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Smart cities are a product of the integration of the new urbanization process and information technology development in China, while inclusive green growth is an important strategic concept for economic development transformation. Whether the smart city pilot policy can promote inclusive green growth in China still needs to be tested. Based on panel data from 210 prefecture level and above cities from 2009 to 2020, this study used the multi-period difference-in-differences method to learn the impact of the smart city pilot policy on inclusive green growth in China, followed by a series of robustness tests. In addition, the sub-system path, location heterogeneity, and spatial spillover effects of policy effects were also examined. The results showed that: (J) The construction of smart cities would have a significant positive impact on inclusive green growth, and the policy effects would gradually increase over time. The construction of smart cities had a significant promoting effect on both economic growth and green growth, but the impact on inclusive growth was not significant. © There was significant regional heterogeneity in the impact of smart city construction on inclusive green growth. There was a significant positive impact in the eastern and western regions, a significant negative impact in the northeast region, and an insignificant impact in the central region. © The policy effects of smart city construction had significant spatial spillover characteristics. Smart city construction could significantly promote economic growth and green growth in adjacent non-pilot areas but could only significantly promote economic growth in adjacent pilot areas. Therefore, in the critical period of China's economic transformation and upgrading, in order to enhance the promotion of the pilot policy on inclusive green growth, on the one hand, the construction of smart cities should focus on promoting inclusive growth and formulating reasonable pilot goals according to local conditions based on the resource endowment of each region. On the other hand, it is necessary to promote the exchange of piloting experience and innovation resources among regions and arrange the investment level of innovation resources reasonably to avoid the occurrence of ' depression effect' and ' siphon effect.' [ABSTRACT FROM AUTHOR]

Objectives: In the large-scale of virtual reality scene, it is difficult to add all graphics data into the video memory for rendering. Removing the occluded objects in advance by visible query technology can reduce the amount of data loaded on the display end to improve the rendering efficiency. Therefore, the research of visible query method for regional objects has important application value for real-time rendering of large-scale urban scene. Methods: We put forward a distributed visible query method based on Map-Reduce. In the map phase, we apply a hierarchical axis-aligned bounding box as viewpoint space partition. When the number of 3D objects in viewpoint space partition exceeds the threshold, the axis-aligned bounding box continues to be divided into sub- boxes. After the above process, the map tasks produce GeoTuples with the VSPID as key and visible query candidate set as value. In the reduce phase, a viewpoint is created for each leaf axis-aligned bounding box where the binary space partitioning trees are build and the visible set is calculated using real-time occlusion algorithm. Results: The experiment results with a building compound, containing more than 200 000 geometric solids, in Shenzhen, China show that: (1) There is no simple linear relationship between the running time of distributed visible query and the number of viewpoint space partitions. (2) Running time and parallelism are not simply inversely proportional. The computational efficiency of each process first increases and then decreases with the increase of parallelism. About 48 parallelism, the process has the highest efficiency. (3) Whether the distributed approach is better than the traditional approach depends on the number of 3D objects. After the amount of 3D objects reaches about 40 000, the distributed algorithm begins to be better than the traditional algorithm. Conclusions: The computational experiments reveal the proposed algorithms outperform competitors in terms of the processing efficiency and feasibility, which can meet the requirement of visible query in large-scale scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Technology Economics is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Taking 13 cities in Beijing, Tianjin and Hebei as the research objects, this paper constructed the urban smart development index system from three aspects: smart space construction, smart element allocation and smart welfare performance, measured the urban smart development level in Beijing, Tianjin and Hebei from 2008 to 2018, and used ArcGIS software to realize the temporal and spatial measurement performance of smart development level. The obstacle degree model was used to reveal the criterion-level factors that hinder the smart development of Beijing, Tianjin and Hebei cities. The main conclusions are as follows: (1) The smart development level in Beijing, Tianjin and Hebei is low, the research period shows a weak fluctuation and downward trend, and there is room for improvement. (2) From the spatial difference of smart development level, the double core trend with Beijing and Tianjin as the high level is obvious, showing a downward trend from the center to the surrounding. (3) From the overall barrier performance of study area, smart welfare performance accounts for the largest proportion, smart space construction takes the second place, and smart factor allocation is the lowest. (4) The obstacles to smart development in different cities vary. The obstacles in Beijing and Tianjin are mainly focused on smart welfare performance. As the main core area to undertake Beijing's noncapital functions, smart space construction and smart element allocation are the obstacles in most cities. [ABSTRACT FROM AUTHOR]

Objectives: Facing the demand for mobile intelligent services in the era of internet of everything, it is an urgent need to establish an aerial-ground collaborative intelligent service platform. Due to the limitations of core equipments and algorithms, the development of aerial-ground collaborative mobile intelligent service platform is relatively slow. Methods: Therefore, we break through the core technologies of realtime position and attitude measurement of sensors, develop the airborne and vehicle mounted aerial-ground multi-platform collaborative networking remote sensing observation system, and realize the quality improvement and intelligent and fine processing of multi-source remote sensing images, which can effectively support remote sensing mapping services, and improve the level of automatic processing of remote sensing information. Results: Thus, our research solves the problems of “accuracy, speed and flexibility” in the aerial-ground collaborative intelligent service platform, and realizes the global autonomous positioning of “BeiDou navigation satellite system with position and attitude measuring instrument”, rapid target searching under the aerial-ground collaborative networking, and automatic change checking under intelligent processing. Conclusions: The aerial-ground collaborative mobile intelligent service platform has been widely used in the monitoring of geographical conditions, smart city planning and construction, emergency mapping support and other industries. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: With the rapid development of the reality acquisition technologies, such as laser scanning and structured light scanning, point cloud has become a high-precision three-dimensional holographic representation for the physics world. As the third important data source, point cloud is very suitable for presenting 3D model and geographic and spatial information, and pushes forward an immense influence on smart city, autonomous driving application and augmented reality. However, the massive, unstructured, and uneven density of point cloud data brings challenges to onboard and offboard storage as well as real-time transmission. Hence, efficient compression methods, which balance between bit rate and quality, are mandatory for ensuring the storage and transmission of such data. Methods: Firstly, we describe different families of approaches in details and summarize the basic technologies that are usually used in 3D point cloud compression. Secondly, we provide detailed description of three open source point cloud codec algorithms and their coding performances. Finally, the promising development tendency of the static point cloud compression is summarized. Results: This paper summarizes the state-of-the-art of domestic and foreign static point cloud compression algorithms, the standard specifications released by moving picture experts group and evaluation metrics for point cloud compression. Conclusions: The purpose is to open up a vision for the further research on the static point cloud compression technology to a certain extent, boost the advancement of theoretical research significance and practical application value in this technical area. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an important and challenging task in building smart city. Accurate forecasting requires consideration of spatio-temporal characteristics consisting of multiple influences such as holiday, similar node, and weather. In order to accurately capture the spatio-temporal characteristics of road network intersections, this paper proposed a prediction model based on graph convolutional neural network, temporal algorithm Prophet and Pearson correlation coefficient to achieve accurate prediction of intersection traffic considering spatial structure, similar nodes, holidays and other influencing factors. Firstly, it introduced pearson correlation coefficient to reduce the influence of similar nodes to improve the temporal algorithm for capturing temporal features. Secondly, it used the graph convolution neural network to capture spatial features; Finally, it determined the fusion weights of the graph convolutional network and the temporal algorithm by linear regression to obtain the results of spatio-temporal fusion prediction. This paper finally extracted the intersection traffic data based on the analysis of Chengdu taxi trajectory data and conducted traffic prediction experiments. The results show that the accuracy of the model proposed in this paper is better than that of most existing baseline methods, compared with the T-GCN, ASTGCN, and AGCRN models, the MAE is reduced by 1.623, 0.724, 0.161, respectively, and the accuracy is improved by 0. 144, 0. 068, and 0. 021, respectively, which verifies the effectiveness of the model in traffic intersection flow prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chengdu University (Social Science) is the property of Journal of Chengdu University (Social Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Crowd counting is widely used in public security, video surveillance, smart city construction and other fields, which plays an important and positive role in controlling the number of people in special places, directing public transportation, avoiding the spread of the epidemic and ensuring social stability. With the development of deep learning, traditional methods are gradually replaced by convolutional neural network (CNN) methods. This paper introduces the research background, current situation and development trend of crowd counting. Two traditional methods are described. Then the CNN methods are analyzed from counting accuracy, network structure, evaluation index to data sets and other aspects. It is found that CNN technologies can effectively solve multi-sacle and cross-scene problems. The weakly supervised counting method based on Vision Transformer(ViT) sequence is described and various methods are compared. The future research prospect of crowd counting is prospected. [ABSTRACT FROM AUTHOR]

Copyright of Public Art is the property of Shanghai Fine Arts Publisher Ltd. co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The k nearest neighbor query method under the road network environment plays an important role in geographic information system, point of interest discovery, smart city, data mining and material distribution. The existing methods mainly deal with the query problem that the query object and data object are single data type. However, in reality, the data object type of the target object set is often not single, and the query problem will become more complex. The existing nearest neighbor query methods in road network cannot directly solve the neighbor query problem in which the query object is a point and the data object includes the data mixed with points and line segments. In order to make up for the shortcomings of the existing methods, the nearest neighbor query algorithm of mixed complex data in the road network is proposed. Methods: The query process is divided into three parts: Preprocessing, data set reduction and data set refining. Firstly, the data set is preprocessed, the line segments are transformed into points. A mapping relationship is formed between the intersection of line segments in the road network and line segments, and the corresponding line segments are found by querying the location of the points. The query points, data points and Hilbert values are stored using arrays. Secondly, the data object set is refined preliminarily by pruning method and shortest path theorem to reduce the number of data objects, and the data set filtering algorithm is given. Finally, the points in the data set are further pruned to calculate the shortest path network distance from the query point to the points in the candidate set. Compared with these distances, the shortest path distance can be determined, and then the nearest neighbor is accurately found. Results: Experimental results show that the proposed algorithm effectively reduces the data set and the amount of calculation in the query process, further refines the nearest neighbor candidate set and reduces the query scope and the query time. There is no need to store the distance information between all boundary nodes in the road network, so a large amount of redundant distance information is eliminated. The data points and road network data that meet the pruning conditions around the query points are pruned. Therefore, the increase of road network scale has little impact on the time cost of the algorithm. Conclusions: The proposed algorithm can directly deal with the nearest neighbor query of mixed data in the road network environment. When the number of data objects and the scale of the road network are large, the algorithm has obvious advantages in central processing unit running time and input/output cost. [ABSTRACT FROM AUTHOR]

Spatiotemporal crowd flow prediction is one of the key technologies in smart cities. There are mainly two major shortcomings that plague researchers and developers. Firstly, crowd flow is affected by complex factors and previous studies have summarized a variety of spatiotemporal prior knowledge. However, it is difficult for followup work to comprehensively incorporate this prior knowledge as its application scenarios are diverse. Secondly, with the development of deep learning technology, implementing state-of- the-art models is cumbersome work and becomes more and more complicated. To fill in the above gaps, this paper designs time- series sampling interfaces and graph construction interfaces. The time series sampling interfaces can generate different types of time series based on diverse prior knowledge, and the graph construction interfaces can build different types of spatial graphs. Moreover, users can extend the above two interfaces to utilize new spatiotemporal prior knowledge. Based on the TensorFlow framework, this paper implements a variety of advanced spatiotemporal graph models and encapsulates the frequently-used spatiotemporal modeling units. Users can leverage state-of- the- art spatiotemporal models and perform customized development based on these advanced layers. In summary, the spatiotemporal crowd flow prediction tool box UCTB integrates diverse spatiotemporal prior knowledge and a variety of advanced models, which may promote the development of spatiotemporal crowd flow prediction applications. The codes and detailed documents are open-source. The URL of UCTB is https://github.com/uctb/UCTB. [ABSTRACT FROM AUTHOR]

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Nowadays, a vast number of sensors and observation platforms bring a significant challenge for city perception. In the study, we focus on developing of a smart city tempo‑spatial perception system. The concept of smart city awareness base station (SCABS) is proposed in this study, and the SCABS architecture considers the above problems. Methods: Firstly, we systematically reviewed the requirements and the complexity of smart city perception and summarized the development process and network architecture of smart city perception technology systems. On this basis, the challenges of ubiquitous access, trusted perception and intelligent management in future smart city perception are addressed. Then, this paper sorted out the demand for future city perception services and constructed the architecture of SCABS. Finally, we standardized the technical indicators and service capabilities of SCABS. Results: We constructed the prototype of SCABS. According to the qualitative analysis and experiments based on real environment, we found that SCABS with unified access, integrated management, and edge intelligence can meet the access and management ability of multi‑platform and high‑density sensing resources. Conclusions: SCABS would act an essential role in the smart city perception in the future. As a cyber‑physical infrastructure, it expects to cover various scenarios in the city with multiple heterogeneous perception access and hybrid access of multi‑protocols, and to automatically support the multi‑platform coordinated control. The SCABS meets global, accurate, heterogeneous, and multiple sensory perception. The SCABS is expected to be one of the critical infrastructures of the future smart city. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: 3D bioprinting has become a crucial research field of tissue engineering with the unceasing development of science and technology. Nevertheless, some limitations cannot be resolved. 4D bioprinting is the most emerging technology, and has become the major solution for the next generation of tissue engineering. OBJECTIVE: To introduce the materials and methods of 4D bioprinting in accordance with various shape transformation principles, and to explore the application of 4D bioprinting to tissue engineering and the current challenges. METHODS: “4D bioprinting, 4D biofabriation, printing, smart materials, smart scaffold, shape memory polymers, tissue engineering” in Chinese and English were utilized as search terms. A total of 127 related articles published from January 1, 2016 to January 1, 2021 were retrieved from CNKI, PubMed, and SCIE databases. After screening out by the first author and adding high-quality references, 106 articles were included for review. RESULTS AND CONCLUSION: (1) 4D bioprinting, the latest emerging technology, combines the concept of time with 3D bioprinting as the fourth dimension. 4D bioprinting can create complex and functional structures. (2) 4D bioprinting can make dynamic 3D biological structures by using smart materials. These structures can alter shape under various stimuli. (3) The functional transformation and maturation of printed cell structures are also considered a form of 4D bioprinting. (4) This technology provides an unprecedented potential for tissue engineering. In the field of biomedicine, although this technology has attracted great attention, more research and development are needed to realize clinical application, because it is still in the emerging stage. [ABSTRACT FROM AUTHOR]

How to realize safety data sharing and promote the integration of multi-source data is one of the important technical challenges faced by academic and industrial circles. In recent years,federated learning has received widespread attention, which is a new technology to deal with this challenge. Federated learning has been applied in fields such as smart healthcare and smart city construction, but there is little research in the field of potential trajectory data mining. To solve this problem, this paper proposed a distributed and secure framework named federated spectral clustering( FSC), and applied it to the spectral clustering of ship AIS trajectory data. In the FSC framework, it used the encrypted sample alignment technology and a homomorphic encryption scheme as building blocks for the clustering algorithm, guaranteeing the security of the data in the process offederal training executed by multi-participants. To illustrate the effect of this algorithm, this paper conducted the experiments on both synthetic datasets and ships AIS trajectory datasets. The comparisons of experiments results with other similar clustering algorithms demonstrate that, besides its security advantage, this algorithm performs well in terms of clustering effect. The results indicate that the FSC can obtain the main route in the marine navigation area, which can provide specialized support for the intelligence of maritime supervision systems. [ABSTRACT FROM AUTHOR]