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Objective: Berberine (BBR) has emerged as a promising therapeutic agent for nonalcoholic fatty liver disease (NAFLD). This study aims to elucidate the underlying molecular mechanisms. Methods: In this study, db/dbmice were chosen as an animal model for NAFLD. A total of 10 healthy C57BL/6J mice and 30 db/dbmice were randomly allocated to one of 4 groups: the normal control (NC) group, the diabetic control (DC) group, the Metformin (MET) therapy group, and the BBR therapy group. The total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels in the serum were measured. The glutathione peroxidase (GSH-Px), glutathione (GSH), malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), interleukin (IL)-1β, tumor necrosis factor (TNF)-α and monocyte chemotactic protein 1 (MCP-1) levels in liver tissue were measured. Hematoxylin and eosin (H&E), acid-Schiff (PAS) and TUNEL stanning was performed for histopathological analysis. Western blotting and immunohistochemistry were conducted to detect the expression levels of key proteins in the AMPK/SIRT1 pathway. Results: BBR could improve lipid metabolism, attenuate hepatic steatosis and alleviate liver injury significantly. The excessive oxidative stress, high levels of inflammation and abnormal apoptosis in db/dbmice were reversed after BBR intervention. BBR clearly changed the expression of AMP-activated protein kinase (AMPK)/Sirtuin 1 (SIRT1), and their downstream proteins. Conclusion: BBR could reverse NAFLD-related liver injury, likely by activating the AMPK/SIRT1 signaling pathway to inhibit oxidative stress, inflammation and apoptosis in hepatic tissue.

• A novel process named as liquid metal assistant self-propagating high-temperature synthesis (LMA-SHS) has been developed, which shows very short reaction time, low energy consumption, high yield, and low cost. • High-purity S-containing high-entropy MAX-phase materials have been successfully synthesized via LMA-SHS. • Liquid metal (Sn or In) acts as a binder among transition metal atoms by generating negative mixing enthalpy, and facilitates mass and heat transfer during the LMA-SHS process. Due to their high-entropy effects, the high-entropy (HE) MAX-phase materials improve the comprehensive performance of MAX phases, opening up more possibilities for practical engineering applications. However, it is still challenging to obtain S-containing high-entropy MAX phases because of the high volatilization behavior of sulfur, suffering from issues such as high reaction temperature and long reaction time of traditional synthesis methods. This paper proposes a novel process named as liquid metal assistant self-propagating high-temperature synthesis (LMA-SHS) for efficient synthesis of high-purity S-containing high-entropy MAX-phase materials. Low-melting-point metal (Sn or In) has been introduced into the raw mixture and melted into a liquid phase during the early stage of the SHS reaction. By serving as a "binder" between transition metal atoms of the M-site due to the negative mixing enthalpy, this liquid phase can accelerate mass and heat transfer during the SHS process, ensuring a uniform solid solution of each element and realizing the synthesis of high-purity (TiNbVZr) 2 SC in an extremely short time. The synthesis method for high-entropy MAX-phase materials developed in this study, i.e., LMA-SHS, showing very short reaction time, low energy consumption, high yield, and low cost, has the promise to be a general energy- and resource-efficient route towards high-purity HE materials. Two Synthesis Methods (SHS, LMA-SHS) and XRD patterns of S-containing high-entropy MAX-phase materials. [Display omitted] [ABSTRACT FROM AUTHOR]

Ocean waves and Stokes drift are generated by typhoons. This study investigated the characteristics of ocean waves and wave-induced Stokes drift and their effects during Typhoon Mangkhut using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 datasets and observational data. The results revealed that the typhoon generated intense cyclones and huge typhoon waves with a maximum wind speed of 45 m/s, a minimum pressure of 955 hPa, and a maximum significant wave height of 12 m. The Stokes drift caused by typhoon waves exceeded 0.6 m/s, the Stokes depth scale exceeded 18 m, and the maximum Stokes transport reached 6 m2/s. The spatial distribution of 10-m wind speed, typhoon wave height, Stokes drift, Stokes depth, and Stokes transport during the typhoon was highly correlated with the typhoon track. The distribution along the typhoon track showed significant zonal asymmetry, with greater intensity on the right side of the typhoon track than on the left side. These findings provide important insights into the impact of typhoons on ocean waves and Stokes drift, thus improving our understanding of the interactions between typhoons and the ocean environment. This study also investigated the contribution of Stokes transport to the total net transport during typhoons using Ekman-Stokes Numbers as a comparative measure. The results indicated that the ratio of Stokes transport to the total net transport reached up to 50% within the typhoon radius, while it was approximately 30% outside the radius. Strong Stokes transport induced by typhoon waves led to divergence in the transport direction, which resulted in upwelling of the lower ocean as a compensation current. Thus, Stokes transport played a crucial role in the vertical mixing of the ocean during typhoons. The findings suggested that Stokes transport should be paid more attention to, particularly in high latitude ocean regions, where strong winds can amplify its effects.

This work studied the activation of the twins in a commercial cast AZ31 Mg alloy by deep cryogenic treatment (DCT) using in-situ EBSD. The initial as-cast microstructure has a range of grain sizes. The DCT mainly activated twins in the areas of fine grain size, in which the twin area fraction increased with increasing DCT time. These twins were {10–12} extension twins, which were evoked and facilitated by the large tensile interior stress that was caused by the large temperature difference during the DCT. Furthermore, only {10–12} extension twins were activated. The activated {10–12} extension twins had the highest Schmid factor and conformed to Schmidt's law. One or two other kinds of tensile twinning variants of the six tensile twinning variants (V1–V6) were activated by the DCT. These activated variants had the highest Schmid factor and the lowest orientation difference (less than 10o), while those non-activated variants had an orientation difference of ∼60o. Theoretical evaluation indicated that these two kinds of tensile twinning variants were the para-position variant rather than the ortho-position variant or the meta-position variant.

A novel layered perovskite tantalate phosphor Sr3LaTa3O12:Sm3+(SLTO:Sm3+) with orange-red emission was obtained for the first time via the solid-phase synthetic method. The phase purity, surface morphology, element distribution and luminescent properties of the SLTO:xSm3+(x = 0.01 mol%–0.30 mol%) phosphors were investigated. Under 408 nm excitation, the optimum doped SLTO:0.10Sm3+phosphor emits orange-red light at 598 nm with the highest emission peak (4G5/2 → 6H7/2). The critical energy transfer distance is 1.687 nm. The prepared SLTO:0.10Sm3+phosphor has excellent thermal stability with temperature quenching temperature (T0.5 > 500 K) and high activation energy (Ea = 0.25 eV). Through calculation, the chromaticity coordinates of all samples are concentrated in the orange-red area, and the color purity reaches 99%. The fabricated white light-emitting diode (w-LED) has a good correlated color temperature (5132 K), a high Ra(89), and the CIE chromaticity coordinates (0.340, 0.327). Consequently, the superiority of orange-red-emitting tantalate SLTO:Sm3+phosphors for w-LEDs is demonstrated.

The hippocampus provides significant inspiration for spatial navigation and memory in both humans and animals. Constructing large-scale spiking neural network (SNN) models based on the biological neural systems is an important approach to comprehend the computational principles and cognitive function of the hippocampus. Such models are usually implemented on neuromorphic computing platforms, which often have limited computing resources that constrain the achievable scale of the network. This work introduces a series of digital design methods to realize a Field-Programmable Gate Array (FPGA) friendly SNN model. The methods include FPGA-friendly nonlinear calculation modules and a fixed-point design algorithm. A brain-inspired large-scale SNN of ∼21 k place cells for path planning is mapped on FPGA. The results show that the path planning tasks in different environments are finished in real-time and the firing activities of place cells are successfully reproduced. With these methods, the achievable network size on one FPGA chip is increased by 1595 times with higher resource usage efficiency and faster computation speed compared to the state-of-the-art.

In the terahertz (THz) band, due to the non-ignorable surface roughness, which is comparable to the wavelength, the scaled-radar cross section (RCS) measurement faces new challenges, which is referred to as the inconsistency with the electrodynamics similitude. As a solution, an RCS scaled-measurement and inversion method for rough metal objects is proposed based on the distribution of surface elements. With the consideration of the random fluctuation, the distribution of the facets was first studied, and a criterion was provided for quantitatively evaluating the difference in their distribution using Kullback–Leibler divergence (KL divergence). The core idea of our method is to construct an RCS estimation system of linear equations consisting of weight factors solved by relevant parameters of prior models. Both simulations and measurements on different scaled rough plates were conducted, demonstrating the effectiveness of the proposed method. Besides providing an effective solution to the scaled-RCS measurement for rough objects in the THz band, this work also introduces a new aspect in studying and estimating the scattering characteristics of rough surfaces.

Brain-inspired structured neural circuits are the cornerstones of both computational and perceived intelligence. Real-time simulations of large-scale high-dimensional neural populations with complex nonlinearities pose a significant challenge. Taking advantage of distributed computations using embedded multi-cores, we propose an ARM-based scalable multi-hierarchy parallel computing platform (EmPaas) for neural population simulations. EmPaas is constructed using 340 ARM Cortex-M4 microprocessors to achieve high-speed and high-accuracy parallel computing. The tree-two-dimensional grid-like hybrid topology completes the overall construction, reducing communication strain and power consumption. As an instance of embedded computing, the optimized model for a biologically plausible basal ganglia-thalamus (BG-TH) network is deployed into this platform to verify the performance. At an operating frequency of 168 MHz, the BG-TH network consisting of 4000 Izhikevich neurons is simulated in the platform for 3000 ms with a power consumption of 56.565 mW per core and an actual time of 2748.57 ms, which shows the parallel computing approach significantly improves computational efficiency. EmPaas can meet the requirement of real-time performance with the maximum amount of 2000 Izhikevich neurons loaded in each Extended Community Unit (ECUnit), which provides a new practical method for research in large-scale brain network simulation and brain-inspired computing.

Detecting rotated faces has always been a challenging task. Fixed convolutional kernels struggle to effectively match features after rotation, while the sampling point offsets of deformable convolutions are limited by complex backgrounds. To address this issue, we propose a guided deformable attention (GDA) network. Guiding the offset direction of sampling points by adding constraints of facial structure to deformable convolutions. The GDA network adopts a dual-stream structure, with one branch detecting the inherent structural information for preliminary positioning of the face area; then, the second branch uses deformable convolution to perform pixel-level feature extraction on the face within the range. In addition, we introduce a novel loss, which, during the guidance process, aligns the activation areas in the feature maps extracted by the two branches through the KL divergence. Extensive experimental results validate that GDA network performs excellently on multiple face detection datasets, surpassing the current state-of-the-art face detection methods.

Array-based millimeter wave (MMW) holographic imaging technology has shown great application potential in nondestructive evaluation (NDE) of dielectric materials (such as composites). With the development of high-precision motion capture systems, the NDE of the internal structure of layered dielectric targets by handheld radar has become a new demand growth point. Since the elements of the handheld-radar-based array are randomly distributed in the 3-D space, the existing fast-imaging algorithms are difficult to be directly applied, which will restrict the development of this application. For this reason, this article proposes a fast-imaging algorithm for layered dielectric targets based on spatial 3-D nonuniform single-input single-output (SISO) array radar. The algorithm takes each element of the spatial 3-D random array as the center, expands it into a virtual uniform plane array, then transforms the echo data of the virtual array from the spatial domain to the wavenumber domain by using fast Fourier transform (FFT). After phase compensation, the wavenumber-domain data of all virtual arrays are added, to obtain the wavenumber-domain scattered echo data of the entire 3-D nonuniform SISO array radar. Finally, the imaging result of the target is obtained through the 2-D inverse Fourier transform (IFT) in azimuth direction and the segmented IFT in range direction. Numerical simulation and experimental measurements show that the proposed algorithm can significantly improve the imaging efficiency compared with the improved back-projection (IBP) algorithm which is also suitable for this scenario.

Terahertz (THz) inverse synthetic aperture radar (ISAR) imaging of space targets has the advantage of high resolution and all-day capability for spatial situational awareness (SSA). However, compared with microwave ISAR imaging, microvibrations of the radar platform and target can cause image defocusing and quality degradation in the THz band. Commonly, the existing algorithms considered vibration as simple harmonic motion in a rigid body and estimated the vibration parameters for phase error compensation. Nevertheless, these methods are dependent on the prominent points and cannot focus on the complex nonrigid vibration of the space target. Aiming at these problems, an ISAR imaging framework that can be leveraged for complex nonrigid vibration compensation is proposed. First, a compressed sensing (CS) model is established to compensate for the vibration phase error in ISAR imaging. Then, the alternating direction method of multipliers (ADMMs) is employed to optimize the CS model. Subsequently, considering that strong scattering points of space targets will cover up the weak scattering information in ISAR images, a sidelobe suppression constraint is appended. Thus, the weak scattering information is retained while removing the sidelobe and noise. It is worth mentioning that the ISAR imaging algorithm is derived to a 2-D matrix form to reduce memory usage and improve computational efficiency. Finally, simulated and measured data are utilized to verify the superiority of the proposed algorithm and prove the potential of ISAR imaging for practical space targets.

Hyperspectral images (HSIs) offer detailed and abundant spectral-spatial information, holding great potential for ground object change detection (CD). Currently, numerous deep learning-based hyperspectral CD (HCD) models have been studied. However, these methods only consider single difference information or temporal information while neglecting the complementary advantage between difference and temporal information. To solve this issue, this work proposes a multitemporal symmetric fusion network (MTSFNet) for HCD, which involves three steps. First, the difference and temporal data are calculated by a subtraction operation and concatenation operation. Then, a dual-branch convolutional network is developed to capture the difference and temporal features. Finally, a symmetric feature fusion scheme is designed to integrate the extracted features followed by a fully connected layer to derive the detection results. Experiments conducted on multiple well-known datasets reveal that the proposed MTSFNet outperforms other advanced CD approaches in terms of qualitative and quantitative results.

The high-resolution inverse synthetic aperture radar (ISAR) imaging of space target holds tremendous significance for recognition and on-orbit maintenance. The terahertz (THz) radar possesses natural superiority in terms of large bandwidth and short wavelength, enabling it to achieve higher resolution both in range and azimuth directions. However, the range cell migration (RCM) induced by rotation is severe in the THz band, even in small rotation angle. In order to achieve high-accuracy RCM correction, an imaging algorithm based on keystone transform (KT) and fast Gaussian gridding nonuniform fast Fourier transform (FGG NUFFT) is presented in this article. Nevertheless, to correct the second-order RCM and compensate the spatial-variant (SV) phase error, the rotation parameters of uncooperative target need to be estimated first. Moreover, by analyzing the ISAR images of space targets, it is found that the scattering intensity of certain components on the satellite body, such as the cavity structure of the antenna, is stronger than that of the solar panels. Considering scattering intensity of these components, the traditional estimation method under global image quality criteria may fail. To prevent getting trapped in a local optimum or finding an incorrect solution due to the influence of strong scattering points, a parameter estimation framework based on prior knowledge added sparrow search algorithm (PKSSA) under local maximum contrast criterion (LMCC) is brought forward. The proposed novel algorithm can not only fast estimate rotation parameters without being affected by strong scattering points but also obtain fine imaging results. Numerous electromagnetic simulation and real measured THz experiment results prove the correctness and effectiveness of the proposed algorithm.

Airborne terahertz video synthetic aperture radar (THz-ViSAR) exhibits excellent superiorities in dynamic situation awareness and moving target detection. However, limited by the recording precision of the inertial navigation system (INS) or global positioning system (GPS), data-driven motion compensation (MOCO) methods are challenging to eliminate motion errors for the THz band. In particular, platform vibrations that are previously negligible in the microwave band become nonnegligible, drastically degrading the focusing performance. To conquer the aforementioned problems, this article proposes an echo-driven robust MOCO framework suited for THz-ViSAR. First, the airborne motion error model (MED) is rederived and the corresponding effects are analyzed in detail. Based on the derived MED, two parts are required to estimate from the echo: radial errors and vibration errors. Thus, a forward estimation of candidate signal of interests (SoI) is proposed based on local peaks of the average range profile and the prominence. For each candidate, radial and vibration errors are estimated from the unwrapped phase of the SoI via the polynomial and multicomponent sinusoidal fitting in turn. Finally, MOCO functions are constructed and evaluated based on the image entropy and image contrast, which determines the optimal SoI. Airborne field experiments are carried out to validate the effectiveness and practicability of the proposed method.

Rotating targets/components often carry beneficial information for identifying high-value targets in video synthetic aperture radar (ViSAR). However, unlike linear motion models, rotating motion models involve more parameters to be estimated, rendering parametric-based methods computationally intensive. Besides, such micro-motion features are relatively insignificant in the microwave band. To conquer the aforementioned problems, this article proposes the nonparametric indication and imaging of rotating targets under terahertz (THz) ViSAR. Taking rotating corner reflectors (RCRs) as the object, the Doppler prior and imaging characteristics (DPIC) are first determined and regarded as the theoretical basis. Combining DPIC and Doppler sensitivity of THz band, RCRs can be easily indicated by the nonparametric time–frequency transform. Thus, range cells of RCRs can be deduced intuitively by the visual saliency, followed by the separation of the residual background and defocused target area. After clutter suppression, two signal of interests (SoIs) of RCRs are extracted from salient points, where unwrapped phases and azimuth spectrums are utilized for fast estimation of phase errors and additional Doppler centroids (DCs), respectively. Finally, the refocused target image is stitched into the residual background. To validate the effectiveness of the proposed method, the airborne field experiment of RCRs is conducted in THz-band for the first time. Moreover, the anti-noise ability, relocation capability, and applicable speed interval of the proposed method are demonstrated, along with its extended application potential in terms of the ground moving target imaging.

As the demand for precise detection of space targets and orbital maintenance increases, it is crucial to obtain high-resolution images of space targets. The terahertz (THz) radar with carrier frequency of 0.1–10 THz has the characteristics of larger bandwidth and shorter wavelength compared with microwave radar. Accordingly, under the same inverse synthetic aperture radar (ISAR) imaging conditions, it has advantages in both range resolution and azimuth resolution theoretically. However, for large-size space targets, the phenomenon of high-order range cell migration (RCM) and azimuth phase error are prone to occur during small-rotation-angle (3°–5°) ISAR imaging, which results in severe imagery defocusing. In this article, a high-resolution ISAR imaging method based on defocused point spread function (DPSF) is proposed. By means of third-order Taylor expansion performing on the range-Doppler (RD) imaging model, the impact of high-order terms on imaging is analyzed. Through revealing the relationship between high-order terms and inclination of defocused PSF measured with Hough transform (HT), the rotation velocity and range center deviation (RCD) are estimated accurately and independently in image domain. After parameters estimation, the first-order RCM is corrected by Keystone transform (KT). Utilizing the estimated rotation velocity, the second-order RCM is eliminated by fast Gaussian gridding nonuniform fast Fourier transform (FGG NUFFT). Then the spatial-variant phase error is compensated based on previous estimation. Finally, azimuth resolution is improved by FGG NUFFT due to nonuniformly sampled data. Numerous simulations and real measured experimental results demonstrate the effectiveness and outperformance of the proposed algorithm for large-size space target ISAR imaging in the THz band.

Short-range millimeter-wave (MMW) multiple-input multiple-output synthetic aperture radar (MIMO-SAR) imaging technology has great development opportunities in the areas of security monitoring, medical diagnosis, and nondestructive evaluation. However, the existing MIMO-SAR fast imaging algorithms typically employ fast Fourier transform (FFT) to invert the target image, which has a serious influence on imaging quality under the highly displayed dynamic range. To address this deficiency, this article presents an adaptive alternating direction method of multipliers (ADMM)-based method on the basis of the state-of-the-art MIMO-SAR fast image-solving model. Different from the traditional ADMM, the proposed algorithm avoids the multiplication and inversion of a large-scale sensing matrix, and the computation is significantly reduced. In addition, adaptive weighting is introduced to adjust the intensity of the ${\ell _{1}}$ regularization item, with the objective of mitigating the loss in imaging accuracy caused by the state-of-the-art fast algorithm. On this foundation, considering the sensitivity of the optimized regularization coefficient to different targets and MIMO array configurations, an efficient optimized regularization coefficient searching method is developed, which greatly enhances the accomplishment of high-quality target image reconstruction in different scenarios as quickly as possible. Numerical simulations and experimental results validate the effectiveness of the proposed algorithm compared with those of the existing FFT-based algorithms.

Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.

The COVID-19 pandemic continues to spread worldwide. To protect and control the spread of SARS-CoV-2, varieties of subunit vaccines based on spike (S) proteins have been approved for human applications. Here, we report a new subunit vaccine design strategy that functions as both an antigen carrier and an adjuvant in immunization to elicit high-level immune responses. The complex of 2-hydroxypropyl-trimethylammonium chloride chitosan and amylose entangles Au nanoparticles (HTCC/amylose/AuNPs) forming 40 nm nanocarriers with a positive charge. The obtained positively charged nanoparticles reveal many merits, including the larger S protein loading capacity in PBS buffer, higher cellular uptake ability, and lower cell cytotoxicity, supporting their potential as safe vaccine nanocarriers. Two functionalized nanoparticle subunit vaccines are prepared via loading full-length S proteins derived from SARS-CoV-2 variants. In mice, both prepared vaccines elicit high specific IgG antibodies, neutralize antibodies, and immunoglobulin IgG1 and IgG2a. The prepared vaccines also elicit robust T- and B-cell immune responses and increase CD19+B cells, CD11C+dendritic cells, and CD11B+macrophages at the alveoli and bronchi of the immunized mice. Furthermore, the results of skin safety tests and histological observation of organs indicated in vivo safety of HTCC/amylose/AuNP-based vaccines. Summarily, our prepared HTCC/amylose/AuNP have significant potential as general vaccine carriers for the delivery of different antigens with potent immune stimulation.

The information in Spiking Neural Networks (SNNs) is carried by discrete spikes. Therefore, the conversion between the spiking signals and real-value signals has an important impact on the encoding efficiency and performance of SNNs, which is usually completed by spike encoding algorithms. In order to select suitable spike encoding algorithms for different SNNs, this work evaluates four commonly used spike encoding algorithms. The evaluation is based on the FPGA implementation results of the algorithms, including calculation speed, resource consumption, accuracy, and anti-noiseability, so as to better adapt to the neuromorphic implementation of SNN. Two real-world applicaitons are also used to verify the evaluation results. By analyzing and comparing the evaluation results, this work summarizes the characteristics and application range of different algorithms. In general, the sliding window algorithm has relatively low accuracy and is suitable for observing signal trends. Pulsewidth modulated-Based algorithm and step-forward algorithm are suitable for accurate reconstruction of various signals except for square wave signals, while Ben's Spiker algorithm can remedy this. Finally, a scoring method that can be used for spiking coding algorithm selection is proposed, which can help to improve the encoding efficiency of neuromorphic SNNs.

In this letter, an efficient near-field millimeter-wave (MMW) imaging algorithm is presented for layered dielectrics based on multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. First, the model of target echo under MIMO-SAR configuration is built by dyadic Green's function, and the precise spectral echo expression is obtained through fast Fourier transform (FFT) and spherical-wave decomposition. Then, the image reconstruction of sparse MIMO-SAR is divided into separate single-input–multiple-output (SIMO)-SAR imaging problems, and the target's subimage corresponding to different SIMO-SAR setup can be quickly solved by employing inverse FFT, multistep phase compensation and wavenumber integration. Finally, the fully focused MIMO-SAR reconstructed image will be obtained by coherently accumulating all the SIMO-SAR results. Simulation analysis and experimental results validate the effectiveness the proposed algorithm.

High-temperature superconducting (HTS) pinning maglev has attracted attention due to its advantages of stable levitation without external energy, and integration of levitation and guidance. As one of the main excitation sources of the HTS pinning maglev system, permanent magnet guideway (PMG) irregularity is affected by the geometric and magnetism of PMG together. Therefore, the measurement method only for geometry in wheel-rail systems no longer meets the measurement requirements of PMG irregularity. In addition, the current research on HTS pinning maglev trains is mainly based on the test line, which has certain requirements for repeated measurement and measurement convenience. In this article, various sensors are set on a single Dewar HTS pinning maglev system. PMG magnetic induction intensity signal, the vibration response signal, and the geometric signal are measured, respectively. The equidistant track marker on the PMG surface is used as a keyphasor pulse signal to realize the spatial resample method. After converting the time-domain signal to the spatial domain, the PMG local irregularity evaluation is carried out. Based on the seven-parameter fitting model, the PMG irregularity spectrum is fit. Taking the Chinese mainline railway track spectrum as the contrast, it can be concluded that the mid-wave irregularity of the HTS pinning maglev system is higher. The research content of this article provides a theoretical basis for the selection of measurement methods under different measurement requirements and conditions. The obtained irregularity power spectrum can provide a reference for the dynamic simulation of HTS pinning maglev.

Video synthetic aperture radar (ViSAR) operating at low frequencies generally suffers from low frame-rate and high latency due to its wide synthetic aperture and long dwell time. To conquer the problems mentioned above, this article proposes a ViSAR imaging processing framework based on the terahertz (THz) regime. The proposed method only needs a data repetition ratio of 0% rather than 80% for the microwave to reach the 0.2-m imaging resolution and 5-Hz frame rate with low latency due to the designed high pulse repetition frequency (PRF) system and short wavelength of THz wave. Thus, robust estimations of baseband Doppler centroid and Doppler rate are proposed based on the multicore adaptive weighting and imaging knowledge prior, which handles the Doppler sensitivity of the THz regime effectively. Finally, airborne experiments are carried out to validate the superiority of dynamic situational awareness and the potential of moving targets indication in the THz-ViSAR.

Hyperspectral oil spill mapping aims to distinguish the type of oil spill. Recently, most hyperspectral oil spill detection methods are based on supervised methods that work well with rich training samples. However, in the marine oil spill detection scenario, pixel annotations are difficult and costly. Moreover, the labels obtained by domain experts within a hyperspectral image (HSI) are often scarce. To address these issues, a self-supervised spectral–spatial transformer network (SSTNet) is proposed for hyperspectral oil spill mapping. First, we propose a transformer-based contrastive learning network to extract the deep discriminative features. Then, the learned features are transferred to the downstream classification network that is fine-tuned with very few labeled samples. Experiments on hyperspectral oil spill database (HOSD) constructed by ourselves indicate that the proposed method can obtain more promising performance than several state-of-the-art oil spill classification techniques in discriminating different types of oil spills, i.e., thick oil, thin oil, sheen, and seawater.

Deep learning mimics the function of the biological nervous system via adopting strategies motivated by neurophysiology. However, integrating biophysical characteristics to make deep learning more brain-like remains challenging. To investigate whether the more realistic simulated neurons could make deep learning more realistic from the biology perspective, we propose a three-layer multicompartment deep learning network with spike frequency adaptation (SFA). We utilize the segregated apical dendrites in the hidden layer to implement the credit assignment and coordinate the distribution of information flow. The introduction of SFA serves as the teaching signal. The results show that the apical dendrites separate the feedforward and feedback signals and instruct the credit assignment problem. The apical dendrites diverse the firing types of target neurons and further decrease the firing rate of nontarget neurons via switching their firing types from the tonic firing type to predominantly burst firing. Furthermore, the expression of SFA in the nontarget neurons further amplifies the difference between the firing rate of target and nontarget signals. This work provides new insight into a deep learning network with more biophysical neurons and is a meaningful supplementary for deep learning.

Lacticaseibacillus paracaseiis a facultative anaerobic bacterium, which is classified as a Generally Recognized as Safe (GRAS) microorganism by the Food and Drug Administration (FDA). In this study, we reported the whole genome sequence of Lacticaseibacillus paracasei36 (L. paracasei36), which consisted of a circular chromosome and three circular plasmids, with a total length of 3,166,237 bp and an average G + C content of 46.21%. Based on genetic analyses of production of harmful metabolites, virulence factors, and antibiotic resistance, as well as in vivosafety tests conducted with L. paracasei36, we postulated that L. paracasei36 was safe. The stress-response genes of L. paracasei36 indicated that it exhibited robust tolerance to the gastrointestinal tract, temperature, and osmotic pressure. Additionally, it demonstrated high capacities for colonization and adhesion, as well as notable antioxidant activity. The metabolomic results of the L. paracasei36 fermentation supernatant demonstrated that metabolites were mainly concentrated in amino acid metabolism and biosynthesis of other secondary metabolites, and there was a notable increase in proclavaminic acid and lactic acid. In conclusion, L. paracasei36 possessed favorable probiotic characteristics and safety, thereby establishing it as a probiotic candidate with considerable potential.

Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%–16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain–machine integration, and the investigation of brain cognition during learning.

The motion of particle clouds (i.e., sediment clouds) usually can be found in engineering applications such as wastewater discharge, land reclamation, and marine bed capping. In this paper, a series of laboratory tests are conducted on coral sand to investigate the shape feature of the single particle and the mixing processes of the coral sand particle clouds. The shape of coral sand particle is measured and quantified. The experimental results demonstrate that the shape of coral sand particles tends to be spherical as the particle size decreases, and empirical equations were established to explain the variation of D50and fS,50of coral sand. Compared with the silica sand, the evolution of the coral sand particle cloud still experiences three stages, but the threshold for the Reynolds number of particle clouds entering the next stage changes. Further, the normalized axial distance of the coral sand particle clouds is 58% smaller. The frontal velocity exhibits similar varying tendency for the coral sand particle cloud. Considering the difference in shape between coral sand particles and silica sand particles, a semi-empirical formula was proposed based on the original silica sand prediction formula by adding the shape factor and the experimental data of 122 µm⩽D50⩽842 µm. It can predict the frontal velocity of the coral sand particle clouds.

The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article presents a large-scale cerebellar network model for supervised learning, as well as a cerebellum-inspired neuromorphic architecture to map the cerebellar anatomical structure into the large-scale model. Our multinucleus model and its underpinning architecture contain approximately 3.5 million neurons, upscaling state-of-the-art neuromorphic designs by over 34 times. Besides, the proposed model and architecture incorporate 3411k granule cells, introducing a 284 times increase compared to a previous study including only 12k cells. This large scaling induces more biologically plausible cerebellar divergence/convergence ratios, which results in better mimicking biology. In order to verify the functionality of our proposed model and demonstrate its strong biomimicry, a reconfigurable neuromorphic system is used, on which our developed architecture is realized to replicate cerebellar dynamics during the optokinetic response. In addition, our neuromorphic architecture is used to analyze the dynamical synchronization within the Purkinje cells, revealing the effects of firing rates of mossy fibers on the resonance dynamics of Purkinje cells. Our experiments show that real-time operation can be realized, with a system throughput of up to 4.70 times larger than previous works with high synaptic event rate. These results suggest that the proposed work provides both a theoretical basis and a neuromorphic engineering perspective for brain-inspired computing and the further exploration of cerebellar learning.

This paper presents a frequency-domain imaging algorithm for near-field one-stationary bistatic synthetic aperture radar (OS-BiSAR) in the millimeter-wave (mmW) band. During the image reconstruction process, the interpolation procedure introduced by traditional fast imaging algorithm is avoided. By appropriately decomposing and approximating the nonlinear phase in frequency-domain echo, only multiplication and fast Fourier transform operations are carried out to achieve the lowest computational load. In addition, this method takes the propagation attenuation into consideration, which leads to a better imaging quality than the improved range migration algorithm for distant targets. Both simulation and measurement results validate the effectiveness of the proposed method on high-precision real-time imaging.

We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used for bedside cerebral perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived head models were used to identify high sensitivity regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on the head. Significant spatial variations in cerebral sensitivity, consistent across subjects, were found. We also identified correlates of such differences suitable for real-time assessment. These variations can be largely attributed to changes in extracerebral thickness and should be taken into account to optimize probe placement in experimental settings.

Abundant bitumen can be discovered in the wells of Upper Sinian Dengying Formation in the Sichuan Basin and their peripheral outcrops sections. As the cracking product of a paleo-oil reservoir, the occurrence morphologies and distribution characteristics of bitumen in carbonate reservoirs can indicate the preservation, migration and other key information of (paleo-) oil and gas reservoirs. In order to provide reference and basis for the deep and ultra-deep oil and gas exploration in the Sichuan Basin and the basins in western China, this paper systematically studies the occurrence morphological characteristics of bitumen in deep and ultra-deep carbonate reservoirs in well Gaoshi 1 of the Sichuan Basin by carrying out thin section observation, scanning electron microscope-energy spectrum analysis and fluid inclusion organic geochemical analysis on the cores of the fourth member of Dengying Formation (Deng 4 Member). In addition, the coupling relationship between the occurrence morphology of bitumen and the key event of hydrocarbon accumulation is revealed. And the following research results are obtained. First, solid bitumen in the Deng 4 Member deep and ultra-deep reservoir has two types of occurrence morphology. The first type of bitumen adheres to pore walls in the shape of a rim or exists in pores in the form of sticky branch (e.g., Wells Chuanshen 1 and Gaoshi 1). There are more obvious shrinkage fractures and residual pores generated by in-situ thermal cracking in this type of bitumen. And combined with fluid inclusion and burial thermal history, it can effectively indicate that a paleo-gas reservoir has been preserved so far since the paleo-oil reservoir was thermally cracked into carbonaceous bitumen. The second type of bitumen is distributed in intra- and inter-neogenic mineral crystals in pores disorderly in the shape of grain and band (e.g., Wells Mashen 1 and Wutan 1) and it has obvious breaking edges. It reveals that after a paleo-oil reservoir was thermally cracked into carbonaceous bitumen, the paleo-gas reservoir suffered blowdown and gas escape and neogenic fluid minerals moved into reservoirs to fill in pores. In conclusion, the occurrence morphologies and distribution characteristics of bitumen in deep and ultra-deep carbonate reservoirs can effectively indicate the hydrocarbon accumulation and evolution process of paleo-oil and gas reservoirs.

The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the corresponding computing platforms. Neuromorphic engineering provides approaches to high-performance biologically plausible computational paradigms inspired by neural systems. In this article, we present a biological-inspired cognitive supercomputing system (BiCoSS) that integrates multiple granules (GRs) of SNNs to realize a hybrid compatible neuromorphic platform. A scalable hierarchical heterogeneous multicore architecture is presented, and a synergistic routing scheme for hybrid neural information is proposed. The BiCoSS system can accommodate different levels of GRs and biological plausibility of SNN models in an efficient and scalable manner. Over four million neurons can be realized on BiCoSS with a power efficiency of 2.8k larger than the GPU platform, and the average latency of BiCoSS is 3.62 and 2.49 times higher than conventional architectures of digital neuromorphic systems. For the verification, BiCoSS is used to replicate various biological cognitive activities, including motor learning, action selection, context-dependent learning, and movement disorders. Comprehensively considering the programmability, biological plausibility, learning capability, computational power, and scalability, BiCoSS is shown to outperform the alternative state-of-the-art works for large-scale SNN, while its real-time computational capability enables a wide range of potential applications.

Novel trivalent europium (Eu3+)-activated La7Ta3W4O30:xEu3+(x = 0.5 mol%–40 mol%) red-emitting phosphors were synthesized by means of a high-temperature solid-state reaction. The structure, morphology, photoluminescence, thermal-stability properties, lifetime, and color-rendering of the prepared phosphors were investigated in detail. The La7Ta3W4O30:Eu3+phosphors show five emission peaks under near-ultraviolet (n-UV) at 397 nm, and these peaks are ascribed to the transitions of 5D0–7Fj(j = 0, 1, 2, 3 and 4) by Eu3+ions. The optimal doping concentration of Eu3+is 20 mol%, and the critical distance of the energy transfer between the Eu3+ions was calculated to be 1.768 nm. The quenching temperature (T0.5) of La7Ta3W4O30:20 mol%Eu3+is about 440 K. The quantum yield (QY) was measured to be 85.85%. The fabricated white-light-emitting diodes (w-LEDs) possess high color-rendering index (Ra) of 90, and high correlated color temperature (CCT) of 5810 K, respectively. The Commission Internationale de L' Eclairage (CIE) coordinates are (0.311, 0.322). Therefore, the prepared phosphor has a promising application for w-LEDs.

A novel red-emitting phosphor tantalate Ca2YTaO6:Eu3+was synthesized by a solid-state reaction. The purity and surface morphology of the phosphors were characterized. The Ca2YTaO6:Eu3+phosphors show a sharp emission peak at 612 nm under near-ultraviolet (n-UV) at 395 nm because of the 5D0 → 7F2transition of Eu3+. The optimal Eu3+doping concentration in Ca2YTaO6is 40 mol% and the critical energy-transfer distance of Eu3+ions was calculated to be 0.9 nm. The emission spectra of Ca2YTaO6:Eu3+from 300 to 480 K were investigated. The thermal-quenching temperature (T0.5) of Ca2YTaO6:Eu3+is above 480 K. The color purity of Ca2YTaO6:40 mol%Eu3+is as high as 99.8%. The luminescence lifetime of Ca2YTaO6:40 mol%Eu3+was also discussed. The high color purity and high thermal stability of Eu3+-doped Ca2YTaO6phosphors contribute to its application value in white light-emitting diodes (w-LEDs).