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Camera localisation is an essential task in the field of computer vision. The objective is to determine the precise position and orientation of one newly introduced camera station based on a collection of control images that are geographically referenced. Traditional feature‐based approaches have been found to face difficulties when confronted with the task of localising images that exhibit significant disparities in viewpoint. Modern deep learning approaches, on the contrary, aim to directly regress camera poses from input image content, being holistic to remedy the problem of viewpoint disparities. This paper posits that although deep networks possess the ability to learn robust and invariant visual features, the incorporation of geometry models can provide rigorous constraints in the process of pose estimation. Following the classic structure‐from‐motion (SfM) pipeline, we propose a PL‐Pose framework to perform camera localisation. First, to improve feature correlations for images with large viewpoint disparities, we perform the combination of point and line features based on a deep learning framework and geometric relation of wireframes. Then, a cost function is constructed using the combined point and line features in order to impose constraints on the bundle adjustment process. Finally, the camera pose parameters and 3D points are estimated through an iterative optimisation process. We verify the accuracy of the PL‐Pose approach through the utilisation of two datasets, that is, the publicly available S3DIS dataset and the self‐collected dataset CUMTB_Campus. The experimental results demonstrate that in both indoor and outdoor scenes, our PL‐Pose method can achieve localisation errors of less than 1 m for 82% of the test points. In contrast, the other four comparison methods yield a best result of merely 72%. Meanwhile, the PL‐Pose method can successfully obtain the camera pose parameters in all the scenes with small or large viewpoint disparities, indicating its good stability and adaptability. [ABSTRACT FROM AUTHOR]

Silver is usually loaded on nano‐titanium dioxide (TiO2) through photodeposition method to enhance visible‐light catalytic functions for environment purification. However, little is known about how the toxicity changes after silver doping and how the physicochemical properties of loaded components affect nanocomposite toxicity. In this study, Ag‐TiO2 with different sizes and contents of silver particles were obtained by controlling photodeposition time (PDT) and silver addition amount. Pro‐inflammatory and pro‐fibrogenic responses of these photocatalysts were evaluated in male C57BL/6J murine lung. As a result, silver was well assembled on TiO2, promoting visible‐light catalytic activity. Notably, the size of silver particles increased with PDT. Meanwhile, toxicity results showed that pure TiO2 (P25) mainly caused neutrophil infiltration, while 2 wt/wt% silver‐loaded TiO2 recruited more types of inflammatory cells in the lung. Both of them caused the increase of proinflammatory cytokines while decreasing the anti‐inflammatory cytokine in bronchoalveolar lavage fluid. However, 2 wt/wt% silver doping also accelerated the lung pro‐fibrogenic response of photocatalysts in the subacute phase from evidence of collagen deposition and hydroxyproline concentrations. Mechanistically, the overactivation of TGFBR2 receptors in TGF‐β/smads pathways by silver‐loaded TiO2 rather than pure TiO2 may be the reason why silver‐loaded TiO2 can promote pro‐fibrogenic effect response. Intriguingly, the increased toxicity caused by silver doping can be rescued by increasing the size of the loaded silver or decreasing the silver amount. These results may be important for the new understanding of the toxicity of TiO2‐based photocatalysts. [ABSTRACT FROM AUTHOR]

Transient memristor, which can be decomposed in water after completing the designed task, has shown great potentials in data security, implantable medical devices, and eco‐friendly electronics. However, the further development of transient memristors is hindered by lacking practical approaches to deal with the dilemma between stability and degradability of the device. Here, using Ag‐doped fibroin film as a switching medium, an unconventional right‐angled‐like resistive switching behavior is observed. Based on this novel resistive switching characteristic, a high‐performance transient memristor is designed and fabricated. A self‐assembled Ag nanoclusters model is proposed to interpret this unique electron‐transport property. The self‐assembled silver nanoclusters can redistribute the charge flux and lower the potential barrier, leading to novel switching characteristics. Furthermore, the device can work at ultralow operating voltages (0.03 V) over 300 cycles with an extremely high memory window of 107. The experimental findings open up a new path to design high‐performance transient memristors for a safe and reliable data storage system. [ABSTRACT FROM AUTHOR]

The Corbino disk is used to study magnetoresistance as it eliminates Hall‐voltage interference and allows for direct probing of bulk properties. A laser‐triggered magnetoresistance effect in the Corbino disk of a Cu/SiO2/Si structure is reported. When a laser is applied, the magnetoresistance is greatly improved, by 360 times, under a 635 nm laser and a 2.1T magnetic field. In addition, the effect shows a close connection with the geometry of the nanoscale metal‐film structure. This effect is attributed to an increase in carrier concentration and bending of the motion path under the laser and magnetic field. This work suggests a new approach for the exploration of the magnetoelectric properties of nanoscale metal films and opens a new window on the use of light to adjust magnetoresistance devices. [ABSTRACT FROM AUTHOR]