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Due to the advantages of small size, lightweight, and simple operation, the unmanned aerial vehicle (UAV) has been widely used, and it is also becoming increasingly convenient to capture high-resolution aerial images in a variety of environments. Existing target-detection methods for UAV aerial images lack outstanding performance in the face of challenges such as small targets, dense arrangement, sparse distribution, and a complex background. In response to the above problems, some improvements on the basis of YOLOv5l have been made by us. Specifically, three feature-extraction modules are proposed, using asymmetric convolutions. They are named the Asymmetric ResNet (ASResNet) module, Asymmetric Enhanced Feature Extraction (AEFE) module, and Asymmetric Res2Net (ASRes2Net) module, respectively. According to the respective characteristics of the above three modules, the residual blocks in different positions in the backbone of YOLOv5 were replaced accordingly. An Improved Efficient Channel Attention (IECA) module was added after Focus, and Group Spatial Pyramid Pooling (GSPP) was used to replace the Spatial Pyramid Pooling (SPP) module. In addition, the K-Means++ algorithm was used to obtain more accurate anchor boxes, and the new EIOU-NMS method was used to improve the postprocessing ability of the model. Finally, ablation experiments, comparative experiments, and visualization of results were performed on five datasets, namely CIFAR-10, PASCAL VOC, VEDAI, VisDrone 2019, and Forklift. The effectiveness of the improved strategies and the superiority of the proposed method (YOLO-UAV) were verified. Compared with YOLOv5l, the backbone of the proposed method increased the top-one accuracy of the classification task by 7.20% on the CIFAR-10 dataset. The mean average precision (mAP) of the proposed method on the four object-detection datasets was improved by 5.39%, 5.79%, 4.46%, and 8.90%, respectively.

Mercury is a potent neurotoxin that poses health risks to the global population. Anthropogenic mercury emissions to the atmosphere are projected to decrease in the future due to enhanced policy efforts such as the Minamata Convention, a legally-binding international treaty entered into force in 2017. Here, we report the development of a comprehensive climate-atmosphere-land-ocean-ecosystem and exposure-risk model framework for mercury and its application to project the health effects of future atmospheric emissions. Our results show that the accumulated health effects associated with mercury exposure during 2010–2050 are $19 (95% confidence interval: 4.7–54) trillion (2020 USD) realized to 2050 (3% discount rate) for the current policy scenario. Our results suggest a substantial increase in global human health cost if emission reduction actions are delayed. This comprehensive modeling approach provides a much-needed tool to help parties to evaluate the effectiveness of Hg emission controls as required by the Minamata Convention. Mercury is a neurotoxin and pollutant with enhanced emissions from anthropogenic activities. Here, the authors develop a global emissions, transport, and human risk model and find substantial future losses in revenue and public health if emission reductions proposed by the Minamata Convention are delayed.

The quick and accurate measurement and evaluation of the deterioration degree and consolidation effectiveness on the surface of masonry relics is valuable for disease investigation and restoration work. However, there is still a lack of quantitative indices for evaluating the deterioration degree and consolidation effectiveness of masonry relics in situ. Based on the micro-drilling resistance method, new quantitative evaluation indices for the deterioration degree and consolidation of masonry materials were proposed. Five types of masonry samples with different deterioration degrees were prepared by artificially accelerated deterioration tests involving sandstone and clay brick as research objects. Three types of consolidants were used to consolidate the deteriorated samples. Drilling resistance tests were conducted for deteriorated and consolidated samples. The variations in deterioration depth and average drilling resistance for samples with different numbers of deterioration cycles were analysed, while the differences in consolidation depth and average drilling resistance for samples with different consolidant types and dosages were compared. Finally, the deterioration degree index ( K ) and consolidation effectiveness index ( R c ), which are based on the average drilling resistance, are proposed. The results can be applied to quick on-site investigations of immovable masonry relics.

Bi-defect sites are highly effective for CO 2 reduction (CO 2 RR) to formic acid, yet most catalytic surfaces predominantly feature inert, non-defective Bi sites. To overcome this limitation, herein, tensile strain is introduced on wholescale non-defective Bi sites. Under rapid thermal shock, the Bi-based metal-organic framework (Bi-MOF-TS) shows weakened Bi–O bonds and produced tiny Bi clusters. During electrochemical reduction, these clusters create numerous continuous vacancies, inducing weak tensile strain over a large range of surrounding non-defective Bi sites. This strain enhances *OHCO intermediates adsorption and substantially lowers the reaction barrier. As a result, Bi-MOF-TS achieves a faradaic efficiency above 90% across 800 mV potential range, with an impressive formate partial current density of −995 ± 93 mA cm −2 . Notably, Bi-MOF-TS exhibits a high HCOOH faradaic efficiency of 96 ± 0.64% at 400 mA cm −2 in acidic electrolyte and a high single-pass carbon conversion efficiency (SPCE) of 62.0%. Additionally, a Zn-CO 2 battery with Bi-MOF-TS as the cathode demonstrates a peak power density of 21.4 mW cm −2 and maintains stability over 300 cycles. A large range of inert and non-defective sites in catalysts is a primary factor impeding catalyst activity in acidic CO 2 electroreduction. Here, the authors achieve high HCOOH selectivity and activity in acidic electrolyte by introducing tensile strain to activate inert sites.

Dynamic deformation measurement (DDM) is critical across infrastructure and industrial applications. Among various advanced techniques, multi-camera digital image correlation (MC-DIC) stands out due to its ability to achieve wide-range, full-field, and non-contact 3D DDM by pairing camera subsystems. However, existing MC-DIC methods typically rely on inefficient manual pairing or a simplistic strategy that aggregates all visible cameras for measuring specific object regions, leading to camera over-grouping. These limitations often result in cumbersome system setup and ill-measured deformations. To overcome these challenges, we propose a novel MC-DIC method with pointwise-optimized model-based stereo pairing (MPMC-DIC). By automatically evaluating and selecting camera pairs based on five evaluation factors derived from 3D model and calibrated cameras, the proposed method overcomes the over-grouping problem and achieves high-precision DDM of semi-rigid objects. A Ø5 × 5 cm cylinder experiment demonstrated an accuracy of 0.03 mm for both horizontal and depth displacements in the 0.0–5.0 mm range, and validated strong robustness against cluttered backgrounds using a 2 × 4 camera array. Vibration measurement of a 9 × 15 × 16 cm PC speaker operating at 50 Hz, using eight surrounding cameras capturing 1920 × 1080 images at 400 fps, confirmed the proposed method’s capability to perform wide-range dynamic deformation analysis and its robustness against complex object geometries.

As the demand for industrial robots continues to increase, monitoring robot manipulators in factory environments has become essential to ensure proper and precise operation. Unexpected vibrations can reduce the production efficiency and quality, causing financial losses, and safety risks to workers. Evaluating a robot’s vibration resistance solely through arm movements makes it challenging to accurately capture fine vibration-frequency responses using conventional methods. Traditional analyses rely on contact sensors, which are limited by the number of measurable points, and often involve high costs. In this study, we employed high-frame-rate (HFR) cameras for non-contact vibration analysis, enabling a detailed evaluation of the vibration characteristics during robot operation. By processing the 500 fps HFR video using digital image correlation, we analyzed the frequency responses of sub-pixel displacements at multiple locations and quantified changes in the vibration amplitude and phase across different parts of the robot. This approach provides a more precise understanding of fine vibration distributions and their impacts. The proposed method is accurate and can simultaneously measure multiple points.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19. COVID-19 is a critical public health threat, but molecular characterizations of patients’ immunity is still lacking. Here the authors collected blood from patients with various disease severity, and prefiltered to exclude selected comorbidity, to obtain genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles to report a trans-omics landscape.

Computer vision tasks, such as motion estimation, depth estimation, object detection, etc., are better suited to light field images with more structural information than traditional 2D monocular images. However, since costly data acquisition instruments are difficult to calibrate, it is always hard to obtain real-world scene light field images. The majority of the datasets for static light field images now available are modest in size and cannot be used in methods such as transformer to fully leverage local and global correlations. Additionally, studies on dynamic situations, such as object tracking and motion estimates based on 4D light field images, have been rare, and we anticipate a superior performance. In this paper, we firstly propose a new static light field dataset that contains up to 50 scenes and takes 8 to 10 perspectives for each scene, with the ground truth including disparities, depths, surface normals, segmentations, and object poses. This dataset is larger scaled compared to current mainstream datasets for depth estimation refinement, and we focus on indoor and some outdoor scenarios. Second, to generate additional optical flow ground truth that indicates 3D motion of objects in addition to the ground truth obtained in static scenes in order to calculate more precise pixel level motion estimation, we released a light field scene flow dataset with dense 3D motion ground truth of pixels, and each scene has 150 frames. Thirdly, by utilizing the DistgDisp and DistgASR, which decouple the angular and spatial domain of the light field, we perform disparity estimation and angular super-resolution to evaluate the performance of our light field dataset. The performance and potential of our dataset in disparity estimation and angular super-resolution have been demonstrated by experimental results.

Anthropogenic mercury (Hg(0)) emissions oxidize to gaseous Hg(II) compounds, before deposition to Earth surface ecosystems. Atmospheric reduction of Hg(II) competes with deposition, thereby modifying the magnitude and pattern of Hg deposition. Global Hg models have postulated that Hg(II) reduction in the atmosphere occurs through aqueous-phase photoreduction that may take place in clouds. Here we report that experimental rainfall Hg(II) photoreduction rates are much slower than modelled rates. We compute absorption cross sections of Hg(II) compounds and show that fast gas-phase Hg(II) photolysis can dominate atmospheric mercury reduction and lead to a substantial increase in the modelled, global atmospheric Hg lifetime by a factor two. Models with Hg(II) photolysis show enhanced Hg(0) deposition to land, which may prolong recovery of aquatic ecosystems long after Hg emissions are lowered, due to the longer residence time of Hg in soils compared with the ocean. Fast Hg(II) photolysis substantially changes atmospheric Hg dynamics and requires further assessment at regional and local scales. Reduction of gaseous Hg(II) compounds drives atmospheric mercury wet and dry deposition to Earth surface ecosystems. Global Hg models assume this reduction takes place in clouds. Here the authors report a new gas-phase Hg photochemical mechanism that changes atmospheric mercury lifetime and its deposition to the surface.

Mercury (Hg), a global contaminant, is emitted mainly in its elemental form Hg⁰ to the atmosphere where it is oxidized to reactive HgII compounds, which efficiently deposit to surface ecosystems. Therefore, the chemical cycling between the elemental and oxidized Hg forms in the atmosphere determines the scale and geographical pattern of global Hg deposition. Recent advances in the photochemistry of gas-phase oxidized HgI and HgII species postulate their photodissociation back to Hg⁰ as a crucial step in the atmospheric Hg redox cycle. However, the significance of these photodissociation mechanisms on atmospheric Hg chemistry, lifetime, and surface deposition remains uncertain. Here we implement a comprehensive and quantitative mechanism of the photochemical and thermal atmospheric reactions between Hg⁰, HgI, and HgII species in a global model and evaluate the results against atmospheric Hg observations. We find that the photochemistry of HgI and HgII leads to insufficient Hg oxidation globally. The combined efficient photoreduction of HgI and HgII to Hg⁰ competes with thermal oxidation of Hg⁰, resulting in a large model overestimation of 99% of measured Hg⁰ and underestimation of 51% of oxidized Hg and ∼66% of HgII wet deposition. This in turn leads to a significant increase in the calculated global atmospheric Hg lifetime of 20 mo, which is unrealistically longer than the 3–6-mo range based on observed atmospheric Hg variability. These results show that the HgI and HgII photoreduction processes largely offset the efficiency of bromine-initiated Hg⁰ oxidation and reveal missing Hg oxidation processes in the troposphere.