A new quantum group associated with a ‘nonstandard’ braid group representation

A new quantum group is derived from a nonstandard braid group representation by employing the Faddeev-Reshetikhin-Takhtajan constructive method. The classical limit is not a Lie superalgebra, despite relations like x ²–y ²=0. We classify all finite-dimensional irreducible representations of the new Hopf algebra and find only one- and two-dimensional ones.     

超短脉冲激光微束对细胞作用的研究

本文详细地介绍了我们自己设计和安装的一个Nd:YAG超短脉冲激光微束系统,该系统分成二个部分:激光源和显微镜。由电光调Q非稳腔Nd:YAG激光倍频后得到355nm紫外光进入显微镜并由物镜聚焦。同时,这超短脉冲激光微束照射在细胞上并且在细胞上产生一个可以自恢复的小孔。在文中我们还讨论了超短脉冲激光微束与细胞之间作用效应及机理。     

Few-photon routing via chiral light-matter couplings

A quantum router is one of the essential elements in the quantum network. Conventional routers only direct a single photon from one quantum channel into another. Here, we propose a few-photon router. The active element of the router is a single qubit chirally coupled to two independent waveguides simultaneously, where each waveguide mode provides a quantum channel. By introducing the operators of the scatter-free space and the controllable space, the output state of the one-photon and two-photon scattering are derived analytically. It is found that the qubit can direct one and two photons from one port of the incident waveguide to an arbitrarily selected port of the other waveguide with unity, respectively. However, two photons cannot be simultaneously routed to the same port due to the anti-bunch effect.     

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.     

Thermally Evaporated Ag–Au Bimetallic Catalysts for Efficient Electrochemical CO2 Reduction

CO₂ reduction reaction (CO₂RR) has indispensable significance for carbon recycling and renewable energy production. As typical electrochemical catalysts, Au and Ag show relatively high reaction activity and selectivity in CO₂RR. In this study, a series of Ag–Au bimetallic catalysts are designed and synthesized through the thermal evaporation method for efficient yet massive production of electrochemical catalysts. The Ag–Au catalysts show significantly enhanced activity and selectivity in CO₂RR, which is mainly attributed to the increased grain boundaries with well-dispersed single Ag atoms. After the optimization, Au20Ag10 exhibits the best performance with a CO Faraday efficiency of 89% at −0.9 V (vs the reversible hydrogen electrode) with good stability.     

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.     

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.     

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。     

细胞松弛素B阻断微核法鉴定男性个体辐射敏感性的方法初探

初步研究了男性个体辐射敏感性的鉴定方法及标准.采集50名男性志愿者的外周血,分别给予不同剂量X射线照射,采用细胞松弛素B阻断双核法测定微核率(MNF),通过二阶多项拟合法,绘制微核剂量效应选项中心标准曲线,将个人微核剂量效应曲线与标准曲线比对后判断个体辐射敏感性.0.0～2.5 Gy剂量范围内,剂量效应二阶多项拟合的中...