Parent compounds of cyclopentadienyltitanium substituted heteropolytungstates with Keggin structure,An[(CpTi)XW11O39]·xH2O(A=Me4N,K;X=P,Si,Co;Cp=η5-C5H5) were synthesized in aqueous phase.By allowing parent hete...Parent compounds of cyclopentadienyltitanium substituted heteropolytungstates with Keggin structure,An[(CpTi)XW11O39]·xH2O(A=Me4N,K;X=P,Si,Co;Cp=η5-C5H5) were synthesized in aqueous phase.By allowing parent heteropoly compounds to react with protonated 8-quinolinol,the title supermolecular compounds(C9H8NO)mAn[(CpTi)XW11O39]·xH2O(A=Me4N,H;X=P,Si,Co) were synthesized.The title compounds were characterized by means of elementary analysis,IR,UV,1H NMR,XRD and TG-DSC.The results indicate that the title compounds are new heteropoly compounds,and there is a charge transfer interaction between the organic cation and heteropoly anion.The results obtained from thermal analysis show that QCpTiPW,QCpTiSiW and QCpTiCoW begin to decompose at 212.4,194.2 and 171.2 ℃,respectively.The results obtained from antibacterial test reveal that QCpTiSiW has the best antibacterial activity,and the MIC values of QCpTiSiW against Escherichia coli and Staphylococcus aurous are 64.0 and 0.500 μg·mL-1,respectively.展开更多
为精准识别与分类不同花期杭白菊,满足自动化采摘要求,该研究提出一种基于改进YOLOv8s的杭白菊检测模型-YOLOv8s-RDL。首先,该研究将颈部网络(neck)的C2f(faster implementation of CSP bottleneck with 2 convolutions)模块替换为RCS-O...为精准识别与分类不同花期杭白菊,满足自动化采摘要求,该研究提出一种基于改进YOLOv8s的杭白菊检测模型-YOLOv8s-RDL。首先,该研究将颈部网络(neck)的C2f(faster implementation of CSP bottleneck with 2 convolutions)模块替换为RCS-OSA(one-shot aggregation of reparameterized convolution based on channel shuffle)模块,以提升骨干网络(backbone)特征融合效率;其次,将检测头更换为DyHead(dynamic head),并融合DCNv3(deformable convolutional networks v3),借助多头自注意力机制增强目标检测头的表达能力;最后,采用LAMP(layer-adaptive magnitude-based pruning)通道剪枝算法减少参数量,降低模型复杂度。试验结果表明,YOLOv8s-RDL模型在菊米和胎菊的花期分类中平均精度分别达到96.3%和97.7%,相较于YOLOv8s模型,分别提升了3.8和1.5个百分点,同时权重文件大小较YOLOv8s减小了6 MB。该研究引入TIDE(toolkit for identifying detection and segmentation errors)评估指标,结果显示,YOLOv8s-RDL模型分类错误和背景检测错误相较YOLOv8s模型分别降低0.55和1.26。该研究为杭白菊分花期自动化采摘提供了理论依据和技术支撑。展开更多
基金supported by the chemical materials institute China academy of engineering physics,the doctoral innovation research assistance program of science and technology review
文摘Parent compounds of cyclopentadienyltitanium substituted heteropolytungstates with Keggin structure,An[(CpTi)XW11O39]·xH2O(A=Me4N,K;X=P,Si,Co;Cp=η5-C5H5) were synthesized in aqueous phase.By allowing parent heteropoly compounds to react with protonated 8-quinolinol,the title supermolecular compounds(C9H8NO)mAn[(CpTi)XW11O39]·xH2O(A=Me4N,H;X=P,Si,Co) were synthesized.The title compounds were characterized by means of elementary analysis,IR,UV,1H NMR,XRD and TG-DSC.The results indicate that the title compounds are new heteropoly compounds,and there is a charge transfer interaction between the organic cation and heteropoly anion.The results obtained from thermal analysis show that QCpTiPW,QCpTiSiW and QCpTiCoW begin to decompose at 212.4,194.2 and 171.2 ℃,respectively.The results obtained from antibacterial test reveal that QCpTiSiW has the best antibacterial activity,and the MIC values of QCpTiSiW against Escherichia coli and Staphylococcus aurous are 64.0 and 0.500 μg·mL-1,respectively.
文摘为精准识别与分类不同花期杭白菊,满足自动化采摘要求,该研究提出一种基于改进YOLOv8s的杭白菊检测模型-YOLOv8s-RDL。首先,该研究将颈部网络(neck)的C2f(faster implementation of CSP bottleneck with 2 convolutions)模块替换为RCS-OSA(one-shot aggregation of reparameterized convolution based on channel shuffle)模块,以提升骨干网络(backbone)特征融合效率;其次,将检测头更换为DyHead(dynamic head),并融合DCNv3(deformable convolutional networks v3),借助多头自注意力机制增强目标检测头的表达能力;最后,采用LAMP(layer-adaptive magnitude-based pruning)通道剪枝算法减少参数量,降低模型复杂度。试验结果表明,YOLOv8s-RDL模型在菊米和胎菊的花期分类中平均精度分别达到96.3%和97.7%,相较于YOLOv8s模型,分别提升了3.8和1.5个百分点,同时权重文件大小较YOLOv8s减小了6 MB。该研究引入TIDE(toolkit for identifying detection and segmentation errors)评估指标,结果显示,YOLOv8s-RDL模型分类错误和背景检测错误相较YOLOv8s模型分别降低0.55和1.26。该研究为杭白菊分花期自动化采摘提供了理论依据和技术支撑。
