Atmospheric nitrous acid (HONO) at a rural coastal site in North China: Seasonal variations and effects of biomass burning

Nitrous acid (HONO) plays a significant role in atmospheric chemistry due to its contribution to hydroxyl radical (OH). However, no scientific consensus has been achieved about the daytime HONO formation mechanisms. To identify the seasonal variations of HONO chemistry and the impacts of biomass burning (BB), we performed a two-phased field study in winter-spring and summer (covering a harvest season) in 2017 at a rural coastal site in North China. Though the mean HONO concentration in winter-spring (0.26 +/- 0.28 ppbv) was higher than in summer (0.17 + 0.19 ppbv), the maximum HONO concentrations were comparable (similar to 2 ppbv) in the two campaigns. Both the HONO/NOx ratio and nocturnal heterogeneous conversion efficiency of HONO (C-HONO) in summer were over twice of that in winter-spring. The daytime budget analysis also revealed that the strength of P(othe)r (i.e., the HONO sources apart from the reaction of OH + NO) in summer was double of that in winter-spring. BB affected the HONO concentration by enhancing the contribution of heterogeneous HONO production on the aerosol surface but weakening the role of photo-related HONO formation. HONO photolysis was a significant source of OH in both winter-spring and summer, and its contribution could be further enhanced during the BB episode in summer. Our study demonstrates the significant seasonal variations of HONO and the effects of BB, and suggests needs for more multi-season observations and considerations of BB, especially during the harvest time, in HONO research

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.Comment: 32 pages, 16 figures. Homepage: https://atm22.grand-challenge.org/. Submitte

A Review of Evaluation Studies on Tourism Suitability

In order to fully understand the development trends and achievements of tourism suitability evaluation at home and abroad, this paper reviews the relevant literature of tourism suitability evaluation from five aspects—tourism climate, tourism environment, tourism resources, tourism destination and evaluation index, and gives discussions

Spatial Assessment of Heat Exposure in Bay Cities: A Case of Xiamen in China

With the global warming, the rapid urbanization process and the increasing intensity and scope of human activities, extreme high temperature and cooresponding high temperature events were frequent, meanwhile, the degree of urban exposure to high temperatures was growing. The research constructed the heat exposure evaluation index system by taking the heat exposure evaluation index system in Xiamen, a gulf-based city with high-temperature. This study evaluated the influence factors, spatial differentiation features and hotspots of the heat exposure based on the methods of geo-spatial analysis, emergy accounting theory, expert consultation and analytic hierarchy process in Xiamen. The research results were expected to promote the theoretical development of urban heat exposure researches, and to provide decision-making reference for heat exposure assessment, regulation and adaptation in Xiamen and similar high temperature cities

Spatial Assessment of Heat Exposure in Bay Cities: A Case of Xiamen in China

With the global warming, the rapid urbanization process and the increasing intensity and scope of human activities, extreme high temperature and cooresponding high temperature events were frequent, meanwhile, the degree of urban exposure to high temperatures was growing. The research constructed the heat exposure evaluation index system by taking the heat exposure evaluation index system in Xiamen, a gulf-based city with high-temperature. This study evaluated the influence factors, spatial differentiation features and hotspots of the heat exposure based on the methods of geo-spatial analysis, emergy accounting theory, expert consultation and analytic hierarchy process in Xiamen. The research results were expected to promote the theoretical development of urban heat exposure researches, and to provide decision-making reference for heat exposure assessment, regulation and adaptation in Xiamen and similar high temperature cities

Research Progress and Application Perspectives of 4D Printing

4D printing technology has attracted people's attention since it came up in 2013. 4D printing is a kind of new manufacturing technology which is based on 3D printing and smart materials. In other word, 4D printing is evolved from 3D printing and aimed at the improvement of structure, property and function. 4D printing predicts that the self-assembly, multifunction and self-healing can be achieved. This paper reviews the whole research progress of 4D printing in time sequence, and summarizes the achievements of this technology in material science, manufacturing industry, bioengineering and medical science. In addition, the application perspectives in this field are also discussed

Identification of chaotic phenomena in a flexible deployable solar panel with multiple clearances

Deployable solar panels are widely used in spacecraft, and the dynamic characteristics of the deployment process directly affect the accuracy, stability, and reliability of the deployment. The flexibility and hinge clearance of a solar panel are important factors affecting the dynamic characteristics of the deployment system. The finite element method (FEM) was used to deal with the deformations of the solar panel. A dynamic model of the deployment process of a flexible solar panel with multiple clearances was established by combining the Lagrange equation with the FEM. The dynamic characteristics of solar panel deployment with multiple clearances and flexibility coupling were analyzed through a numerical solution, and the chaotic phenomena caused by clearances were identified. The results show that reasonably matching the clearance and flexibility of the system structure could quickly stabilize the collision force, improve the system life, and effectively improve the stability of the solar panel deployment process. Chaotic phenomena could be induced by the deployment velocity in a certain range, and the boundary value of the range changed with different clearance radii. The velocity variation law inducing chaotic phenomena also varied with the radius of clearance. This research provides important guidance for the optimum design and manufacturing of deployable solar panel mechanisms.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Experimental platform for coal gangue sorting robot based on image detection

Currently, coal gangue pre-sorting is still mostly done manually, with high labor intensity, low sorting efficiency, and safety hazards. Using coal gangue sorting robots to replace manual coal gangue pre-sorting is an effective way to ensure the health and safety of workers and improve work efficiency. However, the existing coal gangue sorting robots have poor performance in situations such as low light intensity and coal gangue surface covered with coal powder. To solve the above problems, an experimental platform for coal gangue sorting robot based on image detection is proposed. This experimental platform collects coal gangue images through industrial cameras. The platform uses ResNet18-YOLOv3 deep learning algorithm to identify the coal gangue in the images. The platform uses TCP communication to provide the position information of the gangue to the coal gangue sorting module for trajectory planning, then controls the manipulator to clamp the gangue and completes the gangue sorting operation. The platform uses the Halcon calibration method for hand-eye calibration of the experimental platform, in order to achieve the conversion of camera pixel coordinates and manipulator spatial coordinates. The positioning error of the experimental platform is calibrated. For coal gangue samples with sizes above 50 mm, the positioning error should not exceed 9 mm. The experimental results show that the recognition accuracy of the experimental platform for coal gangue under strong lighting conditions is 99%. The recognition accuracy of coal gangue under weak lighting conditions is 95%. The recognition accuracy of coal gangue under pulverized coal adhesion conditions is not less than 82%. The accuracy of coal gangue sorting is 82%