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Density functional theory-based characterization of crystalline tungsten oxide has been well established. Nonetheless, there remains a partial gap in theoretical studies concerning the electrochemical characterization of amorphous tungsten oxide. The electronic structure and diffusion kinetics of amorphous tungsten oxide require a systematic theoretical study. Therefore, we employed second-generation Car–Parrinello molecular dynamics simulations and the density functional theory with HSE06 exchange–correlation hybrid functional to investigate the electronic properties and lithium kinetics of amorphous tungsten oxide (α-WOx, x = 3, 2.5, 2) models. The precise electronic properties of these structures were computed using the HSE06 hybrid functions. The diffusion properties of lithium were determined in the range of 1 × 10−7 to 5 × 10−7 cm2/s by ab initio molecular dynamics. The computational findings provide a critical atomic-scale understanding and contribute to the development of tungsten oxide-based electrochromic devices for practical applications. [ABSTRACT FROM AUTHOR]

Light field cameras are unsuitable for further acquisition of high-quality images due to their small depth of field, insufficient spatial resolution, and poor imaging quality. To address these issues, we proposed a novel four-focal-square microlens and light field system. A square aspheric microlens array with four orthogonal focal lengths was designed, in which the aperture of a single lens was 100 μm. The square arrangement improves pixel utilization, the four focal lengths increase the depth of field, and the aspheric improves image quality. The simulations demonstrate pixel utilization rates exceeding 90%, depth-of-field ranges 6.57 times that of a single focal length, and image quality is significantly improved. We have provided a potential solution for improving the depth of field and image quality of the light field imaging system. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle remote sensing image matching techniques are crucial for urban planning and environmental monitoring. However, existing feature matching methods have limitations due to the lack of effective extraction of key diverse features, which hinders the improvement of matching accuracy for diverse ground scenes. To address the above limitations, we propose a remote sensing image feature-matching method using optimally regularized nonlinear diffusion model. Specifically, we first use the accelerated-KAZE (AKAZE) algorithm framework to detect and characterize features within a nonlinear scale space. To improve the adaptation of diffusion to the local image structure, we propose the introduction of a novel Charbonnier regularized nonlinear diffusion model for constructing the diffusion coefficients. We then propose to combine Brute force with the K -nearest neighbor algorithm to eliminate invalid matches. Lastly, we modified the sampling approach of the random sample consensus (RANSAC) algorithm. Instead, we achieve iterative dataset purification using the progressive sample consensus algorithm, complemented by the implementation of a termination criterion for the sampling process. Experimental results show that our method achieves an average matching precision of 82.8%, 80.2%, 82.9%, and 91.2% when the test set undergoes scale, rotation angle, noise blurring, and luminance variations, respectively, outperforming the existing feature matching methods. [ABSTRACT FROM AUTHOR]

The echinoderm nervous system has been studied as a model for understanding the evolution of the chordate nervous system. Neuronal cells are essential groups that release a 'cocktail' of messenger molecules providing a spectrum of biological actions in the nervous system. Among echinoderms, most evidence on neuronal cell types has been obtained from starfish and sea urchin. In sea cucumbers, most research has focused on the location of neuronal cells, whereas their transcriptional features have rarely been investigated. Here, we observed the ultrastructure of neuronal cells in the sea cucumber, Apostichopus japonicus. The transcriptional profile of neuronal cells from the circumoral nerve ring (CNR) was investigated using single-cell RNA sequencing (scRNA-seq), and a total of six neuronal cell types were identified. 26 neuropeptide precursor genes (NPPs) and 28 G-protein-coupled receptors (GPCR) were expressed in the six neuronal cell types, comprising five NPP/NP-GPCR pairs. Unsupervised pseudotime analysis of neuronal cells showed their different differentiation status. We also located the neuronal cells in the CNR by immunofluorescence (IF) and identified the potential hub genes of key cell populations. This broad resource serves as a valuable support in the development of cell-specific markers for accurate cell-type identification in sea cucumbers. It also contributes to facilitating comparison across species, providing a deeper understanding of the evolutionary processes of neuronal cells., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)