Projected Pupil Plane Pattern: an alternative LGS wavefront sensing technique

We have analysed and simulated a novel alternative Laser Guide Star (LGS) configuration termed Projected Pupil Plane Pattern (PPPP), including wavefront sensing and the reconstruction method. A key advantage of this method is that a collimated beam is launched through the telescope primary mirror, therefore the wavefront measurements do not suffer from the effects of focal anisoplanatism. A detailed simulation including the upward wave optics propagation, return path imaging, and linearized wavefront reconstruction has been presented. The conclusions that we draw from the simulation include the optimum pixel number across the pupilN = 32, the optimum number of Zernike modes (which is 78), propagation altitudes h1 = 10 km and h2 = 20 km for Rayleigh scattered returns, and the choice for the laser beam modulation (Gaussian beam). We also investigate the effects of turbulence profiles with multiple layers and find that it does not reduce PPPP performance as long as the turbulence layers are below h1. A signal-to-noise ratio analysis has been given when photon and read noise are introduced. Finally, we compare the PPPP performance with a conventional Shack–Hartmann Wavefront Sensor in an open loop, using Rayleigh LGS or sodium LGS, for 4-m and 10-m telescopes, respectively. For this purpose, we use a full Monte Carlo end-to-end AO simulation tool, Soapy. From these results, we confirm that PPPP does not suffer from focus anisoplanatism

Laboratory demonstration of an alternative laser guide stars wavefront sensing technique—projected pupil plane pattern

Adaptive optics (AO) is widely used in optical/near-infrared telescopes to remove the effects of atmospheric distortion, and laser guide stars (LGSs) are commonly used to ease the requirement for a bright, natural reference source close to the scientific target in an AO system. However, focus anisoplanatism renders single LGS AO useless for the next generation of extremely large telescopes. Here, we describe proof-of-concept experimental demonstrations of a LGS alternative configuration, which is free of focus anisoplanatism, with the corresponding wavefront sensing and reconstruction method, termed projected pupil plane pattern (PPPP). This laboratory experiment is a critical milestone between the simulation and on-sky experiment, for demonstrating the feasibility of PPPP technique and understanding technical details, such as extracting the signal and calibrating the system. Three major processes of PPPP are included in this laboratory experiment: the upward propagation, return path, and reconstruction process. From the experimental results, it has been confirmed that the PPPP signal is generated during the upward propagation and the return path is a reimaging process whose effect can be neglected (if the images of the backscattered patterns are binned to a certain size). Two calibration methods are used: the theoretical calibration is used for the wavefront measurement, and the measured calibration is used for closed-loop control. From both the wavefront measurement and closed-loop results, we show that PPPP achieves equivalent performance to a Shack–Hartmann wavefront sensor

Design and synthesis of naphthalimide group-bearing thioglycosides as novel β-N-acetylhexosaminidases inhibitors

GH20 human β-N-acetylhexosaminidases (hsHex) and GH84 human O-GlcNAcase (hOGA) are involved in numerous pathological processes and emerged as promising targets for drug discovery. Based on the catalytic mechanism and structure of the catalytic domains of these β-N-acetylhexosaminidases, a series of novel naphthalimide moiety-bearing thioglycosides with different flexible linkers were designed, and their inhibitory potency against hsHexB and hOGA was evaluated. The strongest potency was found for compound 15j (Ki = 0.91 µM against hsHexB; Ki > 100 µM against hOGA) and compound 15b (Ki = 3.76 µM against hOGA; Ki = 30.42 µM against hsHexB), which also exhibited significant selectivity between these two enzymes. Besides, inhibitors 15j and 15b exhibited an inverse binding patterns in docking studies. The determined structure–activity relationship as well as the established binding models provide the direction for further structure optimizations and the development of specific β-N-acetylhexosaminidase inhibitors

Developing Novel Rice Yield Index Using UAV Remote Sensing Imagery Fusion Technology

Efficient and quick yield prediction is of great significance for ensuring world food security and crop breeding research. The rapid development of unmanned aerial vehicle (UAV) technology makes it more timely and accurate to monitor crops by remote sensing. The objective of this study was to explore the method of developing a novel yield index (YI) with wide adaptability for yield prediction by fusing vegetation indices (VIs), color indices (CIs), and texture indices (TIs) from UAV-based imagery. Six field experiments with 24 varieties of rice and 21 fertilization methods were carried out in three experimental stations in 2019 and 2020. The multispectral and RGB images of the rice canopy collected by the UAV platform were used to rebuild six new VIs and TIs. The performance of VI-based YI (MAPE = 13.98%) developed by quadratic nonlinear regression at the maturity stage was better than other stages, and outperformed that of CI-based (MAPE = 22.21%) and TI-based (MAPE = 18.60%). Then six VIs, six CIs, and six TIs were fused to build YI by multiple linear regression and random forest models. Compared with heading stage (R2 = 0.78, MAPE = 9.72%) and all stage (R2 = 0.59, MAPE = 22.21%), the best performance of YI was developed by random forest with fusing VIs + CIs + TIs at maturity stage (R2 = 0.84, MAPE = 7.86%). Our findings suggest that the novel YI proposed in this study has great potential in crop yield monitoring

Multiple attribute decision making model and application to food safety risk evaluation

<div><p>Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the proposed model provides a reliable, accurate method for multiple attribute decision making.</p></div

The choice decision matrix of supplier (A, B, C).

<p>The choice decision matrix of supplier (A, B, C).</p

Developing Novel Rice Yield Index Using UAV Remote Sensing Imagery Fusion Technology

Efficient and quick yield prediction is of great significance for ensuring world food security and crop breeding research. The rapid development of unmanned aerial vehicle (UAV) technology makes it more timely and accurate to monitor crops by remote sensing. The objective of this study was to explore the method of developing a novel yield index (YI) with wide adaptability for yield prediction by fusing vegetation indices (VIs), color indices (CIs), and texture indices (TIs) from UAV-based imagery. Six field experiments with 24 varieties of rice and 21 fertilization methods were carried out in three experimental stations in 2019 and 2020. The multispectral and RGB images of the rice canopy collected by the UAV platform were used to rebuild six new VIs and TIs. The performance of VI-based YI (MAPE = 13.98%) developed by quadratic nonlinear regression at the maturity stage was better than other stages, and outperformed that of CI-based (MAPE = 22.21%) and TI-based (MAPE = 18.60%). Then six VIs, six CIs, and six TIs were fused to build YI by multiple linear regression and random forest models. Compared with heading stage (R2 = 0.78, MAPE = 9.72%) and all stage (R2 = 0.59, MAPE = 22.21%), the best performance of YI was developed by random forest with fusing VIs + CIs + TIs at maturity stage (R2 = 0.84, MAPE = 7.86%). Our findings suggest that the novel YI proposed in this study has great potential in crop yield monitoring

Evaluation index for food safety risk.

<p>Evaluation index for food safety risk.</p