Strain-hardening (the increase of flow stress with plastic strain) is the most important phenomenon in the mechanical behaviour of engineering alloys because it ensures that flow is delocalized, enhances tensile ductility and inhibits catastrophic mechanical failure 1,2. Metallic glasses (MGs) lack the crystallinity of conventional engineering alloys, and some of their properties-such as higher yield stress and elastic strain limit 3-are greatly improved relative to their crystalline counterparts. MGs can have high fracture toughness and have the highest known 'damage tolerance' (defined as the product of yield stress and fracture toughness) 4 among all structural materials. However, the promise of MGs for structural applications is largely thwarted by the fact that they show strain-softening, instead of strain-hardening; this leads to extreme localization of plastic flow in shear bands, and is associated with early catastrophic failure in tension. Although rejuvenation of an MG (raising its energy to values that are typical of glass formation at a higher cooling rate) lowers its yield stress, which might enable strain-hardening 5 , it is unclear whether sufficient rejuvenation can be achieved in bulk samples while retaining their glassy structure. Here we show that plastic deformation under triaxial compression at room temperature can rejuvenate bulk MG samples sufficiently to enable strain-hardening through a mechanism that has not been previously observed in the metallic state. This transformed behaviour suppresses shear-banding in bulk samples in normal uniaxial (tensile or compressive) tests,

Since 1979, China has made tremendous progress in its transformation to a socialist market economy. As part of this process, China's financial system has evolved to one characterised by a high degree of marketization. At the same time, China today faces new challenges to growth and development, particularly from the necessity of restructuring its economy to focus increasingly on innovation and away from government led investment and low wage labour. In the context of digital financial services, China has been a late mover but this has changed dramatically in the past five years, to the point today where China is one of the major centres for digital financial services and financial technology ("fintech"). Looking forward, China needs to provide an appropriate regulatory basis for the future development of digital financial services and fintech, balancing growth and innovation with financial stability. China today is exhibiting signs of a last mover advantage in this respect that may see it leaping regulatory developments elsewhere.

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

The real-time and high-precision trajectory prediction of a moving object is a core technology in the field of aerospace engineering. The real-time monitoring and tracking technology are also significant guarantees of aerospace equipment. A dynamic trajectory prediction method called grey dynamic filter (GDF) which combines the dynamic measurement theory and grey system theory is proposed. GDF can use coordinates of the current period to extrapolate coordinates of the following period. At meantime, GDF can also keep the instantaneity of measured coordinates by the metabolism model. In this paper the optimal model length of GDF is firstly selected to improve the prediction accuracy. Then the simulation for uniformly accelerated motion and variably accelerated motion is conducted. The simulation results indicate that the mean composite position error of GDF prediction is one-fifth to that of Kalman filter (KF). By using a spacecraft landing experiment, the prediction accuracy of GDF is compared with the KF method and the primitive grey method (GM). The results show that the motion trajectory of spacecraft predicted by GDF is much closer to actual trajectory than the other two methods. The mean composite position error calculated by GDF is one-eighth to KF and one-fifth to GM respectively.

In order to improve the precision of a binocular vision measurement system, an effective binocular vision measurement method, named geometrical approximation, is proposed. This method can optimize the measurement results by geometrical approximation operation based on the principles of optimization theory and spatial geometry. To evaluate the properties of the proposed method, both simulative and practical experiments are carried out. The influence of image noise and focal length error on measurement results is discussed. The results show that measurement performance of the proposed method is manifested well. Besides, the proposed method is also compared with Bundle adjustment and least squares method in a practical experiment. The experiment results indicate that the average error, calculated by using the proposed method, is 0.076 mm less than Bundle adjustment’s 0.085 mm, and only half of the least squares method’s 0.146 mm. At the meantime, the proposed method enjoys a high level of computational efficiency when compared to Bundle adjustment. Since no nonlinear iteration optimization is involved, this method can be applied readily to real time on-line measurements.

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