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In conventional p–i–n inverted perovskite solar cells (PSCs), there exists considerable energy loss due to both unsatisfactory light path design and trap‐induced interfacial defects. The sunlight is absorbed competitively by conductive oxide substrates and hole transport material in front of the perovskite layer, while the opaque metal back electrode also prevents light penetration. Worse yet, there is severe nonradiative charge recombination caused by defects between the perovskite layer and the electron transport material. To tackle the above two issues, a new green‐emitting material ((CH3)4 N)2(C2H5)4N·MnBr4 is synthesized and introduced in/on the perovskite layer to achieve both defect passivation and light complementation. The green luminescence of this single crystal at the interface is found to provide secondary light absorption, as evidenced by a remarkable promotion of short‐circuit current density. It is also found that the excess PbI2 on the surface of perovskite can be effectively removed, and as the interfacial additive, ((CH3)4 N)2(C2H5)4 N·MnBr4 inhibits trap‐assisted recombination losses, which provides favorable energy‐level alignment and extends charge carrier lifetime. As a result, the champion PCE (21.23%) of the target‐treated ((CH3)4 N)2(C2H5)4 N·MnBr4 device exceeds that 19.5% of the pristine‐without ((CH3)4 N)2(C2H5)4 N·MnBr4 device. This work provides an effective interfacial strategy for high‐performance and stable inverted PSCs. [ABSTRACT FROM AUTHOR]

BACKGROUND: Although traditional fermented noodles possess high eating quality, it is difficult to realize large‐scale industrialization as a result of the complexity of spontaneous fermentation. In present study, commercial Lactobacillus plantarum and Saccharomyces cerevisiae were applied in the preparation of fermented noodles. RESULTS: The changes in the structural characteristics and aroma components of noodles after fermentation were investigated via scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), low‐field magenetic resonance imaging, electronic nose, and simultaneous distillation and extraction/gas chromatography‐mass spectrometry (GC‐MS) analysis. SEM images revealed that co‐fermentation of the L. plantarum and S. cerevisiae for 10–40 min enhanced the continuity of the gluten network and promoted the formation of pores. FTIR spectra analysis showed that the co‐fermentation increased significantly (P < 0.05) the proportion of α‐helices of noodles gluten protein, enhancing the orderliness of the molecular structure of protein. After fermentation for 10–40 min, the signal density of hydrogen protons increased from the surface to the core, indicating that the water in the noodles migrated inward during a short fermentation process. The results of multivariate statistical analysis demonstrated that the main aroma differences between unfermented and fermented noodles were mainly in hydrocarbons, aromatic compounds and inorganic sulfides. GC‐MS analysis indicated that the main volatile compounds detected were 2, 4‐di‐tert‐butylphenol, bis (2‐ethylhexyl) adipate, butyl acetate, dibutyl phthalate, dioctyl terephthalate, bis (2‐ethylhexyl) phthalate, pentanol and 2‐pentylfuran, etc. CONCLUSION: Co‐fermentation with L. plantarum and S. cerevisiae improved the structure of gluten network and imparted more desirable volatile components to wheat noodles. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Recently, the development of deep learning has facilitated continuous progress in the field of computer vision. Pixel‐level semantic segmentation serves as a fundamental task in computer vision. It achieves significant results by connecting wider and deeper backbone networks and building fine‐grained segmentation heads. However, applications such as self‐driving cars are more critical to the computational speed of the algorithms. The trade‐off between accuracy and real‐time performance of existing algorithms is still a challenging task. To address this challenge, this article proposes an adaptive multiscale segmentation fusion network to fuse multiscale contextual, which designs an adaptive multiscale segmentation fusion module based on an attention mechanism. Using segmentation fusion instead of feature fusion, the multiscale segmentation results are aggregated to obtain more precise segmentation results. The final results achieved 70.9% mIoU of accuracy in the Cityspace test set, processing images at 61 FPS when the input is 1024 × 2048. In addition, when adjusting the input size to 512 × 1024, the images are processed at 185 FPS. [ABSTRACT FROM AUTHOR]

Image matting is a long‐standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images. We argue that the foreground objects can be represented by different‐level information, including the central bodies, large‐grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi‐scale information assembly framework (MSIA‐matte) to pull out high‐quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different‐level foreground expression, then combine them by our well‐designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA‐matte, and we can achieve state‐of‐the‐art performance compared to most existing matting networks. [ABSTRACT FROM AUTHOR]

Abstract: BACKGROUND: Rapid wound healing would be critical for successful long‐term storage of fruits and vegetables. However, there was no direct evidence for the requirement and efficiency of oxygen in the fruit wound‐healing process. This study was conducted to investigate the role of oxygen in wound‐induced suberization by analyzing melanin, suberin polyphenolics (SPPs) and related enzymes in half‐cut kiwifruits exposed to 100%, 50%, 21% and 0% oxygen. RESULTS: By 3 days after wounding, the wound surface of kiwifruit in high (50 and 100%) oxygen appeared as a continuous layer of melanin and SPPs underneath, which effectively prevent excessive water vapor loss from the fruit halves. In contrast, melanin and SPPs deposition in the wound surface in 0% oxygen was significantly reduced, with high water vapor loss. Rapid decrease of soluble phenolic acids (caffeic, p‐coumaric, ferulic acids) was coupled with the increase of bound ferulic acid (coniferyl diacetate) especially in high oxygen by 9 days after wounding. Meanwhile, high oxygen enhanced peroxidase, catalase, phenylalanine ammonia‐lyase, and polyphenol oxidase activities. CONCLUSION: Oxygen is required for wound‐induced melanin and SPPs formation, and high oxygen is effective in promoting wound suberization in postharvest kiwifruit. © 2017 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

We design dynamic k-d (DKD) tree based on classical k-d tree for animated scene rendering. Our method can inherit the benefit of efficient traversal of k-d tree and minimize time cost to update DKD tree, making it well suited for animated geometry. DKD employs primitive reset, redistribution to reflect the updated positions of geometry, and leaf node incremental growing to avoid the deterioration of hierarchy quality due to refitting. Our experiments show that DKD has a significant rendering performance improvement than selected existing methods. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a study which discusses petrogenesis of I-type granites from Yuqikapa granite pluton of the western Kunlun orogen. Topics discussed include multi-stage plate subduction and collision in Western Kunlun orogen, performing geochronology, whole-rock major and trace elements on the Yuqikapa pluton, and trace element compositions of Yuqikapa granites.

Scene understanding is essential to robotic navigation and autonomous driving as it provides semantic information to their controlling system. However, it will fail when processing low‐light images/videos captured under adverse weather or at night use state‐of‐the‐art scene understanding methods. A naive way to directly infer semantics from low‐light images is ill posed because the low‐light condition distorts pixel intensities and buries details. In order to address this problem, we propose the Deep Intensity Manipulation Network (DIMNet), which could relight the input images and recover the details, and combine the DIMNet with a scene understanding network to get a cascaded network to learn the semantics from low‐light images. Through learning pixel intensity manipulation, our method can generate images not only visually pleasing but also practical for scene understanding. Qualitative and quantitative experiments demonstrate that the proposed method is effective and robust for both synthetic and real‐world images. [ABSTRACT FROM AUTHOR]