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To investigate the effects of different amounts of flaxseed powder on the quality and digestive characteristics of noodles, flaxseed powder (0~15%) was added to wheat flour to investigate the changes in the appearance, texture, cooking, sensory, and digestive characteristics of noodles. Results showed that with the increase of the proportion of flaxseed powder, the appearance color of the noodles was deepened, the chewability and resilience of the noodles increased first and then decreased, and the hardness, elasticity and cohesion of the noodles were significantly decreased (P

Recent clinical studies have shown that mutation of phosphatase and tensin homolog deleted on chromosome 10 (PTEN) gene in cancer cells may be associated with immunosuppressive tumor microenvironment (TME) and poor response to immune checkpoint blockade (ICB) therapy. Therefore, efficiently restoring PTEN gene expression in cancer cells is critical to improving the responding rate to ICB therapy. Here, we screened an adeno-associated virus (AAV) capsid for efficient PTEN gene delivery into B16F10 tumor cells. We demonstrated that intratumorally injected AAV6-PTEN successfully restored the tumor cell PTEN gene expression and effectively inhibited tumor progression by inducing tumor cell immunogenic cell death (ICD) and increasing immune cell infiltration. Moreover, we developed an anti-PD-1 loaded phospholipid-based phase separation gel (PPSG), which formed an in situ depot and sustainably release anti-PD-1 drugs within 42 days in vivo. In order to effectively inhibit the recurrence of melanoma, we further applied a triple therapy based on AAV6-PTEN, PPSG@anti-PD-1 and CpG, and showed that this triple therapy strategy enhanced the synergistic antitumor immune effect and also induced robust immune memory, which completely rejected tumor recurrence. We anticipate that this triple therapy could be used as a new tumor combination therapy with stronger immune activation capacity and tumor inhibition efficacy.

Existing testing methods inadequately capture the internal stress state of concrete, a vital structural safety metric, resulting in a divergence between measured and actual internal stress states, especially in larger structures. There is an urgent need for innovative theoretical and technical approaches that can directly, accurately, and instantly reflect the internal stress state of concrete, meeting the growing demand for such detection. This paper conducts experimental research on the internal stress detection of concrete based on the principle of displacement current. Firstly, the theoretical relationship between the internal stress and oscillation frequency of concrete is established. Secondly, two dielectric plates are embedded in the concrete specimen as a sensing unit, and an LC oscillation circuit is connected externally to express the internal stress state of the concrete in the form of oscillation frequency. The research findings suggest that there is a decrease in the oscillation frequency as the dielectric constant of the concrete increases. Additionally, a significant change in the oscillation frequency is observed because of the dielectric polarization effect that takes place during the pouring and curing of the concrete specimen. The concrete age starts from about 25 days, and its oscillation frequency gradually stabilizes; the internal stress of the concrete is proportional to the oscillation frequency, and they show a good correlation in the experiment.

Due to the insufficient long-term protection and significant efficacy reduction to new variants of current COVID-19 vaccines, the epidemic prevention and control are still challenging. Here, we employ a capsid and antigen structure engineering (CASE) strategy to manufacture an adeno-associated viral serotype 6-based vaccine (S663V-RBD), which expresses trimeric receptor binding domain (RBD) of spike protein fused with a biological adjuvant RS09. Impressively, the engineered S663V-RBD could rapidly induce a satisfactory RBD-specific IgG titer within 2 weeks and maintain the titer for more than 4 months. Compared to the licensed BBIBP-CorV (Sinopharm, China), a single-dose S663V-RBD induced more endurable and robust immune responses in mice and elicited superior neutralizing antibodies against three typical SARS-CoV-2 pseudoviruses including wild type, C.37 (Lambda) and B.1.617.2 (Delta). More interestingly, the intramuscular injection of S663V-RBD could overcome pre-existing immunity against the capsid. Given its effectiveness, the CASE-based S663V-RBD may provide a new solution for the current and next pandemic.

Abstract Wideband two‐dimensional multiple input multiple output (MIMO) radar can be used for target three‐dimensional (3‐D) imaging. As the echo of wideband MIMO radar is derived from both time and spatial sampling, the time‐frequency coupling is contained, resulting in imaging defocussing. To solve this problem, a time‐frequency decoupling algorithm based on matrix enhancement and matrix pencil (MEMP) is proposed to achieve high‐resolution 3‐D imaging. By combining the enhanced matrix constructed via MEMP with the modern spectral estimation theory, the time‐frequency coupling term is regarded as a space rotation factor and is eliminated in the process of target position estimation. Simulation results show that the proposed algorithm can reduce the negative influence of time‐frequency coupling and improve the imaging accuracy of wideband two‐dimensional MIMO radar.

Abstract The Shannon‐Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it will change the signal nature, which leads to the unavailability of Shannon‐Nyquist sampling theorem‐based interference detection and parameter estimation methods. According to the different posterior probability distribution features of NBI, DSSS signal, and noise in compressed domain, a posterior probability model of whether NBI exists in the received signal is constructed by using the compressed measurements. The posterior probability that whether NBI exists in the received signal is employed as the feature parameters of NBI detection and parameter estimation. With the feature parameters detected, NBI detection can be achieved and the edge location of NBI components can be located. The relationship between the edge location of NBI components and the edge frequency of NBI is constructed, which will contribute to estimate the NBI edge frequency. The numerical simulation results demonstrate that the proposed method can effectively achieve NBI detection and parameter estimation in the compressed domain and it performs significantly better than the other existing methods.

Person transfer is a major problem for nursing in home and clinical, and this problem is becoming more prominent with the development of aging society and the change of population structure. In this article, a novel person transfer assist system named E-Pat (Easy-Patient Transfer), which can meet the transfer needs of the patient between beds, has been designed and analyzed. The importance and originality of this study are that it explores a theoretical design method for device to enhance patient comfortably when transferred, and put it into practice. The main tasks completed in this article include: (1) a prototype of the device is designed based on the principle of relative static contact point theory. In addition, the corresponding relationship between the human body pressure and the insertion angle is proposed, which provides a theoretical basis for the optimization of the mechanism; (2) based on STM32 (STMicroelectronics 32-bits Microcontroller) chip, the hardware circuit part of the device is designed. According to the transfer environment requirements of the device, the synchronization control algorithm of the device is designed and the experiment is carried out; (3) the performance evaluation of the device is carried out by means of Analytic Hierarchy Process (AHP), through the research of a group of 30 volunteers, the performance of the system has been evaluated accurately, which provides a reference for the industrialization of the prototype. The whole research of this article proves that this kind of person transfer assist system has good performance, it can also reduce the burden of caregivers.

Hand rehabilitation is critical to the recovery of post-stroke patients. However, designing a modular mechanism of the hand to improve the human-machine compatibility and precision of operation is still a challenge. This paper proposes a new type of hand exoskeleton rehabilitation robot with nine degrees of freedom. With the flexible rods, the passive range of motion for finger adduction/abduction is extended under the premise of independent flexion/extension of the metacarpophalangeal and proximal interphalangeal joint. Based on hand anatomy, the relationship between the offset of the metacarpophalangeal joint and the body height in the process of flexion and extension is discussed, and it is applied to the structure optimization and control system. The genetic algorithm is employed to achieve the size optimization, and the kinematics is analyzed. Finally, a prototype is built and preliminary experiments are carried out, including the range of motion and the grasping ability of the robot. The experimental results show that the robot can realize the patients’ hand rehabilitation function and has certain adaptability.

Sparse-recovery-based space–time adaptive processing (STAP) methods can exhibit superior clutter suppression performance with limited training data. However, the clutter suppression performance seriously degrades when the mutual coupling is present in the STAP array elements. In this paper, a sparse Bayesian learning (SBL)-based STAP method against the mutual coupling by using the middle subarray is proposed. Specifically, the mutual coupling matrix (MCM) of the STAP uniform linear array is approximately described as a banded symmetric Toeplitz matrix since the effect of mutual coupling between two array elements is inversely related to their distance and can be negligible when their distance is larger than a few wavelengths. Utilizing this specific structure of the MCM, a mutual coupling mitigation method is developed for STAP by rearranging the received snapshots with the designed spatial–temporal selection matrix. Then, to efficiently recover the clutter angle-Doppler profile, the modified fast converging SBL (FCSBL) named adaptive FCSBL algorithm and its multiple measurement vector case adaptive MFCSBL algorithm are developed with only partial hyper-parameters being updated in a single iteration. The proposed method not only achieves the superior clutter suppression performance in the presence of unknown mutual coupling, but also has low computational load. The simulation results are implemented to validate the effectiveness of the proposed methods.

The presence of mutual coupling usually causes space-time steering vector distortion for space-time adaptive processing (STAP) in airborne radar, thereby causing significant performance degradation. In this paper, a robust STAP approach was proposed against mutual coupling based on middle subarray clutter covariance matrix reconstruction. To address the space-time steering vector distortion caused by the mutual coupling, the training snapshots of middle subarray were obtained using the spatial-temporal selection matrix, and they were collected to compute the sample covariance matrix (SCM). Thus, the clutter-plus-noise covariance matrix (CNCM) of middle subarray was reconstructed by utilizing the clutter Capon spectrum of middle subarray to integrate over the possible clutter spatial-temporal domain. Moreover, the target covariance matrix of middle subarray was calculated by employing the target Capon spectrum of middle subarray to integrate over the possible target region, and the space-time steering vector of the target was estimated with the eigenvector corresponding to its maximum eigenvalue. Finally, the proposed robust STAP weight vector of middle subarray was built. The proposed method is capable of simultaneously mitigating the mutual coupling, target components in the SCM, and target space-time steering vector mismatch. The simulation results verified the effectiveness of the proposed approach.

The high-speed translational motion of space targets will cause the micro-Doppler to shift, tilt, and fold, which brings great difficulty to the extraction of micromotion features. Translational motion must be compensated in advance to extract the authentic characteristics of micro-Doppler curves. To solve the problem of translational motion compensation, an estimation method of translational parameters based on deep learning theory is proposed. A polynomial model describing translational motion is constructed firstly by analyzing its dynamic principle. Meanwhile, the parametric characterization of micromotion signal is deduced by taking the cone target as an example. On this basis, two regression networks that can estimate acceleration and velocity respectively from the time-frequency graph are built using transfer learning. The translational motion compensation is accomplished with high accuracy and low computation complexity. The proposed method can also achieve satisfactory results in the presence of high intensity noise and discontinuous scattering points. Finally, the effectiveness and robustness of the proposed method are validated by simulations.

In this paper, the problem of 2-D direction-of-arrival (2-D-DOA) estimation for coprime planar arrays is investigated, and a method based on the polynomial root finding technique with high accuracy and computational efficiency is proposed. In conventional methods, the whole coprime planar array is usually divided into two subarrays to individually estimate 2-D-DOA, and then the true 2-D-DOA can be distinguished from the ambiguous result utilizing the coprime property. Different from the conventional methods, the proposed method utilizes the signal space and noise space of all data to improve the estimation accuracy. In addition, the computational complexity is reduced by converting the 2-D spectral peak searching problem into double polynomial root finding problem. Furthermore, the matching error is eliminated in the scenario of multiple targets. Simulation results demonstrate that the proposed method achieves not only higher estimation accuracy but also lower computational complexity over conventional methods.

To solve the problem of mutual superposition and interference of multi-target micro-Doppler in midcourse, a method based on modified ant colony optimisation (ACO) is proposed. On the basis of building 3D time–frequency map, pheromone on each spot in the workspace can be updated, according to the changing rule of micro-Doppler. The pheromone leads ant to seek for required optimal micro-Doppler path gradually; thus single micro-Doppler curve corresponding to each scattering point can be separated. Simulation results indicate that the proposed method can overcome the coupling between micro-Doppler of different scattering point and the influence of background noise better, and implement effective separation of multi-target.

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

Most existing finger rehabilitation robots are structurally complex and cannot be adapted to multiple work conditions, such as clinical and home. In addition, there is a lack of attention to active adduction/abduction (A/A) movement, which prevents stroke patients from opening the joint in time and affects the rehabilitation process. In this paper, an end-effector finger rehabilitation robot (EFRR) with active A/A motion that can be applied to a variety of applications is proposed. First, the natural movement curve of the finger is analyzed, which is the basis of the mechanism design. Based on the working principle of the cam mechanism, the flexion/extension (F/E) movement module is designed and the details used to ensure the safety and reliability of the device are introduced. Then, a novel A/A movement module is proposed, using the components that can easily individualized design to achieve active A/A motion only by one single motor, which makes up for the shortcomings of the existing devices. As for the control system, a fuzzy proportional-derivative (PD) adaptive impedance control strategy based on the position information is proposed, which can make the device more compliant, avoid secondary injuries caused by excessive muscle tension, and protect the fingers effectively. Finally, some preliminary experiments of the prototype are reported, and the results shows that the EFRR has good performance, which lays the foundation for future work.

In order to realize the intervention operation in the unstructured and ample environments such as stomach and colon, a dual-spin spherical capsule robot (DSCR) driven by pure magnetic torque generated by the universal rotating magnetic field (URMF) is proposed. The coupled magnetic torque, the viscoelastic friction torque, and the gravity torque were analyzed. Furthermore, the posture dynamic model describing the electric-magnetic-mechanical-liquid coupling dynamic behavior of the DSCR in the gastrointestinal (GI) tract was established. This model is a second-order periodic variable coefficient dynamics equation, which should be regarded as an extension of the Lagrange case for the dual-spin body system under the fixed-point motion, since the external torques were applied. Based on the Floquet–Lyapunov theory, the stability domain of the DSCR for the asymptotically stable motion and periodic motion were obtained by investigating the influence of the angular velocity of the URMF, the magnetic induction intensity, and the centroid deviation. Research results show that the DSCR can realize three kinds of motion, which are asymptotically stable motion, periodic motion, and chaotic motion, according to the distribution of the system characteristic multipliers. Moreover, the posture stability of the DSCR can be improved by increasing the angular velocity of the URMF and reducing the magnetic induction intensity.

An enhanced two-dimensional direction of arrival (2D-DOA) estimation algorithm for large spacing three-parallel uniform linear arrays (ULAs) is proposed in this paper. Firstly, we use the propagator method (PM) to get the highly accurate but ambiguous estimation of directional cosine. Then, we use the relationship between the directional cosine to eliminate the ambiguity. This algorithm not only can make use of the elements of the three-parallel ULAs but also can utilize the connection between directional cosine to improve the estimation accuracy. Besides, it has satisfied estimation performance when the elevation angle is between 70° and 90° and it can automatically pair the estimated azimuth and elevation angles. Furthermore, it has low complexity without using any eigen value decomposition (EVD) or singular value decompostion (SVD) to the covariance matrix. Simulation results demonstrate the effectiveness of our proposed algorithm.

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

In this paper, we focus on the problem of two-dimensional direction of arrival (2D-DOA) estimation for monostatic MIMO Radar with electromagnetic vector received sensors (MIMO-EMVSs) under the condition of gain and phase uncertainties (GPU) and mutual coupling (MC). GPU would spoil the invariance property of the EMVSs in MIMO-EMVSs, thus the effective ESPRIT algorithm unable to be used directly. Then we put forward a C-SPD ESPRIT-like algorithm. It estimates the 2D-DOA and polarization station angle (PSA) based on the instrumental sensors method (ISM). The C-SPD ESPRIT-like algorithm can obtain good angle estimation accuracy without knowing the GPU. Furthermore, it can be applied to arbitrary array configuration and has low complexity for avoiding the angle searching procedure. When MC and GPU exist together between the elements of EMVSs, in order to make our algorithm feasible, we derive a class of separated electromagnetic vector receiver and give the S-SPD ESPRIT-like algorithm. It can solve the problem of GPU and MC efficiently. And the array configuration can be arbitrary. The effectiveness of our proposed algorithms is verified by the simulation result.