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.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Multiple households energy consumption forecasting using consistent modeling with privacy preservation

Traditional data-driven energy consumption forecasting models, including machine learning and deep learning methods, showed outstanding performance in terms of forecasting accuracy and efficiency. The superior performances are based on enough training data samples. Moreover, the derived forecasting model is only applicable to the training dataset and usually is applied to specific household. In real-world smart city development, a centralized forecasting model is required to model and forecasting energy consumption patterns for multiple households, whereas the traditional data-driven forecasting approaches may become invalid. A consistent model is demanded in this scenario modeling multiple households’ energy consumption patterns. Additionally, privacy issues are also highly concerned in such scenarios. Accurate energy consumption forecasting with privacy preservations becomes a key point for the state-of-art research. In this study, we adopt an innovative privacy-preserving structure that combines deep learning and federated learning. Under the premise of guaranteeing forecasting accuracy and privacy preservation, this structure can achieve the forecasting of various household energy consumption with a consistent model that simultaneously forecast multiple household energy consumption data by transmission control protocol.     

Output feedback adaptive RISE control for uncertain nonlinear systems

In this article, committed to extending the robust integral of the sign of the error (RISE) feedback control to the working condition of output feedback, a novel output feedback controller with a continuously bounded control input which combines the adaptive control and integral robust feedback will be proposed for trajectory tracking of a family of nonlinear systems subject to modeling uncertainties. A novel adaptive state observer (ASO) with disturbance rejection performance is creatively constructed to derive real-time estimation of the unmeasured state signals. Moreover, a projection-type adaption law is integrated to handle parameter uncertainties and an integral robust term is employed to deal with external disturbances. It is shown that asymptotic estimation performance and meanwhile asymptotic tracking result can eventually be derived. Simulation validations are implemented to demonstrate the high tracking performance of the presented controller. Notably, the synthesized control algorithm can be readily extended to the Euler–Lagrange systems. Typically, it can be extended to practical electromechanical equipment such as three-dimensional vector forming robots to improve the real-time forming accuracy.     

Extended sliding mode observer based robust adaptive backstepping controller for electro-hydraulic servo system: Theory and experiment

This paper firstly presents an extended sliding mode observer (ESMO) for the electro-hydraulic servo systems (EHSSs) to deal with nonlinear factors like the external disturbance, parameter uncertainties as well as unmodeled characteristics in the EHSS. The model for the EHSS is established by taking these nonlinear factors into consideration and then the statespace representation is obtained. According to the state space, the ESMO for the EHSS is presented, the equivalence principle is properly utilized to simplify the ESMO and some saturation functions are employed to eliminate high frequency interferences caused by the chattering phenomenon. Based on estimated values from the ESMO, a robust adaptive backstepping controller (RABC) is presented in detail with taking some parameter uncertainties into consideration to further improve the tracking performance. The proposed controller has the following advantages: (1) utilizing the ESMO to cope with these nonlinear factors; (2) parameter online adaptive laws are employed in the RABC design to further improve the tracking performance. In order to verify the performance of the proposed controller, an experiment bench was established. Two sine waves reference signal (one amplitude 0.01 m, 1 Hz; the other 0.015 m, 2 Hz) are employed to verify the performance of the controller. Comparative experimental results show that: (1) the tracking performance of the proposed controller is better than that of a DOs based BC, a BC and a PI controller; (2) estimation values from the ESMO contains fewer noise than two conventional disturbance observers (DOs), which will decrease the control input ripples.     

Defect‐Laden MoSe2 Quantum Dots Made by Turbulent Shear Mixing as Enhanced Electrocatalysts

A high density of edge sites and other defects can significantly improve the catalytic activity of layered 2D materials. Herein, this study demonstrates a novel top‐down strategy to maximize catalytic edge sites of MoSe₂ by breaking up bulk MoSe₂ into quantum dots (QDs) via “turbulent shear mixing” (TSM). The ultrasmall size of the MoSe₂ QDs provides a high fraction of atoms in reactive edge sites, thus significantly improving the catalytic activities. The violent TSM further introduces abundant defects as additional active sites for electrocatalytic reactions. These edge‐proliferated and defect‐laden MoSe₂ QDs are found to be efficient electrocatalysts for the hydrogen evolution reaction, and useful as counter electrodes in dye‐sensitized solar cells. The work provides a new paradigm for creating edge‐proliferated and defect‐rich QDs from bulk layered materials.     

Mixed Nb<Subscript>2</Subscript>O<Subscript>5</Subscript>:MoO<Subscript>3</Subscript> (95:5 and 85:15) thin films and their properties for electrochromic device applications

Nb₂O₅:MoO₃ (95:5 and 85:15) thin films were deposited onto glass and fluorine doped tin oxide coated glass substrates at 100 and 300 °C by RF magnetron sputtering technique. The physical and electrochromic properties of the films were studied. XRD result reveals that deposited films were amorphous. The XPS study confirms the compositional purity and the presence of Nb⁵⁺ and Mo⁶⁺ in the deposited film. Surface morphological study shows platelet like features of deposited film. The average transmittance of the film is varied between 91 and 85 %. Photoluminescence study exhibits three characteristic emission peaks and confirms the better optical quality of deposited film. Raman spectra show the LO–TO splitting of Nb–O stretching of the deposited film. Electrochromic behavior of the deposited films characterized by cyclic voltammetry using 0.5 M LiClO₄·PC and 0.5 M H₂SO₄ electrolyte solutions show all the films are having better reversibility and reproducibility in their electrochemical analysis.     

志高小区变压器结构噪声分析与控制

为解决志高小区变压器噪声扰民问题,通过对该小区地下室干式变压器分布的研究,并结合对变压器噪声传播机理的分析,得出由变压器振动引起的结构噪声是扰民的主要原因。采取安装隔振装置等隔振措施,有效地降低了变压器噪声对居民的影响,并通过对居民室及变压器隔振前后的振动加速度进行测量,同一测点振动级降低均在10 d B以上,同一测点的加速度值降低60%以上,基本解决了由变压器结构噪声引起的噪声扰民问题。     

SiC量子点荧光材料光谱特性影响因素的研究

通过腐蚀法制备SiC量子点荧光标记材料,以硝酸和氢氟酸为腐蚀剂,通过超声空化破碎分散及超重力场层析剪裁,得到SiC量子点水相溶液,研究了制备工艺参数对光学特性的影响,结果表明,降酸清洗次数、超声时长对量子点光致发光峰值(荧光强度)的影响最为显著,而腐蚀剂成分及配比、超重力系数在一定程度上影响了其光致发光强度及发射光波长,同时腐蚀剂成分中氢氟酸含量的增加使得光致发光峰值位置发生红移,而超声时长及超重力系数的增加使得SiC量子点光致发光峰值位置发生蓝移。并对SiC颗粒腐蚀过程相关机制也进行了探讨。