自媒体时代下高校和谐师生关系的构建

伴随着新媒体时代的全面来临,各类智能终端设备进驻千家万户,人们的生活方式和思维方式已受到了极大的影响和改变。本文以自媒体时代为背景,分析了当前高校大学生目前使用自媒体媒介的现状,结合自媒体媒介的优缺点,探讨了当前高校师生关系存在的问题,并根据目前存在的问题,提出了构建自媒体时代和谐师生关系的几条对策。     

传统教育思想融入应用型大学通识教育发展的意义及路径探究

中国传统教育思想内涵深厚、源远流长,依然需要今天的教育研究者不断发现其现代价值,并运用到当代教育的改革发展实践中。本文探究了中国传统教育思想对通识教育发展的意义,分析了当代应用型大学通识教育的缺失及其原因,并应用中国传统教育理念对通识教育的目标、课程体系的完整性及课程设计中合作学习的设计三个方面提出了发展建议。     

面向区域新业态的地方高校金属材料工程专业改造升级路径研究

本文从适应地方新业态的金属材料工程专业改造和升级路径出发,探索对接产业需求的人才培养模式,形成与信息技术深度融合兼有学科交叉特点的课程体系,探索具有新工科特色的专业人才培养质量评价体系,以期实现地方应用型高校传统工科专业的改造升级和跨越式发展。     

Development of a space cold atom clock

Atomic clocks with cold atoms play important roles in the field of fundamental physics as well as primary frequency standards. Operating such cold atom clocks in space paves the way for further exploration in fundamental physics, for example dark matter and general relativity. We developed a space cold atom clock (SCAC), which was launched into orbit with the Space Lab TG-2 in 2016. Before it deorbited with TG-2 in 2019, the SCAC had been working continuously for almost 3 years. During the period in orbit, many scientific experiments and engineering tests were performed. In this article, we summarize the principle, development and in-orbit results. These works provide the basis for construction of a space-borne time-frequency system in deep space.     

Dynamics and manipulation of ferroelectric domain walls in bismuth ferrite thin films

Ferroelectric domain walls differ from domains not only in their crystalline and discrete symmetry, but also in their electronic, magnetic, and mechanical properties. Although domain walls provide a degree of freedom to regulate the physical properties at the nanoscale, the relatively lower controllability prevents their practical applications in nano-devices. In this work, with the advantages of 3D domain configuration detection based on piezoresponse force microscopy, we find that the mobility of three types of domain walls (tail-to-tail, head-to-tail, head-to-head) in (001) BiFeO₃ films varies with the applied electrical field. Under low voltages, head-to-tail domain walls are more mobile than other domain walls, while, under high voltages, tail-to-tail domain walls become rather active and possess relatively long average lengths. This is due to the high nucleation energy and relatively low growth energy for charged domain walls. Finally, we demonstrate the manipulation of domain walls through successive electric writings, resulting in well-aligned conduction paths as designed, paving the way for their application in advanced spintronic, memory and communication nano-devices.     

Paving the road toward the use of β-Fe2O3 in solar water splitting: Raman identification,phase transformation and strategies for phase stabilization

Although β-Fe₂O₃ has a high theoretical solar-to-hydrogen efficiency because of its narrow band gap, the study of β-Fe₂O₃ photoanodes for water splitting is elusive as a result of their metastable nature. Raman identification of β-Fe₂O₃ is theoretically and experimentally investigated in this study for the first time, thus clarifying the debate about its Raman spectrum in the literature. Phase transformation of β-Fe₂O₃ to α-Fe₂O₃ was found to potentially take place under laser and electron irradiation as well as annealing. Herein, phase transformation of β-Fe₂O₃ to α-Fe₂O₃ was inhibited by introduction of Zr doping, and β-Fe₂O₃ was found to withstand a higher annealing temperature without any phase transformation. The solar water splitting photocurrent of the Zr-doped β-Fe₂O₃ photoanode was increased by 500% compared to that of the pure β-Fe₂O₃ photoanode. Additionally, Zr-doped β-Fe₂O₃ exhibited very good stability during the process of solar water splitting. These results indicate that by improving its thermal stability, metastable β-Fe₂O₃ film is a promising photoanode for solar water splitting.     

Predicting future dynamics from short-term time series using an Anticipated Learning Machine

Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.