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Remote sensing is widely used for lake-water-quality monitoring, but the inversion of the total nitrogen (TN) and total phosphorus (TP) of rivers and non-optical parameters is still a difficult problem. The use of high spatial and temporal resolution multispectral imagery combined with machine learning techniques is an effective solution for this difficulty. Three machine learning methods based on support vector regression (SVR), neural network (NN) and random forest (RF) were used to invert TN and TP using actual water-quality measurement data and Sentine-2 remote-sensing images, and analyzed the factors influencing water quality in terms of pollutant emissions and land use. The results show that RF performs the best in both TN (R2 = 0.800, RMSE = 0.640, MSE = 0.400, MAE = 0.480) and TP (R2 = 0.830, RMSE = 0.033, MSE = 0.001, MAE = 0.022) inversion models, and that the optimal selection of feature variables improves model performance. The TN and TP concentrations in the Minjiang River Meishan Water Function Development Zone were the highest in the downstream section and in 2018. Analysis of the factors influencing water quality shows that pollution sources and amounts were closely related to land-use types, and land use in riparian zones at different spatial scales had different degrees of impact on water quality.

A time and covariance threshold triggered optimal maneuver planning method is proposed for orbital rendezvous using angles-only navigation (AON). In the context of Yamanaka-Ankersen orbital relative motion equations, the square root unscented Kalman filter (SRUKF) AON algorithm is developed to compute the relative state estimations from a low-volume/mass, power saving, and low-cost optical/infrared camera’s observations. Multi-impulsive Hill guidance law is employed in closed-loop linear covariance analysis model, based on which the quantitative relative position robustness and relative velocity robustness index are defined. By balancing fuel consumption, relative position robustness, and relative velocity robustness, we developed a time and covariance threshold triggered two-level optimal maneuver planning method, showing how these results correlate to past methods and missions and how they could potentially influence future ones. Numerical simulation proved that it is feasible to control the spacecraft with a two-line element- (TLE-) level uncertain, 34.6% of range, initial relative state to a 100 m v-bar relative station keeping point, at where the trajectory dispersion reduces to 3.5% of range, under a 30% data gap per revolution on account of the eclipse. Comparing with the traditional time triggered maneuver planning method, the final relative position accuracy is improved by one order and the relative trajectory robustness and collision probability are obviously improved and reduced, respectively.

A time and covariance threshold triggered optimal maneuver planning method is proposed for orbital rendezvous using angles-only navigation (AON). In the context of Yamanaka-Ankersen orbital relative motion equations, the square root unscented Kalman filter (SRUKF) AON algorithm is developed to compute the relative state estimations from a low-volume/mass, power saving, and low-cost optical/infrared camera’s observations. Multi-impulsive Hill guidance law is employed in closed-loop linear covariance analysis model, based on which the quantitative relative position robustness and relative velocity robustness index are defined. By balancing fuel consumption, relative position robustness, and relative velocity robustness, we developed a time and covariance threshold triggered two-level optimal maneuver planning method, showing how these results correlate to past methods and missions and how they could potentially influence future ones. Numerical simulation proved that it is feasible to control the spacecraft with a two-line element- (TLE-) level uncertain, 34.6% of range, initial relative state to a 100 m v-bar relative station keeping point, at where the trajectory dispersion reduces to 3.5% of range, under a 30% data gap per revolution on account of the eclipse. Comparing with the traditional time triggered maneuver planning method, the final relative position accuracy is improved by one order and the relative trajectory robustness and collision probability are obviously improved and reduced, respectively. [ABSTRACT FROM AUTHOR]

The nervous system of the silkworm is vital for the development of organisms. It achieves and maintains normal life activities by regulating the function of the organs and all kinds of physiological processes in the silkworm. BmApontic (BmApt), as an important bZIP transcription factor, is required for the formation of pigmentation in the silkworm. However, the function of BmApt in the development of the nervous system of the silkworm remains unclear. Here, we showed that amino acid sequence of BmApt was evolutionarily conserved in its Myb/SANT motif and basic DNA binding domain. BmApt was expressed in the nervous system at the embryonic stage. Knockdown of Bmapt by RNA interference resulted in abnormal development of axons. Moreover, the expression of BmnetrinA, BmnetrinB and Bmfrazzled was decreased in the Bmapt knockdown embryos. These results demonstrate that BmApt controls neurodevelopment by activating the expression of Bmnetrin and Bmfrazzled.

In this paper, Li storage in P-doped borophene nanosheet was stimulated by Density Functional Theory (DFT). Without destroying the nanosheet structure, borophene doped with P atom possessed high binding energy of 3.42 eV. The electronic properties, binding energy, capacity and open-circuit voltage of P-doped borophene were calculated. These results demonstrated that P-doping has a positive effect on the Li storage of borophene nanosheet. Besides, the maximum adsorption number of Li atoms in P-doped borophene is 18, accompanied with an ultra-high theoretical capacity of 1732 mAh/g. [ABSTRACT FROM AUTHOR]