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To propose a sensory evaluation method suitable for Qingzhuan tea, a representative Qingzhuan tea produced in Hubei was soak or spray boiled in order to analyze the effects of different boiling conditions on the sensory quality and the leaching rate of major chemical components of Qingzhuan tea. The sensory quality and chemical components of the tea infusions were compared with those of tea infusion obtained by the brewing method specified in the Chinese national standard. The results showed that the sensory quality of the boiled tea infusion was significantly better than that of the brewed tea infusion; the leaching rates of major quality components such as water extract, tea polyphenols, amino acids, soluble sugars and tea pigments were much higher in the former than in the latter. From the perspectives of sensory quality and the leaching rate of major quality components, tea infusion prepared by boiling for 2 min with a tea/water ratio of 1:80 and then standing for 2–4 min tasted moderate and had good internal quality. Further correlation analysis showed high correlation coefficients between the leaching rates of soluble sugars, theabrownine and water extract and taste quality. The results of this study provide a reference for the objective evaluation of the sensory quality of Qingzhuan tea and other types of compressed tea.

Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled data, but perform poorly compared to the supervised alternatives. In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision. The weak nature of the supervision arises from the requirement of video-level labels, i.e. person identities who appear in the video, in contrast to the more precise frame-level annotations. Towards this goal, we propose a multiple instance attention learning framework for person re-identification using such video-level labels. Specifically, we first cast the video person re-identification task into a multiple instance learning setting, in which person images in a video are collected into a bag. The relations between videos with similar labels can be utilized to identify persons, on top of that, we introduce a co-person attention mechanism which mines the similarity correlations between videos with person identities in common. The attention weights are obtained based on all person images instead of person tracklets in a video, making our learned model less affected by noisy annotations. Extensive experiments demonstrate the superiority of the proposed method over the related methods on two weakly labeled person re-identification datasets.

Summary: The N6‐methyladenosine (m6A) mRNA modification is crucial for plant development and stress responses. In rice, the male sterility resulting from the deficiency of OsFIP37, a core component of m6A methyltransferase complex, emphasizes the significant role of m6A in male fertility. m6A is reversible and can be removed by m6A demethylases. However, whether mRNA m6A demethylase regulates male fertility in rice has remained unknown. Here, we identify the mRNA m6A demethylase OsALKBH9 and demonstrate its involvement in male fertility regulation. Knockout of OsALKBH9 causes male sterility, dependent on its m6A demethylation activity. Cytological analysis reveals defective tapetal programmed cell death (PCD) and excessive accumulation of microspores exine in Osalkbh9‐1. Transcriptome analysis of anthers shows up‐regulation of genes involved in tapetum development, sporopollenin synthesis, and transport pathways in Osalkbh9‐1. Additionally, we demonstrate that OsALKBH9 demethylates the m6A modification in TDR and GAMYB transcripts, which affects the stability of these mRNAs and ultimately leads to excessive accumulation of pollen exine. Our findings highlight the precise control of mRNA m6A modification and reveal the pivotal roles played by OsALKBH9‐mediated m6A demethylation in tapetal PCD and pollen exine accumulation in rice. [ABSTRACT FROM AUTHOR]

Printed circuit board (PCB) surface defect detection is an essential part of the PCB manufacturing process. Currently, advanced CCD or CMOS sensors can capture high-resolution PCB images. However, the existing computer vision approaches for PCB surface defect detection require high computing effort, leading to insufficient efficiency. To this end, this article proposes a local and global context-enhanced lightweight CenterNet (LGCL-CenterNet) to detect PCB surface defects in real time. Specifically, we propose a two-branch lightweight vision transformer module with local and global attention, named LGT, as a complement to extract high-dimension features and leverage context-aware local enhancement after the backbone network. In the local branch, we utilize coordinate attention to aggregate more powerful features of PCB defects with different shapes. In the global branch, Bi-Level Routing Attention with pooling is used to capture long-distance pixel interactions with limited computational cost. Furthermore, a Path Aggregation Network (PANet) feature fusion structure is incorporated to mitigate the loss of shallow features caused by the increase in model depth. Then, we design a lightweight prediction head by using depthwise separable convolutions, which further compresses the computational complexity and parameters while maintaining the detection capability of the model. In the experiment, the LGCL-CenterNet increased the mAP@0.5 by 2% and 1.4%, respectively, in comparison to CenterNet-ResNet18 and YOLOv8s. Meanwhile, our approach requires fewer model parameters (0.542M) than existing techniques. The results show that the proposed method improves both detection accuracy and inference speed and indicate that the LGCL-CenterNet has better real-time performance and robustness. [ABSTRACT FROM AUTHOR]

As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes. [ABSTRACT FROM AUTHOR]

Image editing technology has brought about revolutionary changes in the field of architectural design, garnering significant attention in both the computer and architectural industries. However, architectural image editing is a challenging task due to the complex hierarchical structure of architectural images, which complicates the learning process for the high-dimensional features of architectural images. Some methods invert the images into the latent space of a pre-trained generative adversarial network (GAN) model, completing the editing process by manipulating this latent space. However, the task of striking a balance between reconstruction fidelity and editing efficacy through latent space mapping presents a formidable challenge. To address this issue, we propose a Residual Spatial Cross-Attention Network (RSCAN) for architectural image editing, which is an encoder model integrating multiple latent spaces. Specifically, we introduce the spatial feature extractor, which maps the image to the high-dimensional space F of the synthesis network, to enhance the spatial information retention and preserve the structural consistency of the architectural image. In addition, we propose the residual cross-attention to learn the mapping relationship between the low-dimensional space W and F space, generating modified features corresponding to the latent code and leveraging the benefits of multiple latent spaces to facilitate editing. Extensive experiments are performed on the LSUN Church dataset, and the experimental results indicate that our proposed RSCAN achieves significant improvements over the relevant methods in quantitative analysis metrics including the reconstruction quality, SSIM, FID, L2, LPIPS, PSNR, and editing effect Δ S , with enhancements of 29.49%, 17.29%, 8.81%, 11.43%, 11.26%, and 47.8%, respectively, thereby enhancing the practicality of architectural image editing. [ABSTRACT FROM AUTHOR]