Rupture stress of eutectic composite ceramics with rod-shaped crystals

Eutectic composite ceramics has a wide range of applications in the aerospace industry due to its excellent mechanical properties. The rupture stress of the materials is a subject of considerable importance. Eutectic composite ceramics primarily consist of rod-shaped crystals, with a small amount of particles and preexisting defects dispersed throughout. Aligned nano-micron fibers are embedded within the rod-shaped crystals. Rupture stress of a eutectic composite ceramic depends on its fracture surface energy and preexisting defects. In this study, the equivalent fracture surface energy of a eutectic ceramic composite was calculated based on its additional fracture work. Next, the effects of the preexisting defects were considered. Then, a micromechanical model of the eutectic composite ceramic was established based on its microstructural characteristics. The defects were assumed to be lamellar, and the surrounding matrix was assumed to be transversely isotropic. Using this information, the rupture stress of the eutectic ceramic composite was predicted. A comparison of the theoretical and experimental results indicated that the predicted rupture stresses corresponded with the tested data

Research on interface slippage of fiber reinforced composite ceramics

Based on the microscopic characteristics of fiber reinforced composite ceramics, the slippage stress at the interface of composite ceramics under external loading is analyzed. The relation between the applied strain of the triangular symmetrical eutectic and the load of composite ceramics is confirmed. And the maximum shear stress that the triangular symmetrical eutectic can endure is computed. The yield shear stress was calculated by the hardness and fracture toughness of composite ceramics. When the maximum shear stress which the triangular symmetrical eutectic can bear is equal to the yield shear stress, the slipping stress of micro-mechanical interface in composite ceramics is obtained. The results showed that fiber inclusions in the eutectic having smaller dimension and larger volume content would provide larger partial plastic deformation of composite ceramics

Reconstruction of tokamak plasma safety factor profile using deep learning

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep learning-based surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system

High optical transmittance of aluminum ultrathin film with hexagonal nanohole arrays as transparent electrode

We fabricate samples of aluminum ultrathin films with hexagonal nanohole arrays and characterize the transmission performance. High optical transmittance larger than 60% over a broad wavelength range from 430 nm to 750 nm is attained experimentally. The Fano-type resonance of the excited surface plasmon plaritons and the directly transmitted light attribute to both of the broadband transmission enhancement and the transmission suppression dips

Analysis of the effect of neuroendoscopy-assisted microscopy in the treatment of Large (Koos grade IV) vestibular schwannoma

IntroductionThis article aimed to investigate the effects of the endoscopic-assisted microsurgery technique on the resection of large (Koos grade IV) vestibular schwannoma (VS) and provide a prognosis analysis of the patients.MethodsA retrospective analysis of the use of the endoscopic-assisted microsurgery technique in 16 cases of large vestibular schwannoma surgery was carried out. Intraoperative nerve electrophysiological monitoring was conducted to explore the effect of neuroendoscopy on the resection of internal auditory canal tumors, protection of the facial nerve, and minimizing postoperative complications.ResultsTumors were completely removed in all 16 cases, and the facial nerve was anatomically preserved in 14 cases (87.5%). There was no postoperative cerebrospinal fluid leakage and no intracranial infection complications occurred.Following the House-Brackmann (H-B) grading system, post-operative facial nerve function was grade I in 5 cases, grade II in 6 cases, grade III in 3 cases, and grade V in 2 cases. As a result, the preservation rate of facial nerve function (H-B grade I-II) was 68.8%. All 16 patients were followed up for 3 to 24 months, and no tumor recurrence was found on enhanced MRI.DiscussionUsing the endoscopic-assisted microsurgery technique in the retrosigmoid approach has many advantages over the microscopic-only approach. When compared to the microscopy-only approach, the endoscope can provide a wide-angle surgical field superior to that of a microscope in areas such as the internal auditory canal in the resection of large VS, minimize iatrogenic injuries, ensure complete removal of internal auditory canal tumors, and well as reducing postoperative complications such as cerebrospinal fluid leakage and the loss of facial and auditory nerve functions

FBNet: Feedback Network for Point Cloud Completion

The rapid development of point cloud learning has driven point cloud completion into a new era. However, the information flows of most existing completion methods are solely feedforward, and high-level information is rarely reused to improve low-level feature learning. To this end, we propose a novel Feedback Network (FBNet) for point cloud completion, in which present features are efficiently refined by rerouting subsequent fine-grained ones. Firstly, partial inputs are fed to a Hierarchical Graph-based Network (HGNet) to generate coarse shapes. Then, we cascade several Feedback-Aware Completion (FBAC) Blocks and unfold them across time recurrently. Feedback connections between two adjacent time steps exploit fine-grained features to improve present shape generations. The main challenge of building feedback connections is the dimension mismatching between present and subsequent features. To address this, the elaborately designed point Cross Transformer exploits efficient information from feedback features via cross attention strategy and then refines present features with the enhanced feedback features. Quantitative and qualitative experiments on several datasets demonstrate the superiority of proposed FBNet compared to state-of-the-art methods on point completion task.Comment: The first two authors contributed equally to this work. The source code and model are available at https://github.com/hikvision-research/3DVision/. Accepted to ECCV 2022 as oral presentatio

CorrAUC: a Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine Learning Techniques

Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in IoT network. To address the problem, a new framework model is proposed. Firstly, a novel feature selection metric approach named CorrAUC proposed, and then based on CorrAUC, a new feature selection algorithm name Corrauc is develop and design, which is based on wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using AUC metric. Then, we applied integrated TOPSIS and Shannon Entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT dataset and four different ML algorithms. Experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average