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Spatiotemporal correlation function is the basic characteristic of dynamic laser speckle and the basis of various applications. The correlation function of the speckle intensity from a 3D target in turbulent atmosphere is derived based on the model of random phase screen and Fresnel Kirchhoff Diffraction Formula, and a fast algorithm based on FFT is developed. The particularity of dynamic speckle in turbulence is numerically analyzed and discussed. The results show that the speckle intensity fluctuates at two independent scales both in space and time domain, which are affected by target size and atmospheric turbulence respectively. In particular, the time scale caused by turbulence is also affected by the target translating velocity and the wind direction. The theory and algorithm developed in this paper can play important roles in applications of laser speckle such as remote detection in atmospheric environment.

Tourism operators generate carbon emissions during their operations, and their environmental responsibility behaviors, such as carbon offsetting, significantly impact the ecological environment of tourist sites. Understanding the operators' willingness to engage in carbon offsetting and the factors influencing this willingness is crucial for achieving the "dual carbon" goals and promoting sustainable growth in China's tourism sector. This study collected 746 offline questionnaires from operators at rural tourism sites across 100 counties and districts in Jiangxi Province. It empirically analyzed their willingness to participate in carbon offsetting, comparing various factors such as the strength of willingness, spatial differences, and distinctions between all-for-one tourism counties and non-all-for-one tourism counties, as well as suburban, outer suburban, and remote tourism sites. Using the value–belief–norm theory and the theory of planned behavior, a structural equation model was developed to comprehensively examine the operators' willingness and its influencing mechanisms. The structural statistical analysis revealed that the integrated model effectively predicted rural tourism operators' willingness to participate in carbon offsetting. The results showed that, except for Hypothesis 6, all suggested correlations between the variables were significant. Value perception had a significantly positive effect on participants' desire to engage in carbon offsetting. This research provides various theoretical and practical implications for local authorities regarding rural tourism operators, advancing the incentive for carbon offsetting and sustainable tourism development. [ABSTRACT FROM AUTHOR]

Remote sensing image object detection is widely used in civil and military fields. The important task is to detect objects such as ships, planes, airports, harbours and so on, and then it can obtain object category and position information. It is of great significance to use remote sensing images to observe the densely arranged and directional targets such as cars and ships parked in parking lots and harbours. The object detection task mainly includes object localization and classification. Remote sensing images contain large number of small objects and dense scenes due to the long shooting distance and wide coverage. Small objects occupy few pixels in the image, and they are easily miss‐detected. In dense scenes, the overlapping part of each object is large, so it is easy to detect objects repeatedly. The traditional small object detection methods deliver low accuracy and take long time. Therefore, object detection is very challenging. We put forward a novel deep learning‐based single shot multibox detector (SSD) model for object detection. First, we propose an improved inception network to optimize SSD to strengthen the small object feature extraction ability (FEA) in the shallow network. Second, the feature pyramid network is modified to enhance the fusion effect. Third, the deep feature fusion module is designed to improve the FEA of the deep network. Finally, the extracted image features are matched with candidate boxes with different aspect ratios to perform object detection and location with different scales. Experiments on DOTA show that the proposed algorithm can adapt to the remote sensing object detection in different backgrounds, and effectively improve the detection effect of remote sensing objects in complex scenes. [ABSTRACT FROM AUTHOR]

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet [ABSTRACT FROM AUTHOR]

Natural products, while valuable for drug discovery, encounter limitations like uncertainty in targets and toxicity. As an important active ingredient in traditional Chinese medicine, celastrol exhibits a wide range of biological activities, yet its mechanism remains unclear. In this study, they introduced an innovative "Degradation‐based protein profiling (DBPP)" strategy, which combined PROteolysis TArgeting Chimeras (PROTAC) technology with quantitative proteomics and Immunoprecipitation‐Mass Spectrometry (IP‐MS) techniques, to identify multiple targets of natural products using a toolbox of degraders. Taking celastrol as an example, they successfully identified its known targets, including inhibitor of nuclear factor kappa B kinase subunit beta (IKKβ), phosphatidylinositol‐4,5‐bisphosphate 3‐kinase catalytic subunit alpha (PI3Kα), and cellular inhibitor of PP2A (CIP2A), as well as potential new targets such as checkpoint kinase 1 (CHK1), O‐GlcNAcase (OGA), and DNA excision repair protein ERCC‐6‐like (ERCC6L). Furthermore, the first glycosidase degrader is developed in this work. Finally, by employing a mixed PROTAC toolbox in quantitative proteomics, they also achieved multi‐target identification of celastrol, significantly reducing costs while improving efficiency. Taken together, they believe that the DBPP strategy can complement existing target identification strategies, thereby facilitating the rapid advancement of the pharmaceutical field. [ABSTRACT FROM AUTHOR]

Calcium silicate hydrate (C‐S‐H) synthesis follows multistep nucleation processes ranging from amorphous globules to nanocrystals. This type of nonclassical nucleation pathway attracts extensive interest and yet related molecular processes are mostly unexplored. Herein, atomic‐level structure evolution during this process is unveiled. From inside to outside of the globule, the atomic arrangement evolves from amorphous configuration to layered stacking of calcium silicate layers with layers spaced by 1.1 nm. Numerous cations surrounding the globules provide extra calcium ions to form nano C‐S‐H foils at the edge of the globule. The polymerization to form calcium silicate chains has almost been completed before 5 min reaction, and the C‐S‐H structure formation is basically based on the self‐assembly to be calcium silicate layers and then lamellar crystal inside the globule. Silicate chain defects inhibit a‐ and c‐axes crystallization, but promote b‐axes one, leading to anisotropic growth of crystalline C‐S‐H. The defects lead to the crystallization pathways conversion from Si–O–Si bonds formation to Ca2+ electrostatic linking. [ABSTRACT FROM AUTHOR]

To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning and a total variation (TV) prior. The method minimizes the first-order moment distance between the deep prior of a Fast and Flexible Denoising Convolutional Neural Network (FFDNet) and the Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement and reinforce each other's advantages. Specifically, the original HSI is initially processed with a random binary sparse observation matrix to achieve a sparse representation. Subsequently, the plug-and-play (PnP) algorithm is employed within the framework of generalized alternating projection (GAP) to denoise the sparsely represented HSI. Experimental results demonstrate that, compared to existing methods, this method shows significant advantages in both quantitative and qualitative assessments, effectively enhancing the quality of HSIs. [ABSTRACT FROM AUTHOR]

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model's ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions. [ABSTRACT FROM AUTHOR]