Isoflavonoids from Astragalus membranaceus Hairy Roots

Chemistry of Natural Compounds -     

Electro-assisted Molecular Assembly Giving Atomic-Scale Catalytic Active-Site Detection

The artificially accurate design of nonmetal electrocatalysts’ active site has been a huge challenge because no pure active species with the specific structure could be strictly controlled by traditional synthetic methods. Species with a multiconfiguration in the catalyst hinder identification of the active site and the subsequent comprehension of the reaction mechanism. We have developed a novel electro-assisted molecular assembly strategy to obtain a pure pentagon ring on perfect graphene avoiding other reconstructed structures. More importantly, the active atom was confirmed by the subtle passivation process as the topmost carbon atom. Recognition of the carbon-defect electrocatalysis reaction mechanism was first downsized to the single-atom scale from the experimental perspective. It is expected that this innovative electro-assisted molecular assembly strategy could be extensively applied in the active structure-controlled synthesis of nonmetal electrocatalysts and verification of the exact active atom.     

Multifunctional fibrous wound dressings for refractory wound healing

Refractory wounds have always been an important issue to healthcare systems, whose healing process is always delayed by multiple factors, including bacterial infections, chronic inflammation, and excessive exudates, etc. Employing multifunctional wound dressings is recognized as an effective strategy to deal with refractory wounds, which has yielded promising outcomes in recent years. Among these advanced wound dressings, fibrous dressings have gained growing attention due to their unique merits. Such wound dressings have demonstrated great potential in delivering theranostic agents, such as antibacterial agents, anti-inflammatory drugs, growth factors, and diagnostic probes, etc., for the purposes of accelerating wound healing. This paper reviews the development of multifunctional fibrous dressings and their applications in treating refractory wounds. The construction approaches of novel fibrous dressing with capabilities of antibacterial, anti-inflammation, exudate management and diagnosis were also introduced. Furthermore, the existing problems and challenges are also discussed briefly.     

A Novel Ternary Composite of Polyurethane/Polyaniline/Nanosilica with Antistatic Property and Excellent Mechanical Strength: Preparation and Mechanism

Chinese Journal of Polymer Science - Antistatic and strength properties are of vital importance for polyurethane rubber used in moving parts of many industrial instruments. Herein, polyurethane was...     

基于快速神经网络架构搜索的鲁棒图像水印网络算法

为解决深度学习在图像水印算法中计算量大且模型冗余的问题，提高图像水印算法在抵抗噪声、旋转和剪裁等攻击时的鲁棒性，提出基于快速神经网络架构搜索（neural architecture search，NAS）的鲁棒图像水印网络算法。通过多项式分布学习快速神经网络架构搜索算法，在预设的搜索空间中搜索最优网络结构，进行图像水印的高效嵌入与鲁棒提取。首先，将子网络中线性连接的全卷积层设置为独立的神经单元结构，并参数化表示结构单元内节点的连接，预先设定结构单元内每个神经元操作的搜索空间；其次，在完成一个批次的数据集训练后，依据神经元操作中的被采样次数和平均损失函数值动态更新概率；最后，重新训练搜索完成的网络。水印网络模型的参数量较原始网络模型缩减了92%以上，大大缩短了模型训练时间。由于搜索得到的网络结构更为紧凑，本文算法具有较高的时间性能和较好的实验效果，在隐藏图像时，对空域信息的依赖比原始网络更少。对改进前后的2个网络进行了大量鲁棒性实验，对比发现，本文算法在CIFAR-10数据集上对抵抗椒盐噪声和旋转、移除像素行（列）等攻击优势显著；在ImageNet数据集上对抵抗椒盐高斯噪声、旋转、中值滤波、高斯滤波、JPEG压缩、裁剪等攻击优势显著，特别是对随机移除行（列）和椒盐噪声有较强的鲁棒性。     

基于级联PPMgLN晶体的双倍/三倍频双波长激光器

本文演示了紧凑的绿色和近红外双色连续波激光光源,其发射波长分别为516 nm和775 nm。设计并制造了级联的周期性极化掺镁铌酸锂晶体,用于同时转换通信波长的二次谐波(SHG)和三次谐波(THG),可以在相同温度下获得绿色和近红外激光的输出。通过建立一个单程激光测量系统,在2 W泵浦功率下获得516 nm的0.15 mW绿光和775 nm的1.19 mW的光,晶体温度控制在30.8 ℃。实验结果将为单激光器泵浦的紧凑型双波长共线激光器提供重要的案例。     

概率语言多属性群决策方法及其在新型智慧城市市民获得感评价中的应用

针对公众参与的语言信息多属性群决策问题，研究了考虑参与者满意度的概率语言多属性群决策方法。首先，根据参与者的语言评价信息确定并规范化概率语言决策矩阵。然后，对大群体进行共识分析，由最大化参与者群体的满意度构建线性规划模型，确定参与者群组的权重；构造正、负理想方案的评价向量，构建多目标规划模型，用拉格朗日乘子法求解属性权重；定义各方案的加权贴近度，并以此对方案进行排序和优选。最后，通过新型智慧城市市民获得感评价案例验证了模型的可行性和有效性。     

Lower Bounds for the Robustness of Multilevel Coherence

International Journal of Theoretical Physics - Quantum coherence, coming from quantum superposition, occupies a significant position in the field of physics. We put forward a lower bound of the...     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.