Large-area two-dimensional(2D)materials,such as graphene,MoS_(2),WS_(2),h-BN,black phosphorus,and MXenes,are a class of advanced materials with many possible applications.Different applications need different substrat...Large-area two-dimensional(2D)materials,such as graphene,MoS_(2),WS_(2),h-BN,black phosphorus,and MXenes,are a class of advanced materials with many possible applications.Different applications need different substrates,and each substrate may need a different way of transferring the 2D material onto it.Problems such as local stress concentrations,an uneven surface tension,inconsistent adhesion,mechanical damage and contamination during the transfer can adversely affect the quality and properties of the transferred material.Therefore,how to improve the integrity,flatness and cleanness of large area 2D materials is a challenge.In order to achieve high-quality transfer,the main concern is to control the interface adhesion between the substrate,the 2D material and the transfer medium.This review focuses on this topic,and finally,in order to promote the industrial use of large area 2D materials,provides a recipe for this transfer process based on the requirements of the application,and points out the current problems and directions for future development.展开更多
The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-n...The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.展开更多
目的研究长春市35~79岁汉族人群维生素D受体基因BsmⅠ、FokⅠ位点多态性的分布特征及其与腰椎骨密度(bone mineral density,BMD)的相关性,分析不同基因型对骨代谢的调节与影响,同时研究血清TGF-β、TRACP水平与腰椎BMD的相关性,为骨质...目的研究长春市35~79岁汉族人群维生素D受体基因BsmⅠ、FokⅠ位点多态性的分布特征及其与腰椎骨密度(bone mineral density,BMD)的相关性,分析不同基因型对骨代谢的调节与影响,同时研究血清TGF-β、TRACP水平与腰椎BMD的相关性,为骨质疏松预防、早期诊断和治疗提供分子生物学依据。方法应用Hologic Discovery WA型骨密度仪检测腰椎正位(L1-L4)BMD;采用聚合酶链反应-限制性片段长度多态性(PCR-RFLP)分析BsmⅠ和FokⅠ位点多态性;采用酶联免疫吸附分析法检测血清TGF-β、TRACP水平;采用SPSS 23.0软件进行统计分析。结果在500名汉族人群中,BsmⅠ位点基因型以bb型为主,占80.2%,Bb型占15.2%,BB型占4.6%;FokⅠ位点ff基因型占18.6%,Ff型占45.8%,FF型占35.6%。BsmⅠ位点各基因型骨质疏松组与非骨质疏松组分布特征组间差异无统计学意义(P>0.05);FokⅠ位点ff基因型骨质疏松组所占比例高于非骨质疏松组,差异有统计学意义(P<0.05)。BsmⅠ位点bb基因型男性、女性BMD值均低于Bb型和BB型,但差异无统计学意义(P>0.05);FokⅠ位点ff基因型男性、女性BMD值均低于Ff型和FF型,差异有统计学意义(P<0.05)。血清TGF-β水平骨质疏松组显著低于非骨质疏松组,而TRACP水平骨质疏松组高于非骨质疏松组,组间差异有统计学意义(P<0.05)。结论本研究500名汉族人群中,BsmⅠ位点以bb基因型为主,占80.2%,各基因型骨密度值组间差异无统计学意义;FokⅠ位点ff型BMD值低于Ff型和FF型,骨质疏松组ff基因型所占比例高于非骨质疏松组,差异有统计学意义,提示ff基因型可能是骨质疏松发生的危险因素;骨质疏松组血清TGF-β水平显著低于非骨质疏松组,而TRACP水平高于非骨质疏松组,差异有统计学意义,表明TGF-β、TRACP是评价骨代谢状态的良好指标。展开更多
基金the National Key R&D Program of China(2022YFA1505200)the National Natural Science Foundation of China(22472140,22021001)the Fundamental Research Funds for the Central Universities(20720210017 and 20720210009)。
文摘Large-area two-dimensional(2D)materials,such as graphene,MoS_(2),WS_(2),h-BN,black phosphorus,and MXenes,are a class of advanced materials with many possible applications.Different applications need different substrates,and each substrate may need a different way of transferring the 2D material onto it.Problems such as local stress concentrations,an uneven surface tension,inconsistent adhesion,mechanical damage and contamination during the transfer can adversely affect the quality and properties of the transferred material.Therefore,how to improve the integrity,flatness and cleanness of large area 2D materials is a challenge.In order to achieve high-quality transfer,the main concern is to control the interface adhesion between the substrate,the 2D material and the transfer medium.This review focuses on this topic,and finally,in order to promote the industrial use of large area 2D materials,provides a recipe for this transfer process based on the requirements of the application,and points out the current problems and directions for future development.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation 2022M720419 to provide fund for conducting experiments。
文摘The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.
