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The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations. [ABSTRACT FROM AUTHOR]

In the context of pursuing high-quality development, the coupling and coordination of the ecosystem and economy has become the fundamental goal and inevitable choice for achieving the sustainable development of urban agglomerations. Based on remote sensing and statistical data for the Pearl River Delta (PRD) region from 2005 to 2020, in this paper, we construct an index system of the ecological and economic levels to assess the ecosystem service value (ESV). We use the equivalent factor method, entropy method, coupling coordination model, and relative development model to systematically grasp the spatial pattern of the levels of the two variables, analyse and evaluate their spatial and temporal coupling and coordination characteristics, and test the factors influencing their coupling and coordination using the geographical and temporal weighted regression (GTWR) model. The results show that ① the ESV in the PRD exhibited a fluctuating decreasing trend, while the level of the economy exhibited a fluctuating increasing trend; ② the coordination degree of the ESV and economy in the PRD exhibited a fluctuating increasing trend, and the region began to enter the basic coordination period in 2007; ③ in terms of the spatial distribution of the coordination degree, there was generally a circular pattern, with the Pearl River Estuary cities as the core and a decrease in the value towards the periphery; ④ the coordinated development model is divided into balanced development, economic guidance, and ESV guidance, among which balanced development is the major type; ⑤ the results of the GTWR reveal that the influencing factors exhibited significant spatial–temporal heterogeneity. Government intervention and openness were the dominant factors affecting the coordination, and the normalised difference vegetation index was the main negative influencing factor. [ABSTRACT FROM AUTHOR]

The tertiary industry has become the main driving force for China's economic development, and the adjustment and optimization of its structure are important prerequisites for achieving high-quality economic development. Existing studies have mostly focused on the spatial layout and influencing factors of the tertiary industry, with insufficient exploration of its internal structure. In this study, the PRD urban agglomeration is selected as the study area. On the basis of classifying the tertiary industry, the Dagum Gini coefficient, kernel density estimation, and local spatial autocorrelation are used to explore the spatial differentiation of various tertiary industries. The influencing factors are analyzed using geographical detectors, and suggestions for future development strategies are proposed. The results show that in terms of regional differentiation, the agglomeration of various tertiary industries in Guangzhou and Shenzhen is the most significant, but there is insufficient spillover to surrounding cities. In terms of development structure, the level of agglomeration of the consumptive tertiary industry is higher, the public tertiary industry tends to be more evenly distributed, and the productive tertiary industry is relatively dispersed. In terms of influencing factors, the interaction between population and employment dominates the spatial differentiation and evolution of the tertiary industry in the PRD urban agglomeration. Therefore, in the future, the tertiary industry in PRD urban agglomeration should promote the optimization of industrial structure and regional coordinated development under the guidance of the government. [ABSTRACT FROM AUTHOR]

The polycentric spatial structure is the most common spatial form of urban agglomerations, so exploring the evolution of this structure and analyzing its influencing factors is of great significance for the optimization of the spatial structure of urban agglomerations. However, there are relatively few studies on the topic that fuse multisource big data analysis, especially in the urban agglomeration of Western China. Therefore, this study uses a fusion of nighttime light (NTL) data, point of interest (POI) data and LandScan data to identify the polycentric spatial structure and its evolution in the Kunming–Yuxi (Kunyu) urban agglomeration and analyzes the factors that have dominated its evolution at different periods using geographic detectors. Results show that the fusion of multisource big data are more in line with the actual development process of the Kunyu urban agglomeration and the factors that have dominated the spatial evolution at different periods vary but the government and sectors have gradually become increasingly important. This study provides a feasible path for exploring urban spatial evolution through the fusion analysis of multisource big data in the Kunyu urban agglomeration and provides a reference for the key directions of urban agglomeration planning and development at different periods. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) is an emerging and promising antimicrobial agent for treating biofilms, yet its enrichment in biofilms has become a major challenge. In this contribution, iron oxide-polydopamine (Fe3O4-PDA) core–shell nanoparticles were fabricated and then loaded with (N,N′-di-sec-butyl-N,N′-dinitroso-1,4-phenylenediamine, BNN6), a NO donor, to yield a magnetic field-driven and near-infrared (NIR)-responsive NO delivery system for biofilm treatment. In a magnetic field, the nanoparticles were found to penetrate into and accumulate in the biofilms more effectively, resulting in a higher concentration of the nanoparticles in the biofilms, which upon exposure to NIR laser (808 nm) irradiation, heat up the local environment by the photothermal effect to trigger the release of more NO in a controlled manner. The combination of photothermal and NO gas therapies exhibited a synergistic antibacterial effect against Staphylococcus aureus biofilms in vitro, and the nanoparticles showed excellent antibacterial properties in vivo in the treatment of the mice wound infection model and accelerated the wound healing process. Overall, the Fe3O4-PDA–BNN6 nanoparticles realized spatial- and temporal-controlled release of NO in biofilms using NIR under a magnetic field, providing an effective antibiofilm strategy and taking the NO gas therapy one step closer to clinical application. [ABSTRACT FROM AUTHOR]

With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important reference to judge the level of urban development. The accurate extraction of urban built-up area plays an important role in formulating scientific planning thus to promote the healthy development of both urban area and rural area. Although nighttime light (NTL) data are used to extract urban built-up areas in previous studies, there are certain shortcomings in using NTL data to extract urban built-up areas. On the other hand, point of interest (POI) data and population migration data represent different attributes in urban space, which can both assist in modifying the deficiencies of NTL data from both static and dynamic spatial elements, respectively, so as to improve the extraction accuracy of urban built-up areas. Therefore, this study attempts to propose a feasible method to modify NTL data by fusing Baidu migration (BM) data and POI data thus accurately extracting urban built-up areas in Guangzhou. More accurate urban built-up areas are extracted using the method of U-net deep learning network. The maximum built-up area extracted from the study is 1103.45 km2, accounting for 95.21% of the total built-up area, and the recall rate is 0.8905, the precision rate is 0.8121, and the F1 score is 0.8321. The results of using POI data and BM data to modify NTL data to extract built-up areas have not been significantly improved due to the fact that the more data get fused, the more noise there would be, which would ultimately affect the results. This study analyzes the feasibility and insufficiency of using big data to modify NTL data through data fusion and feature extraction system, which has important theoretical and practical significance for future studies on urban built-up areas and urban development. [ABSTRACT FROM AUTHOR]

Long‐chain branched poly (lactic acid) (LCB‐PLA) with antimicrobial functional group was fabricated through a two‐step ring‐opening reactive processing for improving antimicrobial properties and broadening the PLA processing window. A combination of linear viscoelasticity and the branch‐on‐branch (BOB) model predicted probable compositions and chain topologies of the products. It is also explored the antimicrobial properties of LCB‐PLA to provide a theoretical basis for broadening the possible applications. [ABSTRACT FROM AUTHOR]

As one of the most important causes of water quality deterioration, NPS (non-point source) pollution has become an urgent environmental and livelihood issue. To date, there have been only a few studies focusing on NPS pollution conforming to the estimation, and the pollution sources are mainly concentrated in nitrogen and phosphorus nutrients. Unlike studies that only consider the intensity of nitrogen and phosphorus loads, the NPS pollution risk for the China's Fuxian Lake Basin was evaluated in this study by using IECM (Improve Export Coefficient Model) and RUSLE (Revised Universal Soil Loss Equation) models to estimate nitrogen and phosphorus loads and soil loss and by using a multi-factor NPS pollution risk assessment index established on the basis of the data mentioned above. First, the results showed that the load intensity of nitrogen and phosphorus pollution in the Fuxian Lake Basin is low, so agricultural production and life are important sources of pollution. Second, the soil loss degree of erosion in the Fuxian Lake is mild, so topography is one of the most important factors affecting soil erosion. Third, the risk of NPS pollution in the Fuxian Lake Basin is at a medium level and its spatial distribution characteristics are similar to the intensity characteristics of nitrogen and phosphorus loss. Nitrogen, phosphorus, sediment, and mean concentrations are important factors affecting NPS pollution. These factors involve both natural and man-made environments. Therefore, it is necessary to comprehensively consider the factors affecting NPS in order to assess the NPS risk more accurately, as well as to better solve the problem of ecological pollution of water resources and to allow environmental restoration. [ABSTRACT FROM AUTHOR]

The accurate delineation of urban agglomeration boundary is conductive to not only the better understanding of the development relationship between cities in urban agglomeration but also to the guidance of regional functions as well as the formulation of regional management policies. At the same time, the fusion of land relations and urban internal relations can greatly improve the accuracy of the delineation of urban agglomeration boundary. Still, for all that, previous studies delineated the boundary only from the perspective of land relations. In this study, firstly, wavelet transform is used to fuse Night-time Light data (NTL), POI (Point of Interest) data and Tencent Migration data, respectively. Then, the image is segmented by multiresolution segmentation to delineate the urban agglomeration boundary of GBA. Finally, the results are verified. The results show that the accuracy of urban agglomeration boundary delineated by NTL data is 85.57%, with the Kappa value as 0.6256, respectively. While, after fusing POI data, the accuracy is 88.97%, with the Kappa value as 0.7011, respectively. What is more, the accuracy of delineating urban agglomeration boundary by continuous fusion of population movement data reaches 93.60%, and that of Kappa value as 0.8155. Therefore, it can be concluded that compared with delineating the boundary of urban agglomeration only based on land relations, the fusion of population movement data of urban agglomerations by wavelet transform strengthens the interconnection between cities in urban agglomeration and contributes to the accurate division of urban agglomeration boundaries. What is more, such accurate delineation not only has important practical value for optimizing the spatial structure of urban agglomerations, but also assists in the formulation of regional management and development planning policies. [ABSTRACT FROM AUTHOR]

As the most infectious disease in 2020, COVID-19 is an enormous shock to urban public health security and to urban sustainable development. Although the epidemic in China has been brought into control at present, the prevention and control of it is still the top priority of maintaining public health security. Therefore, the accurate assessment of epidemic risk is of great importance to the prevention and control even to overcoming of COVID-19. Using the fused data obtained from fusing multi-source big data such as POI (Point of Interest) data and Tencent-Yichuxing data, this study assesses and analyzes the epidemic risk and main factors that affect the distribution of COVID-19 on the basis of combining with logistic regression model and geodetector model. What's more, the following main conclusions are obtained: the high-risk areas of the epidemic are mainly concentrated in the areas with relatively dense permanent population and floating population, which means that the permanent population and floating population are the main factors affecting the risk level of the epidemic. In other words, the reasonable control of population density is greatly conducive to reducing the risk level of the epidemic. Therefore, the control of regional population density remains the key to epidemic prevention and control, and home isolation is also the best means of prevention and control. The precise assessment and analysis of the epidemic conducts by this study is of great significance to maintain urban public health security and achieve the sustainable urban development. [ABSTRACT FROM AUTHOR]

Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction. [ABSTRACT FROM AUTHOR]