Negative thermal expansion: Mechanisms and materials

Negative thermal expansion (NTE) of materials is an intriguing phenomenon challenging the concept of traditional lattice dynamics and of importance for a variety of applications. Progresses in this field develop markedly and update continuously our knowledge on the NTE behavior of materials. In this article, we review the most recent understandings on the underlying mechanisms (anharmonic phonon vibration, magnetovolume effect, ferroelectrorestriction and charge transfer) of thermal shrinkage and the development of NTE materials under each mechanism from both the theoretical and experimental aspects. Besides the low frequency optical phonons which are usually accepted as the origins of NTE in framework structures, NTE driven by acoustic phonons and the interplay between anisotropic elasticity and phonons are stressed. Based on the data documented, some problems affecting applications of NTE materials are discussed and strategies for discovering and design novel framework structured NET materials are also presented.     

A Study of the Dynamics of Formation of Plasmoids in the Gatchina Discharge

Technical Physics - The results of studies of the Gatchina discharge conducted at the Harbin University of Technology (China) have been presented. The Gatchina discharge is mainly used to create an...     

基于维纳滤波的同轴电缆脉冲信号传输畸变补偿研究

由于同轴电缆的低通传输特性,脉冲信号在同轴电缆中传输时不可避免地会出现畸变,并且畸变程度会随着脉冲信号频率、带宽以及传输距离的增加而增大。创新性地将图像处理领域中的一种图像复原方法——维纳滤波法应用于脉冲信号同轴电缆传输畸变补偿,仅利用同轴电缆的S参数和输出信号即可完成输入信号的重构。并以10 m同轴电缆为对象,采用该方法分别对双指数脉冲信号、高斯调制脉冲信号、调制方波信号进行传输畸变补偿。结果表明：对于不同样式的信号,该方法均具有优异的补偿性能;并且,与工程上常用的衰减补偿法相比,该方法不仅补偿精度高,还具有高的计算效率,在同轴电缆脉冲信号传输畸变补偿中具有很好的实用价值。     

任意阶高运算恒定性分抗逼近电路—标度格型级联双口网络

标度拓展经典负半阶分抗逼近电路,可实现具有任意分数阶微积算子运算功能的分抗逼近电路,但牺牲了运算恒定性.从电路网络的角度分析具有恒定运算性能的负半阶Carlson分形格分抗逼近电路.根据标度分形格分抗逼近电路的等效无源双口网络,探讨该双口网络右侧端口的运算有效性,设计具有高运算恒定性的任意阶标度分形格分抗逼近电路.结合...     

向心加速度公式的教具制作及其教学研究——基于Tracker和Phyphox软件的定量探究

从教具制作、定量实验探究和教学启示等三方面来探究匀速圆周运动的向心加速度公式,即利用自制教具探究向心加速度αc与圆周运动半径r和角速度ω之间的定量关系.实验发现,结果非常好地符合理论预期.最后讨论了该教具所蕴含的教学启示,可以将其设计成适合高中物理教学的验证性、探究性实验及课外科创活动,从而培养学生的科学探究能力、科学...     

A visual uncertainty analytics approach for weather forecast similarity measurement based on fuzzy clustering

Journal of Visualization - Forecast calibration methods based on historical similar atmospheric state are effective means weather forecast accuracy. Conventional approaches search similar forecasts...     

α-decay study of 218Ac and 221Th in 40Ar+186W reaction

In this study,²¹⁸Ac and ²²¹Th nuclides were produced via the heavy-ion induced fusion evaporation reaction ⁴⁰Ar+¹⁸⁶W.Their decay properties were studied with the help of the gas-filled recoil spectrometer SHANS and a digital data acquisition system.The cross section ratio between ²²²Pa and ²¹⁸Ac was extracted experimentally,withmeasured value 0.69(9).Two new possible α decay branches to ²²¹Th are suggested.The valence neutron configurations for the daughter ²¹⁷Ra are discussed in terms of the hindrance factors.     

SensorAware: visual analysis of both static and mobile sensor information

Journal of Visualization - Sensor networks composed of static and mobile sensors are applicable for situation monitoring. In this paper, we propose SensorAware, an interactive system for...     

Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.     

用信号处理技术及传播理论还原雷声频谱

以往关于雷声的研究都限于观测点雷声频域范围的探讨, 由于噪音和衰减因素的影响, 观测点的雷声频谱有很大失真. 针对在兰州地区观测的一次雷暴过程中的雷声信号, 利用数字信号处理技术,由观测点处的雷声音频信号得到频率谱,并对雷声频谱进行噪音分析; 然后结合声音在大气中传播的衰减理论, 分析了不同大气环境对声吸收的影响, 并推算出了声源附近的雷声频率谱. 关键词： 雷声频谱 信号处理 传播理论 峰值频率