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| 1 | Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications显示文摘Line-of-sight(LoS)probability prediction is critical to the performance optimization of wireless communication systems.However,it is challenging to predict the LoS probability of air-to-ground(A2G)communication scenarios,because the altitude of unmanned aerial vehicles(UAVs)or other aircraft varies from dozens of meters to several kilometers.This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios.Before estimating the model parameters,we design a K-nearest neighbor(KNN)based strategy to classify LoS and non-LoS(NLoS)paths.Then,a two-layer back propagation neural network(BPNN)based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude.Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing(RT)data,the measurement data,and the results obtained using the standard models.Our model can also provide wider applicable altitudes than other LoS probability models,and thus can be applied to different altitudes under various A2G scenarios. | Minghui PANG Qiuming ZHU Zhipeng LIN Fei BAI Yue TIAN Zhuo LI Xiaomin CHEN | 2022 | Frontiers of Information Technology & Electronic Engineering2022，23，9： | 0 |
| 2 | Single-cell dissection,hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis显示文摘Ulcerative colitis(UC)develops as a result of complex interactions between various cell types in the mucosal microenvironment.In this study,we aim to elucidate the pathogenesis of ulcerative colitis at the single-cell level and unveil its clinical significance.Using single-cell RNA sequencing and high-dimensional weighted gene co-expres-sion network analysis,we identify a subpopulation of plasma cells(PCs)with significantly increased infiltration in UC colonic mucosa,characterized by pronounced oxidative stress.Combining 10 machine learning approaches,we find that the PC oxidative stress genes accurately distinguish diseased mucosa from normal mucosa(independent external testing AUC=0.991,sensitivity=0.986,specificity=0.909).Using MCPcounter and non-negative matrix factorization,we identify the association between PC oxidative stress genes and immune cell infiltration as well as patient heterogeneity.Spatial transcriptome data is used to verify the infiltration of oxidatively stressed PCs in colitis.Finally,we develop a gene-immune convolutional neural network deep learning model to diagnose UC mucosa in different cohorts(independent external testing AUC=0.984,sensitivity=95.9%,specificity=100%).Our work sheds light on the key pathogenic cell subpopulations in UC and is essential for the development of future clinical disease diagnostic tools. | Shaocong Mo Xin Shen Baoxiang Huang Yulin Wang Lingxi Lin Qiuming Chen Meilin Weng Takehito Sugasawa Wenchao Gu Yoshito Tsushima Takahito Nakajima | 2023 | Acta Biochimica et Biophysica Sinica2023，55，11： | 0 |
| 3 | Time of flight improved thermally grown oxide thickness measurement with terahertz spectroscopy显示文摘As a nondestructive testing technique,terahertz time-domain spectroscopy technology is commonly used to measure the thickness of ceramic coat in thermal barrier coatings(TBCs).However,the invisibility of ceramic/thermally grown oxide(TGO)reflective wave leads to the measurement failure of natural growth TGO whose thickness is below 10μm in TBCs.To detect and monitor TGO in the emergence stage,a time of flight(TOF)improved TGO thickness measurement method is proposed.A simulative investigation on propagation characteristics of terahertz shows the linear relationship between TGO thickness and phase shift of feature wave.The accurate TOF increment could be acquired from wavelet soft threshold and cross-correlation function with negative effect reduction of environmental noise and system oscillation.Thus,the TGO thickness could be obtained efficiently from the TOF increment of the monitor area with different heating times.The averaged error of 1.61μm in experimental results demonstrates the highly accurate and robust measurement of the proposed method,making it attractive for condition monitoring and life prediction of TBCs. | Zhenghao ZHANG Yi HUANG Shuncong ZHONG Tingling LIN Yujie ZHONG Qiuming ZENG Walter NSENGIYUMVA Yingjie YU Zhike PENG | 2022 | Frontiers of Mechanical Engineering2022，17，4： | 0 |
| 4 | Broadening and enhancing emission of Cr^(3+) simultaneously by co-doping Yb^(3+) in Ga1.4ln0.6SnO5显示文摘Cr^(3+)doped broadband near-infrared(NIR) emitting phosphors are currently the focus of research.Researchers have developed a variety of strategies to achieve broad and strong NIR emission,However,it is a conundrum to simultaneously broaden and enhance the emission of Cr^(3+)with a single strategy,In this work,we solved this problem by co-doping Yb^(3+).Under 452 nm excitation,Ga_(1.4)In_(0.6)SnO_(5)(GISO):0.01Cr^(3+)shows ultrabroadband NIR emission covering 650-1300 nm with a peak of 884 nm,The full width half maximum(FWHM) of the emission is 215 nm and the internal quantum yield(IQY) is 25%.This indicates that the double sites occupation strategy is favorable to achieve ultra-broadband NIR emission.The co-doping of Yb^(3+)can effectively broaden and enhance the emission of Cr^(3+).The FWHM for GISO:0.01Cr^(3+),0.002Yb^(3+)extends to 245 nm,and the IQY increases to 28%.Further increasing the concentration of Yb^(3+)to 0.005,the IQY is lifted to 32%.Finally,a phosphor-co nverted light emitting diode(pc-LED) was prepared by integrating the GISO:0.01Cr^(3+),0.002Yb^(3+)with a blue light chip.Under the current drive of 40 mA,the maximum output power of pc-LED is 4.54 mW,and the photoelectric conversion efficiency is 4.12%.These results indicate that Yb^(3+)ions can simultaneously broaden the emission band and improve the emission efficiency.This work provides an effective strategy for the design of efficient broadband NIR phosphors in the future. | Shuang Zhao Zhongfei Mu Lulu Lou Shuwen Yuan Min Liao Qiuming Lin Daoyun Zhu Fugen Wu | 2023 | Journal of Rare Earths2023，41，12： | 0 |
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