Optical design of the Lyman alpha based beam emission spectroscopy(LAB)diagnostic on the HL-2A tokamak

In this article, we present the optical design of a novel diagnostic on the HL-2 A tokamak, i.e. the20-channel edge Lyman-alpha beam emission spectroscopy, which is a promising solution for edge density turbulence research on tokamaks, as it offers the possibility of density fluctuation measurement with a 3.3 mm spatial resolution while maintains a high temporal resolution of1 μs. The optical path, including the reflective collection optics, the high-dispersion spectrometer, and the linear detector array, is carefully optimized to obtain a good image quality and a high throughput. The maximum root mean square radius of the collection optics is 64 μm.The detected photon flux is estimated to be about 10~(11) photons/s/channel.     

Pricing and personal data collection strategies of online platforms in the face of privacy concerns

Electronic Commerce Research - We consider a platform providing free content for users and earning profit from the sale of advertising. The platform can collect and analyze personal data to...     

颗粒活性炭在饮用水深度处理中的应用

概括介绍了国内外活性炭水处理的发展和应用情况、活性炭深度处理的机理及处理效果。针对当前水源有机污染现状和发展趋势 ,论述了采用活性炭进行饮用水深度处理的必要性 ,给出了颗粒活性炭吸附池设计的参数及要求。最后介绍了北京市城子水厂炭吸附池设计及特点。     

正电子湮没技术在研究合金形状记忆性能中的应用

本文用正电子湮没技术测试了Cu-Zn-Al形状记忆合金100℃母相时效过程中空位的变化。结果发现,随着时效时间的延长,空位浓度不断降低,空位尺寸不断增大。这一结果很好地解释了记忆合金马氏体逆转变温度As的变化规律,从而证明过饱和空位是Cu-Zn-Al形状记忆合金马氏体稳定化的一个主要原因。     

叶盘结构频率转向特征的量化分析研究

研究叶盘结构频率转向的曲线特征,是预测叶盘结构的失谐敏感性及振动局部化程度的重要前提。文章基于叶盘系统的集中参数模型,分析了频率转向曲线特征参数(频率曲线转向处曲率、频率曲线斜率、两频率曲线转向处最小距离)与叶盘系统结构参数(耦合刚度,轮盘质量)之间的内在联系,仿真研究了频率转向曲线特征参数随叶盘系统结构参数的变化规律。研究结果表明:频率曲线转向处曲率越大(或斜率越大),系统失谐敏感性越大;频率转向曲线间隙处附近的系统模态局部化变化剧烈,该区域模态对结构失谐非常敏感。     

中继协同无人机辅助的认知无线电网络安全通信

针对中继协同无人机（UAV）辅助的无线通信网络,提出一种基于认知无线电网络（CRN）的物理层安全通信方案。利用二次发射机协同解码转发中继向目的接收机发送机密消息,将UAV用作移动干扰器发送干扰噪声,以降低窃听者的解码能力。在不影响主用户通信的前提下,通过联合优化UAV的飞行轨迹和发射功率提高系统的平均保密率,采用基于连续凸逼近的算法求解近似凸规划的保密率最大化问题。仿真结果表明,相较于优化功率和优化轨迹这两种传统方案,该方案能够进一步提高CRN系统通信的安全性。     

贵州省气象档案管理系统建设

贵州省气象档案管理系统的研究与开发,解决了贵州省气象信息中心在日常工作中气象档案收集至归档之间的脱节问题,减少了工作量,为档案管理员提供了完整的档案业务功能。它涵盖了档案的收、管、存、用的全过程,实现对各种档案种类进行有效的组织和管理,初步建立了贵州省气象档案的自动化管理,在一定程度上提高了贵州省气象档案管理水平。     

Nested relation extraction with iterative neural network

Most existing researches on relation extraction focus on binary flat relations like BornIn relation between a Person and a Location. But a large portion of objective facts described in natural language are complex, especially in professional documents in fields such as finance and biomedicine that require precise expressions. For example, “the GDP of the United States in 2018 grew 2.9% compared with 2017” describes a growth rate relation between two other relations about the economic index, which is beyond the expressive power of binary flat relations. Thus, we propose the nested relation extraction problem and formulate it as a directed acyclic graph (DAG) structure extraction problem. Then, we propose a solution using the Iterative Neural Network which extracts relations layer by layer. The proposed solution achieves 78.98 and 97.89 F1 scores on two nested relation extraction tasks, namely semantic cause-and-effect relation extraction and formula extraction. Furthermore, we observe that nested relations are usually expressed in long sentences where entities are mentioned repetitively, which makes the annotation difficult and errorprone. Hence, we extend our model to incorporate a mentioninsensitive mode that only requires annotations of relations on entity concepts (instead of exact mentions) while preserving most of its performance. Our mention-insensitive model performs better than the mention sensitive model when the random level in mention selection is higher than 0.3.     

多尺度特征融合U-Net的遥感影像黑臭水体智能检测

研究采用卫星遥感技术获取高分辨率遥感影像水体样本数据集,基于深度卷积神经网络从高分辨遥感影像中提取水体并进行黑臭水体智能监测,提出了一种改进U-Net的黑臭水体检测网络模型(IWDNet)。基于U-Net结构引入跳跃式多尺度特征融合,结合通道注意力机制、卷积注意力模块、通道与空间注意力机制生成不同多尺度特征融合注意力机制(MFFAM)模块进行对比,并引入空洞卷积扩大网络感受野,最终实现黑臭水体的识别检测。实验证明：基于跳跃式多尺度融合与CBAM注意力机制的黑臭水体检测网络(MFFCBAM-IWNet)模型有效提升了识别精度,在高分辨遥感影像水体样本数据集上表现最佳,总体精度达98.56%,Kappa系数达0.978 4。     

Covalent Adaptable Microstructures via Combining Two-Photon Laser Printing and Alkoxyamine Chemistry: Toward Living 3D Microstructures

Manufacturing programmable materials, whose mechanical properties can be adapted on demand, is highly desired for their application in areas ranging from robotics, to biomedicine, or microfluidics. Herein, the inclusion of dynamic and living bonds, such as alkoxyamines, in a printable formulation suitable for two-photon 3D laser printing is exploited. On one hand, taking advantage of the dynamic covalent character of alkoxyamines, the nitroxide exchange reaction is investigated. As a consequence, a reduction of the Young´s Modulus by 50%, is measured by nanoindentation. On the other hand, due to its “living” characteristic, the chain extension becomes possible via nitroxide mediated polymerization. In particular, living nitroxide mediated polymerization of styrene results not only in a dramatic increase of the volume (≈8 times) of the 3D printed microstructure but also an increase of the Young's Modulus by two orders of magnitude (from 14 MPa to 2.7 GPa), while maintaining the shape including fine structural details. Thus, the approach introduces a new dimension by enabling to create microstructures with dynamically tunable size and mechanical properties.