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A higher-order robust correlation Kalman filtering approach is presented to achieve attitude estimation for satellites with unknown modeling errors. A robust correlation Kalman filter (RCKF) is preliminarily derived by using the sequence orthogonal principle. To improve its performance further, a higher-order sigma version of the RCKF is designed by incorporating a novel sigma point generation algorithm. This modified filter can capture the third and the fourth central moment's information of the system posteriori probability density function. It is proved that the proposed filter achieves better estimation accuracy and robustness. The effectiveness of the developed filter is demonstrated by simulation results with it applied to the satellite attitude estimation problem. [ABSTRACT FROM AUTHOR]

Background modeling is a popular issue in computer vision society during the past few decades. We analyze the usual pixel-level approach and build an adaptive background mixture model with spatio-temporal samples called STS. These samples are described based on a joint domain-range formulation and the pixel is classified with a weight marginal probability density function. On-line update including proximal sample selection, parameter learning, and neighbor update are used to regulate the model to adapt to varying scenes. We perform a bootstrap modeling experiment, and results show that our method outperforms other widely adopted techniques which use only intensity or region description as pixel feature with higher F - measure on most datasets. And our method is a matched rival to combination methods which use both pixel intensity and region description. [ABSTRACT FROM AUTHOR]

The construction of semiconductor heterojunctions is regarded as an efficient strategy for promoting photogenerated carrier transfer and enhancing photocatalytic performance. [1] Moreover, the in-situ self-assembly method of the main catalyst and the cocatalyst is believed to overcome the shortcomings of the traditional surface loading method and provide a more efficient hydrogen evolution reaction capability. In this work, Zn-Cd-Mo-S quaternary metal sulfide mesoporous nanospheres were generated by in-situ self-assembly via a one-step mild hydrothermal method. A photocatalyst with a double-heterojunction synergistic structure and a large specific surface area was constructed to form a high-speed charge transfer channel as a mechanism to promote photocatalytic hydrogen production. The best quaternary catalyst ZCM 5 S has a hydrogen evolution yield of 23.32 mmol h−1 g−1, which is 53 times and 11 times that of the corresponding binary (CdS) and ternary (ZnCdS) metal sulfides, respectively, and still has high stability within 20 h. Furthermore, the ZCM 5 S gets an apparent quantum efficiency (AQE) of 6.9 % at 420 nm. Through further analysis, such as photoluminescence (PL) and photoelectrochemical, etc., the enhanced photocatalytic performance of ZCM 5 S can be attributed to the synergistic effect of the heterojunction between ZnS and CdS and the heterojunction between ZnCdS and MoS 2. In addition, the Pt-like structure of molybdenum sulfide cocatalyst can lead to efficient photoinduced charge separation and transfer. Meanwhile, the mesoporous structure endows the material with abundant reactive sites and increases the catalytic reaction efficiency. The unique in situ construction method of this work also provides a reference for the design and synthesis of heterojunction quaternary catalysts for green energy conversion. [Display omitted] • The mesoporous structure increases the reactive site area. • The double heterojunction synergy facilitates exciton transfer. • Quaternary sulfides outperform their parent materials. • A method for in situ syntheses of quaternary metal sulfides is proposed. • The main and co-catalyst one-step method is beneficial and feasible. [ABSTRACT FROM AUTHOR]

• Ionic liquid and deep eutectic solvent are compared as alternative CO 2 absorbent. • Multilevel screening of ionic liquid and deep eutectic solvent are performed. • Top three ionic liquids and deep eutectic solvents are screened from a large space. • CO 2 absorption experiments are performed to validate the top deep eutectic solvent. • QC calculations are performed to gain microscopic insight into CO 2 absorption. Because of the high energy consumption and volatility of organic amine solvents in conventional CO 2 capture processes, ionic liquids (ILs) and deep eutectic solvents (DESs) have been widely expected as alternative CO 2 absorbents. In this work, promising ILs and DESs are identified through a comparative, multi-level absorbent screening method proposed for the first time from a large space of cation–anion combinations and potential DES components, respectively. Important physical properties (namely melting point, viscosity, and toxicity) of potential ILs and salt components of DESs are estimated by a recently-reported deep learning model. Key thermodynamic properties including physically-based absorption and desorption potentials as well as eutectic behaviors between DES component combinations are predicted by the COSMO-RS model. Five EHS (environment, health, and safety) related properties of non-salt components of DESs are assessed by the VEGA platform. From above, the top three ILs and DESs are identified from 5,440 and 38,389 candidates, respectively. The CO 2 capture processes based on the retained ILs and DESs are simulated in Aspen Plus in rate-based mode, identifying 1-methyl-1-propylpyrrolidinium bromide: methylimidazole (1:2) as the absorbent with the highest process performance. The experiments shows that the molality-based Henry constant of CO 2 in DES is as low as 1.32, validating the competitive absorption performance of this potential DES. Finally, quantum chemistry calculations are conducted to unveil the microscopic absorption mechanism of this DES. [ABSTRACT FROM AUTHOR]

In high-resolution remote sensing images, the problems of large scale variation, large intra-class variance of background, small variability and irregularity of arrangement between different targets always exist in remote sensing images, making the modeling between targets and background more difficult and the target detection task more difficult. However, general target detection methods mainly use convolutional layers of different scales to enhance the target's perceptual domain and fuse different scale features to solve the scale variation problem, without considering the other two problems prevalent in remote sensing scenes of earth observation. In order to solve the above two problems, this paper proposes a semantic correction and focusing network (SCFNet) from the perspective of modeling the relationship between background and target and target to target. The network consists of two core modules the Local Correction Module (LCM) calculates the similarity of local features through the global features of the image to correct the local features and exclude the non-relevant The Non-local Focus Module (NLFM) enhances the recognition of target features by obtaining the non-local dependencies and the corrected local features from the LCM. To demonstrate the effectiveness and robustness of our proposed method, we conducted extensive experiments on two publicly popular large remote sensing multi-target detection datasets, namely DIOR and DOTA. the experimental results show that our SCFNet achieves best-in-class performance and significant accuracy improvement on the datasets. [ABSTRACT FROM AUTHOR]

• Principal pigment components analysis indicated that 4 major anthocyanins and 46 carotenoids were in four tulip cultivars. • Candidate genes involved in the petal pigmentation were identified by transcriptome profiles. • Analyzed the function of the MBW complex in regulating tulip petal color formation. Pigmentation of various components leads to different colors in tulip flowers. To understand the molecular basis of the petal coloration in tulip, integrative analyses of the pigment components and transcriptome profiles were conducted on four tulip cultivars with different petal colors. A total of four major anthocyanins and 46 carotenoids were identified. The anthocyanin cyanidin 3-O-galactoside showed markedly higher abundances in the B cultivar than in the other varieties, and among the 46 kinds of carotenoids, (E/Z)-phytoene, violaxanthin myristate and violaxanthin palmitate were the major components. The RNA-seq and qRT-PCR results indicated that the pigment accumulation was linked to the expression of genes involved in the anthocyanin and carotenoid biosynthesis pathways. Yeast two-hybrid (Y2H) assays showed the interaction between different regulator factors in tulip MYB-bHLH-WD40 (MBW) complexes. Co-expression analyses of genes were performed, which include anthocyanin and carotenoid biosynthesis genes and transcription factors involved in MYB, bHLH, WRKY, AUX-IAA and MADS-box. The co-expression network and related analysis provide a basis for the discovery of color regulatory factors. Taken together, our study sheds light on the anthocyanin and carotenoid synthesis pathways and candidate regulatory transcription factors underlying flower coloration and shows the potential of flower breeding or pigments engineering in tulips. [ABSTRACT FROM AUTHOR]