobile, GVS, and Galea), draws analogies with the development processes of network security and neurosecurity, bioethics and neuroethics, and discusses the challenges and potential issues that may arise when brain-machine interfaces mature and are widely applied. Furthermore, it looks ahead to the diverse possibilities of deep and varied applications of brain-machine interfaces in the metaverse in the future

The effect of dopant and optical micro-cavity on the photoluminescence of Mn-doped ZnSe nanobelts

Pure and Mn-doped ZnSe nanobelts were synthesized by a convenient thermal evaporation method. Scanning electron microscopy, X-ray powder diffraction, energy dispersive X-ray spectroscopy and corresponding element mapping, and transmission electron microscope were used to examine the morphology, phase structure, crystallinity, composition, and growth direction of as-prepared nanobelts. Raman spectra were used to confirm the effective doping of Mn(2+) into ZnSe nanobelts. Micro-photoluminescence (PL) spectra were used to investigate the emission property of as-prepared samples. A dominant trapped-state emission band is observed in single ZnSe(Mn) nanobelt. However, we cannot observe the transition emission of Mn ion in this ZnSe(Mn) nanobelt, which confirm that Mn powder act as poor dopant. There are weak near-bandgap emission and strong (4)T(1) → (6)A(1) transition emission of Mn(2+) in single [Formula: see text] and [Formula: see text] nanobelt. More interesting, the (4)T(1) → (6)A(1) transition emission in [Formula: see text] nanobelt split into multi-bands. PL mapping of individual splitted sub-bands were carried out to explore the origin of multi-bands. These doped nanobelts with novel multi-bands emission can find application in frequency convertor and wavelength-tunable light emission devices

Topological Vortices in Chiral Gauge Theory of Graphene

Generation mechanism of energy gaps between conductance and valence bands is at the centre of the study of graphene material. Recently Chamon, Jackiw, et al. proposed a mechanism of using a Kekul\'{e} distortion background field $\varphi$ and its induced gauge potential $A_{i}$ to generate energy gaps. In this paper various vortex structures inhering in this model are studied. Regarding $\varphi$ as a generic background field rather than a fixed Nielson-Oleson type distribution, we have found two new types of vortices on the graphene surface --- the velocity field vortices and the monopole-motion induced vortices --- from the inner structure of the potential $A_{i}$. These vortex structures naturally arise from the motion of the Dirac fermions instead of from the background distortion field.Comment: 12 page

A multiobjective single bus corridor scheduling using machine learning-based predictive models

Many real-life optimisation problems, including those in production and logistics, have uncertainties that pose considerable challenges for practitioners. In spite of considerable efforts, the current methods are still not satisfactory. This is primarily caused by a lack of effective methods to deal with various uncertainties. Existing literature comes from two isolated research communities, namely the operations research community and the machine learning community. In the operations research community, uncertainties are often modelled and solved through techniques like stochastic programming or robust optimisation, which are often criticised for their over conservativeness. In the machine learning community, the problem is formulated as a dynamic control problem and solved through techniques like supervised learning and/or reinforcement learning, which could suffer from being myopic and unstable. In this paper, we aim to fill this research gap and develop a novel framework that takes advantages of both short-term accuracy from mathematical models and high-quality future forecasts from machine learning modules. We demonstrate the practicality and feasibility of our approach for a real-life bus scheduling problem and two controlled bus scheduling instances that are generated artificially. To our knowledge, the proposed framework represents the first multi-objective bus-headway-optimisation method for non-timetabled bus schedule with major practical constraints being considered. The advantages of our proposed methods are also discussed, along with factors that need to be carefully considered for practical applications. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group

Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network

Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with uneven illumination or the fundus image with disease interference. In this paper, we present a spatial and scale uncertainty-aware network (SSU-Net) that fully uses both spatial and scale uncertainty to highlight ambiguous regions and integrate hierarchical structure contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps using Monte Carlo dropout to approximate Bayesian networks. Based on these spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA) module to guide the model to focus on ambiguous regions. Second, we extract the uncertainty under different scales and propose the multi-scale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction. Finally, we visualize the uncertainty map of final prediction, providing interpretability for segmentation results. Experiment results show that the SSU-Net performs best on cornea endothelial cell and retinal vessel segmentation tasks. Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more accurate and robust

Generation and Characterization of Novel Human IRAS Monoclonal Antibodies

Imidazoline receptors were first proposed by Bousquet et al., when they studied antihypertensive effect of clonidine. A strong candidate for I1R, known as imidazoline receptor antisera-selected protein (IRAS), has been cloned from human hippocampus. We reported that IRAS mediated agmatine-induced inhibition of opioid dependence in morphine-dependent cells. To elucidate the functional and structure properties of I1R, we developed the newly monoclonal antibody against the N-terminal hIRAS region including the PX domain (10–120aa) through immunization of BALB/c mice with the NusA-IRAS fusion protein containing an IRAS N-terminal (10–120aa). Stable hybridoma cell lines were established and monoclonal antibodies specifically recognized full-length IRAS proteins in their native state by immunoblotting and immunoprecipitation. Monoclonal antibodies stained in a predominantly punctate cytoplasmic pattern when applied to IRAS-transfected HEK293 cells by indirect immunofluorescence assays and demonstrated excellent reactivity in flow immunocytometry. These monoclonal antibodies will provide powerful reagents for the further investigation of hIRAS protein functions

Design, Fabrication, and Characterization of a Bifrequency Colinear Array

Ultrasound imaging with high resolution and large penetration depth has been increasingly adopted in medical diagnosis, surgery guidance, and treatment assessment. Conventional ultrasound works at a particular frequency, with a −6 dB fractional bandwidth of ~70 %, limiting the imaging resolution or depth of field. In this paper, a bi-frequency co-linear array with resonant frequencies of 8 MHz and 20 MHz was investigated to meet the requirements of resolution and penetration depth for a broad range of ultrasound imaging applications. Specifically, a 32-element bi-frequency co-linear array was designed and fabricated, followed by element characterization and real-time sectorial scan (S-scan) phantom imaging using a Verasonics system. The bi-frequency co-linear array was tested in four different modes by switching between low and high frequencies on transmit and receive. The four modes included the following: (1) transmit low, receive low, (2) transmit low, receive high, (3) transmit high, receive low, (4) transmit high, receive high. After testing, the axial and lateral resolutions of all modes were calculated and compared. The results of this study suggest that bi-frequency co-linear arrays are potential aids for wideband fundamental imaging and harmonic/sub-harmonic imaging