An Advanced Multi-Band Acousto-Optical Radio-Wave Spectrometer with Multi-Channel Frequency Processing for Astrophysical Studies

We present an advanced schematic arrangement of the radio-wave spectrometer with a few parallel optical arms for processing the data flow. This arrangement includes two principal novelties. First of them consists in the proposed design, where each individual optical arm exhibits its original performances providing parallel multi-band observations within a few different scales simultaneously. These optical arms have the beam shapers providing both the needed incident light polarization and apodization to increase the dynamic range. After parallel acousto-optical processing, data flows of all the optical arms are united by the joint CCD matrix on the stage of the combined electronic data processing. The second novelty is in usage of unique wide-aperture bastron-based acousto-optical cell providing one of the best performances at the middle-frequencies (about 500 MHz) in comparison with the other available crystalline materials in this range. Such multi-band capabilities have a number of applications in astrophysical scenarios at different scales: from objects in the distant universe to planetary atmospheres in the Solar system. Thus one yields the united versatile instrument, which provides comprehensive studies of astrophysical objects simultaneously with precise synchronization in various frequency ranges.

Multifunctional aggregates for precise cellular analysis

Precisely analyzing the target materials in living cells can reveal the essence and mystery of life at a deeper level,which will provide reliable theoretical basis for the occurrence,development,treatment and prognosis of major diseases.However,because living cells are in the dynamic process of metabolism,there are several challenges existed in accurate analysis,including subcellular compartment heterogeneity,plasma membrane interface barrier,and cell cycle regulation.In this regard,our group has designed and synthesized a series of multifunctional aggregates by mainly integrating the peptide elements,nucleic acid elements and aggregation elements to overcome the barriers.This article summarizes the latest developments of multifunctional aggregates for precise cell analysis by our group,and systematically introduces them according to different design concepts and targeting dimensions,such as space,efficiency as well as time.We hope this work could contribute to analyzing the biomarkers in cells through constructing the multifunctional aggregates,understanding the operation mechanism of cells,finally inspiring technology breakthroughs in biomedical fields.

Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion

Obtaining high-precision,long-term sequences of vegetation water content(VWC)is of great significance for assessing surface vegetation growth,soil moisture,and fire risk.In recent years,the global navigation satellite system-interferometric reflection(GNSS-IR)has become a new type of remote sensing technology with low cost,all-weather capability,and a high temporal resolution.It has been widely used in the fields of snow depth,sea level,soil moisture content,and vegetation water content.The normalized microwave reflectance index(NMRI)based on GNSS-IR technology has been proven to be effective in monitoring changes in VWC.This paper considers the advantages and disadvantages of remote sensing technology and GNSS-IR technology in estimating VWC.A point-surface fusion method of GNSS-IR and MODIS data based on the GA-BP neural network is proposed to improve the accuracy of VWC estimation.The vegetation index products(NDVI,GPP,LAI)and the NMRI that unified the temporal and spatial resolution were used as the input and output data of the training model,and the GA-BP neural network was used for training and modeling.Finally,a spatially continuous NMRI product was generated.Taking a particular area of the United States as a research object,experiments show that(1)a neural network can realize the effective fusion of GNSS-IR and MODIS products.By comparing the GA-BP neural network,BP neural network,and multiple linear regression(MLR),the three models fusion effect.The results show that the GA-BP neural network has the best modeling effect,and the r and RMSE between the model estimation result and the reference value are 0.778 and 0.0332,respectively;this network is followed by the BP neural network,in which the r and RMSE are 0.746 and 0.0465,respectively.MLR has the poorest effect,with r and RMSE values of 0.500 and 0.0516,respectively.(2)The spatiotem-poral variation in the 16 days/500 m resolution NMRI product obtained by GA-BP neural network fusion is consistent with that in the experimental area.Through the testing of GNSS stations that did not participate in the modeling,the r between the estimated value of the NMRI and the reference value is greater than 0.87,and the RMSE is less than 0.049.Therefore,the method proposed in this paper is optional and effective.The spatially continuous NMRI products obtained by fusion can reflect the changes in VWC in the experimental area more intuitively.