Gas pressure sintering of BN/Si3N4 wave-transparent material with Y2O3–MgO nanopowders addition

AbstractBN/Si3N4 ceramics performed as wave-transparent material in spacecraft were fabricated with boron nitride powders, silicon nitride powders and Y2O3–MgO nanopowders by gas pressure sintering at 1700°C under 6MPa in N2 atmosphere. The effects of Y2O3–MgO nanopowders on densification, phase evolution, microstructure and mechanical properties of BN/Si3N4 material were investigated. The addition of Y2O3–MgO nanopowders was found beneficial to the mechanical properties of BN/Si3N4 composites. The BN/Si3N4 ceramics with 8wt% Y2O3–MgO nanopowders showed a relative density of 80.2%, combining a fracture toughness of 4.6MPam1/2 with an acceptable flexural strength of 396.5MPa

A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition

Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects). Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR) can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE), which achieves values of 77.88% and 7.33% on average, respectively. For the online analysis, the average classification accuracy and standard deviation of ASFM in the subject-to-subject evaluation for all the 15 subjects in a dataset was 75.11% and 7.65%, respectively, gaining a significant performance improvement compared to the best baseline LR which achieves 56.38% and 7.48%, respectively. The experimental results confirm the effectiveness of the proposed method relative to state-of-the-art methods. Moreover, computational efficiency of the proposed ASFM method is much better than standard domain adaptation; if the numbers of training samples and test samples are controlled within certain range, it is suitable for real-time classification. It can be concluded that ASFM is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the field of EEG-based emotion recognition

Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the F 1 score of 0.90 and area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.99 over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar

Targeted therapy using engineered extracellular vesicles: principles and strategies for membrane modification

Abstract Extracellular vesicles (EVs) are 30–150 nm membrane-bound vesicles naturally secreted by cells and play important roles in intercellular communication by delivering regulatory molecules such as proteins, lipids, nucleic acids and metabolites to recipient cells. As natural nano-carriers, EVs possess desirable properties such as high biocompatibility, biological barrier permeability, low toxicity, and low immunogenicity, making them potential therapeutic delivery vehicles. EVs derived from specific cells have inherent targeting capacity towards specific cell types, which is yet not satisfactory enough for targeted therapy development and needs to be improved. Surface modifications endow EVs with targeting abilities, significantly improving their therapeutic efficiency. Herein, we first briefly introduce the biogenesis, composition, uptake and function of EVs, and review the cargo loading approaches for EVs. Then, we summarize the recent advances in surface engineering strategies of EVs, focusing on the applications of engineered EVs for targeted therapy. Altogether, EVs hold great promise for targeted delivery of various cargos, and targeted modifications show promising effects on multiple diseases. Graphical Abstrac

Phase-type Fresnel zone plate with multi-wavelength imaging embedded in fluoroaluminate glass fabricated via ultraviolet femtosecond laser lithography

Herein, we report a novel optical glass material, fluoroaluminate (AlF3) glass, with excellent optical transmittance from ultraviolet to infrared wavelength ranges, which provides more options for application in optical devices. Based on its performance, the phase-type Fresnel zone plate (FZP) by ultraviolet femtosecond (fs) laser-inscribed lithography is achieved, which induces the refractive index change by fs-laser tailoring. The realization of ultraviolet fs-laser fabrication inside glass can benefit from the excellent optical performance of the AlF3 glass. Compared with traditional surface-etching micro-optical elements, the phase-type FZP based on AlF3 glass exhibits a clear and well-defined geometry and presents perfect environmental suitability without surface roughness problems. Additionally, optical focusing and multi-wavelength imaging can be easily obtained. Phase-type FZP embedded in AlF3 glass has great potential applications in the imaging and focusing in glass-integrated photonics, especially for the ultraviolet wavelength range.Published versio

Reweighted Robust Particle Filtering Approach for Target Tracking in Automotive Radar Application

In view of the decline of filtering accuracy caused by measured outliers in target tracking application, a novel reweighted robust particle filter is proposed to acquire accurate state estimates in an automotive radar system. To infer the importance of each entry in the multidimensional contaminated measurement vector, we employ a weight vector, which follows a Gamma distribution, to model the measured noise and carry out accurate state estimates. Additionally, the particle filter method is employed to perform approximate posterior inference of state estimates in the nonlinear model. The Cramer–Rao lower bound is provided for the performance evaluation of the proposed method. Both simulation and experimental results demonstrate the superiorities of the proposed algorithm over other robust solutions

Reweighted Robust Particle Filtering Approach for Target Tracking in Automotive Radar Application

In view of the decline of filtering accuracy caused by measured outliers in target tracking application, a novel reweighted robust particle filter is proposed to acquire accurate state estimates in an automotive radar system. To infer the importance of each entry in the multidimensional contaminated measurement vector, we employ a weight vector, which follows a Gamma distribution, to model the measured noise and carry out accurate state estimates. Additionally, the particle filter method is employed to perform approximate posterior inference of state estimates in the nonlinear model. The Cramer–Rao lower bound is provided for the performance evaluation of the proposed method. Both simulation and experimental results demonstrate the superiorities of the proposed algorithm over other robust solutions

Object Detection Method for Grasping Robot Based on Improved YOLOv5

In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper

Coseismic Rupture Model and Tectonic Implications of the January 7 2022, Menyuan Mw 6.6 Earthquake Constraints from InSAR Observations and Field Investigation

A Mw 6.6 earthquake struck Menyuan, Qinghai, China, on 7 January 2022. To determine the rupture parameters of this event, the coseismic InSAR deformation fields were mapped and further employed to estimate the focal mechanism. The best-fitting solution emphasized that the 2022 Menyuan earthquake ruptured at the junction of the Tuolaishan fault and the Lenglongling fault. Both rupturing faults were dominated by sinistral strike-slip, and the main slip was concentrated on the shallow part of the rupture plane. The latter was the main rupture segment with a strike of 106° and a dip of 86°. The slip mainly occurred at depths of 0–8 km, and the rupture was exposed at the surface. The maximum slip reached ~3.5 m, which occurred mainly at a depth of 4 km. Joint analysis of the optimal slip model, relocated aftershocks, Coulomb stress change, and field observation suggested that the strain energy in the Tuolaishan fault may not have been fully released and needs further attention. Moreover, the 2022 Mw6.6 Menyuan earthquake caused a significant stress loading effect on the western Tuolaishan fault and eastern Lenglongling fault, which implies that the 2022 event increased the seismic hazard in these regions

Research on Intelligent Robot Point Cloud Grasping in Internet of Things

The development of Internet of Things (IoT) technology has enabled intelligent robots to have more sensing and decision-making capabilities, broadening the application areas of robots. Grasping operation is one of the basic tasks of intelligent robots, and vision-based robot grasping technology can enable robots to perform dexterous grasping. Compared with 2D images, 3D point clouds based on objects can generate more reasonable and stable grasping poses. In this paper, we propose a new algorithm structure based on the PointNet network to process object point cloud information. First, we use the T-Net network to align the point cloud to ensure its rotation invariance; then we use a multilayer perceptron to extract point cloud characteristics and use the symmetric function to get global features, while adding the point cloud characteristics attention mechanism to make the network more focused on the object local point cloud. Finally, a grasp quality evaluation network is proposed to evaluate the quality of the generated candidate grasp positions, and the grasp with the highest score is obtained. A grasping dataset is generated based on the YCB dataset to train the proposed network, which achieves excellent classification accuracy. The actual grasping experiments are carried out using the Baxter robot and compared with the existing methods; the proposed method achieves good grasping effect