具有分子取向的拉伸高聚物(PET)中~(13)C—NMR化学位移值的估算

本文从已知的聚对苯二甲酸乙二醇酯(PET)的X-射线原子坐标和化学位移张量主值出发,计算了拉伸的PET样品,相对于外磁场不同放置方位下的~(13)C-NMR谱峰的化学位移值,很好地解释了实验结果,并得到了拉伸条件下,高聚物中分于取向的信息。     

THE APPLICATION OF HIGH RESOLUTION SOLID STATE NMR IN ADSORPTION SYSTEMS

High resolution solid state NMR techniques(such as MAS,CP/MAS andCRAMPS)were employed to study the nature of organic molecules adsorbed onporous solids. A magic angle spinning system was achieved for sealed samples with a spinning speed from 2KHz to 4.2KHz.Using this technique,high resolution ~1HMAS spectra of organic molecules and H_2S adsorbed on charcoal were obtained.EXperimental results suggest that for high coverage of adsorbed organic molecules,the spectral lines were resolved very well.But for low coverage,the spectrallines could not be separated completely.As the organic molecules condensed in     

自由电子激光光导的数值模拟与分析

从一维自由电子激光理论出发,唯象地考虑衍射效应,计算和分析了光导效应的特点和规律;针对曙光一号装置的自由电子激光参数,利用二维理论对光场剖面、填充因子、光场半径等进行了数值模拟;根据计算结果分析和讨论了影响光导的因素和实验条件。     

Two-parameter estimation with three-mode NOON state in a symmetric triple-well potential

We propose a scheme to realize two-parameter estimation via Bose–Einstein condensates confined in a symmetric triple-well potential. The three-mode NOON state is prepared adiabatically as the initial state. The two parameters to be estimated are the phase differences between the wells. The sensitivity of this estimation scheme is studied by comparing quantum and classical Fisher information matrices. As a result, we find an optimal particle number measurement method. Moreover, the precision of this estimation scheme means that the Heisenberg scaling behaves under the optimal measurement.     

Impact of Extracellular Polymeric Substance in the Inactivation of Harmful Algae by Ag2O/g-C3N4 under Visible Light

Photocatalysis has attracted much attention as an emerging algae removal technology, but the inactivation performance is inevitably affected by the extracellular polymeric substance (EPS) produced by algae. In this study, a photocatalyst (Ag₂O/g-C₃N₄) with efficient algae inactivation is adopted to investigate the interactions with EPS, and the impact of EPS on photocatalytic algae removal is studied. The results show that EPS can adhere to the surface of Ag₂O/g-C₃N₄ by electrostatic force. The interaction with EPS decreases the surface zeta potential of the Ag₂O/g-C₃N₄ from 7.71 to −22.3 mV with the increase in EPS concentration, and the maximum ratio of particle size increases from 825 to 1281 nm. In addition, the interaction with EPS inhibits the release of Ag⁺ in Ag₂O/g-C₃N₄ by half, thus, the toxicity of metal ions will be alleviated. Meanwhile, EPS can also be degraded by Ag₂O/g-C₃N₄, indicating that EPS can work as a radical scavenger to protect the algae cells. Without the protection of EPS, 97.8% of algae cells are inactivated after 5 h photocatalysis. Therefore, more attention should be given to the interaction between EPS and photocatalyst to promote the design and application of the photocatalytic.     

Maximum Entropy Approach to Reliability of Multi-Component Systems with Non-Repairable or Repairable Components

The degradation and recovery processes are multi-scale phenomena in many physical, engineering, biological, and social systems, and determine the aging of the entire system. Therefore, understanding the interplay between the two processes at the component level is the key to evaluate the reliability of the system. Based on the principle of maximum entropy, an approach is proposed to model and infer the processes at the component level, and is applied to repairable and non-repairable systems. By incorporating the reliability block diagram, this approach allows for integrating the information of network connectivity and statistical moments to infer the hazard or recovery rates of the degradation or recovery processes. The overall approach is demonstrated with numerical examples.     

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).     

FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion

Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects’ features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.     

数字束流位置探测器通道自动增益校准方法的研究与实现

在现有硬件基础上,基于BPM测量准确度的需求,在自制的电子学FPGA芯片内,通过Verilog语言实现了一种数字BPM采样数据增益自动校准的设计。首先介绍了自动增益校准模块的系统总体设计;然后对模块的实现方法做了详细说明,设计并搭建了ADC数据自动增益校准测试平台以验证自动增益较准模块的功能;最后介绍了该设计在BPM通道标定中的应用。实验结果表明,该方法可以实现4通道增益一致,使ADC采样后的数据幅度相同,有效解决了由通道增益不一致引起的测量偏差,以及工程应用中ADC数据幅度校准工作量大且难于操作的问题,将在BPM系统通道自动标定中发挥重要作用。     

基于学科素养论证分析2020年高考全国卷Ⅰ物理压轴题

2020年高考物理试题依托高考评价体系,结合物理学科特点,考试内容以学科素养考查为导向,即以物理观念、科学思维、科学探究、科学态度与责任的物理学科素养为导向,注重对核心物理概念和规律的考查,引导学生夯实学习发展的基础,促进中学重视培养学生的基础知识、基本技能以及提升学生的基本素养.基于物理学科素养,论证分析2020年高...