Numerical Simulation Study on Impact Initiation on Shielded Charge Using Hypervelocity Composite-Structure Reactive Fragments.

In order to study the impact initiation process and mechanism of hypervelocity PTFE/Al composite structure reactive fragments on a shielded charge, first, an existing PTFE/Al reactive fragment hypervelocity collision experiment was numerically simulated using the SPH algorithm in ANSYS/AUTODYN 17.0 software. Then, the Lee-Tarver model was verified to describe the detonation reaction behavior and explosion damage effect of reactive materials. A numerical simulation analysis of the impact of two kinds of ultra-high-speed PTFE/Al composite-structure reactive fragments on a shielded charge was carried out using the SPH algorithm. These were steel-coated PTFE/Al and steel-semi-coated PTFE/Al fragments, and they were compared with the impact of steel fragments. The results indicate that the threshold velocities of the impact initiation of the two composite-structure reactive fragments on the shielded charge were both 2.6 km/s, while the threshold velocity of the steel fragment was 2.7 km/s. Under the threshold velocity condition, the two composite-structure reactive fragments increase the time and intensity of the compressed shock wave pulse in the explosive due to the impact energy release effect of the reactive materials, causing the shielded charge to detonate under the continuous long-term pulse loads. However, the mechanism of the steel fragment on the shielded charge belongs to the shock-detonation transition. The research results can provide scientific references for the design of hypervelocity reactive fragments and the study of their damage mechanism.

A novel 3D evaluation method for surface defects using broadband laser-generated Rayleigh waves with wavenumber analysis.

To address the issues of large imaging errors for small defects and the difficulty in depth evaluation using local wavenumber estimation for surface defect imaging, a novel three-dimensional (3D) evaluation method for surface defects using broadband laser-generated Rayleigh waves with wavenumber analysis is proposed. A finite element model is established to investigate the interaction between the Rayleigh wave and the surface defect and reveal the wavenumber change mechanism of the non-dispersive Rayleigh wave in the case of defects. It is discovered that when the Rayleigh wave encounters the surface defect, various mode-converted scattered waves are generated, resulting in the appearance of new components with wavenumbers lower than that of the incident Rayleigh wave in the wavenumber domain. Additionally, the maximum amplitude of the Rayleigh wave in the B-scan image increases as the defect depth increases. Based on the simulation analysis, a 3D evaluation method for surface defects is proposed. Firstly, the scattered Rayleigh wave caused by the defect is extracted using frequency-wavenumber analysis. Secondly, a space-frequency-wavenumber analysis is used to determine the local wavenumber of the scattered Rayleigh wave for defect imaging. Finally, the defect depth is estimated by analyzing the maximum amplitude of the Rayleigh wave. A surface defect detection experiment is conducted to verify the effectiveness of the proposed method, and the experimental results demonstrate that the proposed method can suppress noise interference and accomplish high-precision imaging of small surface defects compared to the traditional method. Moreover, the method can establish a linear mapping relationship between the defect depth and the maximum amplitude of the Rayleigh wave for depth evaluation. The research results can provide a potential application for the 3D evaluation of surface defects.

Quantitative detection of surface defect using laser-generated Rayleigh wave with broadband local wavenumber estimation.

Laser ultrasonic technology has been widely used in surface defect detection attribute to its non-contact, non-destructive and high spatial resolution characteristics. This paper proposes a surface defect quantitative detection method using laser-generated Rayleigh wave with broadband local wavenumber estimation. In this method, considering the broadband characteristics of laser-generated Rayleigh wave, the broadband local wavenumber estimation is presented to achieve the defect imaging accurately, and then the defect geometric parameters are estimated based on image segmentation. A surface defect detection experiment using the laser ultrasonic detection system is conducted to verify the effectiveness of the proposed method. The experimental results show that the proposed method has superior imaging effect for vertical and inclined defects than the standing wave energy method or reflected wave energy method. Besides, the geometric parameters such as length, width, and inclination angle of a surface defect can be accurately identified by the proposed method, the errors of vertical defects are 1.6% in length and 4.0% in width respectively, as well as the maximum and minimum error of inclined defects are 5.0% and 1.28% in inclination angle respectively. The research results provide a potential application for the fast and non-destructive surface defect detection of metal structures.

A Sound and Vibration Fusion Method for Fault Diagnosis of Rolling Bearings under Speed-Varying Conditions.

The fault diagnosis of rolling bearings is critical for the reliability assurance of mechanical systems. The operating speeds of the rolling bearings in industrial applications are usually time-varying, and the monitoring data available are difficult to cover all the speeds. Though deep learning techniques have been well developed, the generalization capacity under different working speeds is still challenging. In this paper, a sound and vibration fusion method, named the fusion multiscale convolutional neural network (F-MSCNN), was developed with strong adaptation performance under speed-varying conditions. The F-MSCNN works directly on raw sound and vibration signals. A fusion layer and a multiscale convolutional layer were added at the beginning of the model. With comprehensive information, such as the input, multiscale features are learned for subsequent classification. An experiment on the rolling bearing test bed was carried out, and six datasets under various working speeds were constructed. The results show that the proposed F-MSCNN can achieve high accuracy with stable performance when the speeds of the testing set are the same as or different from the training set. A comparison with other methods on the same datasets also proves the superiority of F-MSCNN in speed generalization. The diagnosis accuracy improves by sound and vibration fusion and multiscale feature learning.

Study of Unilateral Extrapedicular and Bilateral Pedicle Approach Percutaneous Kyphoplasty for Osteoporotic Vertebral Compression Fracture.

OBJECTIVE: To determine the efficacy and complication of unilateral extrapedicular approach (UEA) and bilateral pedicle approach (BPA) percutaneous kyphoplasty (PKP) in treating thoracolumbar osteoporotic compression fractures. STUDY DESIGN: A descriptive study. PLACE AND DURATION OF STUDY: Department of Orthopaedics, Bazhou district people Hospital, Sichuan, China, from December 2016 to March 2021. METHODOLOGY: Patients with single-level thoracolumbar osteoporotic compression fractures, who underwent BPA and UEA PKP, were divided into the UEA (n=47) and BPA group (n=42). Index was recorded including operation duration, bone cement volume, intraoperative X-ray times, complication, visual analogue scale (VAS), Cobb angle, Oswestry dysfunction index (ODI), the recurrence rate of the injured vertebra and adjacent vertebral fractures within 12 months after operation. RESULTS: There were significant differences in operation duration, bone cement volume, and intraoperative X-ray time between the two groups (p<0.05). VAS, Cobb angle, and ODI significantly improved at 3 days and 12 months after the surgery in each group (p<0.05), but no significant statistical difference was found at each time point between the groups (p>0.05). Bone cement leaked 2 cases in UEA and 8 cases in the BPA group (p<0.05). No pulmonary embolism, neurovascular injury, and infection occurred. No significant difference was found in fracture recurrence rate within 12 months after operation (p>0.05). CONCLUSION: Unilateral extrapedicular percutaneous kyphoplasty is an effective and safe way in treating thoracolumbar osteoporotic fractures with relatively less duration of surgery, intraoperative X-ray exposure, bone cement volume, and leakage rate. KEY WORDS: Thoracolumbar osteoporotic compression fractures, Unilateral extrapedicular approach, Percutaneous kyphoplasty, Bilateral pedicle approach.

4-Thiouridine-Enhanced Peroxidase-Generated Biotinylation of RNA.

Peroxidase-generated proximity labeling is in widespread use to study subcellular proteomes and the protein interaction networks in living cells, but the development of subcellular RNA labeling is limited. APEX-seq has emerged as a new method to study subcellular RNA in living cells, but the labeling of RNA still has room to improve. In this work, we describe 4-thiouridine (s4 U)-enhanced peroxidase-generated biotinylation of RNA with high efficiency. The incorporation of s4 U could introduce additional sites for RNA labeling, enhanced biotinylation was observed on monomer, model oligo RNA and total RNA. Through the s4 U metabolic approach, the in vivo RNA biotinylation efficiency by peroxidase catalysis was also dramatically increased, which will benefit RNA isolation and study for the spatial transcriptome.

Pt(IV) Prodrugs Designed to Embed in Nanotubes of a Polysaccharide for Drug Delivery.

Cisplatin exhibits a sufficient killing effect on cancer cells; however, it damages normal cells simultaneously. Herein, we developed a prodrug delivery system based on branched ß-(1→3)-d-glucan. This natural biomacromolecule-based polysaccharide nanotube was modified with cisplatin embedded in the hollow cavity (BFCP), showing high anticancer activity and low toxicity in vitro. It is a broad-prospect system, which is based on biocompatible nanomaterials loaded with Pt(IV) prodrugs for cancer cell absorption with subsequent release in tumors by utilizing the intracellular reducibility. BFCP chains adopted a nanotube conformation in water, observed by transmission electron microscopy. In comparison to cisplatin, the Pt(IV) prodrugs not only displayed better antitumor properties but also had significant tumor targeting. A potent natural complex conjugated with redox-responsive platinum prodrugs is a significantly efficient tumor drug demonstrated in vitro and in vivo.

A m6 A Sensing Method by Its Impact on the Stability of RNA Double Helix.

N6 -Methyladenosine (m6 A) is one of the most important RNA modifications in epigenetics. The development of detection method for m6 A is limited by its abundance and structure. Although it has been previously reported that its presence has an impact on the complementary pairing of RNA, few assays have been developed using this finding. We used this discovery and designed a detection method based on Cas13a system, which has different fluorescence signals for target RNAs containing m6 A modification and target RNAs without m6 A modification. We verified the fact that the presence of m6 A could cause the instability of dsRNA using the Cas13a system and provided a new direction and strategy for the development of m6 A detection methods in the future.

Keth-seq for transcriptome-wide RNA structure mapping.

RNA secondary structure is critical to RNA regulation and function. We report a new N3-kethoxal reagent that allows fast and reversible labeling of single-stranded guanine bases in live cells. This N3-kethoxal-based chemistry allows efficient RNA labeling under mild conditions and transcriptome-wide RNA secondary structure mapping.

N1-Methyladenosine detection with CRISPR-Cas13a/C2c2.

Recent studies suggested that the widespread presence of N1-methyladenosine (m1A) plays a very important role in environmental stress, ribosome biogenesis and antibiotic resistance. The RNA-guided, RNA-targeting CRISPR Cas13a exhibits a "collateral effect" of promiscuous RNase activity upon target recognition with high sensitivity. Inspired by the advantage of CRISPR Cas13a, we designed a system to detect m1A induced mismatch, providing a rapid, simple and fluorescence-based m1A detection. For A-ssRNA, the Cas13a-based molecular detection platform showed a high fluorescence signal. For m1A-ssRNA, there is an about 90% decline of fluorescence. Moreover, this approach can also be used to quantify m1A in RNAs and applied for the analysis of dynamic m1A demethylation of 28S rRNA with AlkB.

Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

Noisy scattering dynamics in the randomly driven Hénon-Heiles oscillator.

Noisy scattering dynamics in the randomly driven Hénon-Heiles oscillator is investigated when the energy is above the threshold to permit particles to escape from the scattering region. First, some basic simulation procedures are briefly introduced and the fractal exit basins appear to be robust when the bounded noisy excitation is imposed on the oscillator. Second, several key fractal characteristics of the sample basin boundaries, such as the delay-time function and the uncertainty dimension, are estimated from which this oscillator is found to be structurally unstable against the bounded noisy excitation. Moreover, the stable and unstable manifolds of some sample chaotic invariant sets are estimated and illustrated in a special two-dimensional Poincaré section. Lastly, several previous methods are developed to identify three arbitrarily chosen noisy scattering time series of the randomly driven Hénon-Heiles oscillator, from which the quasiperiodic-dominant and the chaotic-dominant dynamical behaviors are distinguished.

Extracting invariable fault features of rotating machines with multi-ICA networks.

This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together. Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.