Phase equilibria and microstructure development in Mg-rich Mg-Gd-Sr alloys: Experiments and CALPHAD assessment

Mg-Sr alloys are promising to fabricate orthopedic implants. The alloying of rare earth elements such as Gd may improve the comprehensive mechanical properties of Mg-Sr alloys. The information on the phase diagram and the microstructure development are required to design chemical composition and microstructure of Gd alloyed Mg-Sr alloys. The phase equilibria and the microstructure development in Mg-rich Mg-Gd-Sr alloys (Gd, Sr < 30 at. %) are experimentally investigated via phase identification, chemical analysis, and microstructure observation with respect to the annealed ternary alloys. The onset temperatures of liquid formation are measured by differential scanning calorimetry. A thermodynamic database of the Mg-rich Mg–Gd–Sr ternary system is developed for the first time via CALPHAD (CALculation of PHAse Diagram) approach assisted by First-Principles calculations. The thermodynamic calculations with the developed database enable a well reproduction of the experimental findings and the physical-metallurgical understanding of the microstructure formation in solidification and annealing.     

Adaptive adversarial prototyping network for few-shot prototypical translation

Translating multiple real-world source images to a single prototypical image is a challenging problem. Notably, these source images belong to unseen categories that did not exist during model training. We address this problem by proposing an adaptive adversarial prototype network (AAPN) and enhancing existing one-shot classification techniques. To overcome the limitations that traditional works cannot extract samples from novel categories, our method tends to solve the image translation task of unseen categories through a meta-learner. We train the model in an adversarial learning manner and introduce a style encoder to guide the model with an initial target style. The encoded style latent code enhances the performance of the network with conditional target style images. The AAPN outperforms the state-of-the-art methods in one-shot classification of brand logo dataset and achieves the competitive accuracy in the traffic sign dataset. Additionally, our model improves the visual quality of the reconstructed prototypes in unseen categories. Based on the qualitative and quantitative analysis, the effectiveness of our model for few-shot classification and generation is demonstrated.     

Multi-layer and Multi-scale feature aggregation for DIBR-Synthesized image quality assessment

Depth-Image-Based Rendering (DIBR) is one of the main fundamental techniques for generating new viewpoints in 3D video applications such as multi-viewpoint video (MVV), free viewpoint video (FVV) and virtual reality (VR). Due to the imperfections of color images, depth maps or texture restoration techniques, several types of distortions occur in synthesized views. However, most of related works evaluated the quality of DIBR-synthesized views by only detecting a specific type of distortion, such as stretching, black holes, blurring, etc., which were unable to accurately evaluate the quality of DIBR-synthesized views. In this paper, a new no-reference image quality assessment method is proposed to evaluate the quality of DIBR-synthesized images by combining multi-layer and multi-scale features of images. To be specific, the distortions introduced by different stages of virtual viewpoint synthesis are first analyzed, and then multi-layer and multi-scale features are extracted to estimate the degree of texture and structure distortions. As a result, individual quality scores associated with two types of distortions (e.g., structural distortion and texture distortion) are aggregated to an overall image quality. Experimental results on two publicly available DIBR datasets show that the method has better performance than the state-of-the-art models.Index Terms: image quality assessment, DIBR-synthesized image, distortion correction, BIQA.     

Thermodynamic assessment of RuO4 oxide

RuO₄ oxide appears much less stable than RuO₂(s) in the Ru–O binary system with a melting point close to room temperature and a certain propensity to vaporize or decompose at low temperatures. Ab initio simulations in the framework of density functional theory (DFT) on RuO₄(s) are performed to analyze the cubic and monoclinic structures and to evaluate the heat capacities at low temperatures. Then, a critical evaluation of thermodynamic data from calorimetry and vapor pressure determinations - was carried out coupled with ab-initio calculations to propose new thermodynamic data: the entropy.S° (RuO_4, s, cubic, 298K) = 132.7 J·K⁻¹mol⁻¹ and formation enthalpy.Δ_fH° (RuO₄, s, cubic, 298K) = −252.4 ± 5.5 kJ mol⁻¹.     

结合简短可链接环签名的智能合约选举方案

目前一些电子选举协议利用可链接环签名或一次性环签名来保护投票者的隐私,并防止重复投票的情况发生,但签名大小随投票人数的增大而增大,而简短可链接环签名大小始终固定不变,但已有简短可链接环签名算法效率较低。针对这些问题,利用累加器和基于知识证明的签名,构造新的高效的简短可链接环签名,结合匿名地址及星际文件系统,提出一种新的智能合约选举方案,分别设计了选举的创建、投票、计票等阶段的操作。对方案的不可伪造性、可链接性、匿名性、隐私性、公开验证性以及运行效率进行了证明和分析。最后进行了实验仿真评估,结果显示,随着投票者人数的增大,该方案选票的签名大小及Gas消耗保持不变且较少,生成时间及验证时间增长缓慢且较少。     

Adaptive fault-tolerant control of switched nonlinear systems based on tuning function method

This paper solves the problem of adaptive fault-tolerant control of uncertain switched nonlinear systems with unknown parameters and actuator failures. Based on the multi-Lyapunov function method, a novel adaptive switched controller with tuning function is designed using the backstepping procedure. Meanwhile, a switched fault-tolerant adaptation mechanism is also given to compensate the fault efficiency. Furthermore, it is certificated that all the signals of the closed-loop system are uniformly bounded and the tracking error restrain to a small neighbor. Finally, the simulation results verify the feasibility of the proposed method.     

Stabilization of uncertain networked control systems with actuator saturation and probabilistic cyberattacks

This paper investigates the stabilization of uncertain networked control systems (NCSs) with actuator saturation and cyberattacks. The cyberattacks are governed by a set of independent random variables satisfying Bernoulli distribution. To relieve the network bandwidth load effectively, an event-triggered communication strategy is proposed. By employing Lyapunov stability theory and stochastic analysis techniques, a stability criterion is obtained for the system with actuator saturation and cyberattacks. Moreover, the desired controller gain is derived by solving some matrix inequalities. Finally, the validity and applicability of the criteria are verified through numerical examples.