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5G, the most disruptive innovation, had played a significant role in the COVID-19 pandemic prevention and control. However, as a novel technology and context, we have little knowledge about how 5G enabled pandemic prevention and control. This study collected 212 cases and conducted qualitative research to explore how the 5G worked in prevention and control. Based on the concepts of materiality and affordance, we grounded two affordances of spatialization and de-spatialization from the data. Spatialization provides non-contact ways to complete the tasks which are supposed to be completed in contact, and de-spatialization provides remote operations to complete the tasks which are supposed to be completed on-site. Spatialization and de-spatialization enabled the diagnosis and treatment of the infectors to relieve the unbalance of medical staff, cutting the infectious route to contain the viral spread, and logistic supply to support the prevention and control. Our study offers theoretical contributions to digital pandemic prevention and control, and the literature on 5G also offers practical implications.

Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national studies, or at finer scales to develop modern site-specific management approaches. These new approaches to the application of crop models raise new questions concerning the evaluation of their performance, particularly for downscaled applications. This article first reviews the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework. A strong focus is then given to the evaluation of these spatialized crop models. Evaluation metrics, including the consideration of dedicated sensitivity indices are reviewed from the published studies. Using a simple example of a spatialized crop model being used to define management zones in precision viticulture, it is shown that classical model evaluation involving aspatial indices (e.g. the RMSE) is not sufficient to characterize the model performance in this context. A focus is made at the end of the review on potentialities that a complementary evaluation could bring in a precision agriculture context.

There are various health issues associated with the different stages of long-distance pipeline transportation. These issues pose potential risks to environmental pollution, resource waste, and the safety of human life and property. It is essential to have real-time knowledge of the overall health status of pipelines throughout their entire lifecycle. This article investigates various health-monitoring technologies for long-distance pipelines, providing references for addressing potential safety issues that may arise during long-term transportation. This review summarizes the factors and characteristics that affect pipeline health from the perspective of pipeline structure health. It introduces the principles of major pipeline health-monitoring technologies and their respective advantages and disadvantages. The review also focuses on the application of Distributed Acoustic Sensing (DAS) technology, specifically time and space continuous monitoring technology, in the field of pipeline structure health monitoring. This paper discusses the process of commercialization development of DAS technology, the main research progress in the experimental field, and the open research issues. DAS technology has broad application prospects in the field of long-distance transportation pipeline health monitoring.

Binaural audio provides human listeners with an immersive spatial sound experience, but most existing videos lack binaural audio recordings. We propose an audio spatialization method that draws on visual information in videos to convert their monaural (single-channel) audio to binaural audio. Whereas existing approaches leverage visual features extracted directly from video frames, our approach explicitly disentangles the geometric cues present in the visual stream to guide the learning process. In particular, we develop a multi-task framework that learns geometry-aware features for binaural audio generation by accounting for the underlying room impulse response, the visual stream’s coherence with the sound source(s) positions, and the consistency in geometry of the sounding objects over time. Furthermore, we introduce two new large video datasets: one with realistic binaural audio simulated for real-world scanned environments, and the other with pseudo-binaural audio obtained from ambisonic sounds in YouTube 360∘ videos. On three datasets, we demonstrate the efficacy of our method, which achieves state-of-the-art results.

Music has been primarily conceived as a temporal art. However, over the last two centuries or so, researchers across different disciplines including musicology, psychology, and philosophy, have been intrigued by the spatial nature of music and sounds, using spatial concepts to define music. This paper aims to demonstrate that an understanding of music perception from a temporal perspective inherently implies a certain spatial dimension. To do this, first, I briefly examine some key arguments that lead to conceiving sound perception in temporal terms. At the same time, I highlight some of the limitations of a purely temporal account of sound perception which necessitate the incorporation of spatial considerations into the conceptualization of sound perception. Consequently, I move on to consider prominent spatial accounts of musical sounds that have been elaborated by psychologists, musicologists, and music composers. In conclusion, I discuss some of the challenges arising from the analogy between music and space, whether conceived in perceptual or cognitive terms.

The county is the basic administrative unit of China, and the spatialization of carbon budget at the county scale plays an irreplaceable role in deepening the understanding of the carbon emission mechanism and spatial pattern. Yueqing County, an economically developed county in the Yangtze River Delta of China, was selected as the study area, the spatial pattern of the carbon budget and the optimal resolution of the spatialization at the county level were dissected on the basis of accurate accounting, and driving factors of carbon emissions were further identified using the geographically weighted regression model. The results indicated that (1) the carbon emissions were mainly generated from fossil fuel combustion related to energy, accounting for 98.8% of the total carbon budget in the study area; (2) the optimal resolution of spatialization was 200 m and carbon emissions were concentrated in the southeast of the study area; (3) energy intensity, energy structure, per capita GDP, and urbanization rate were positively correlated with carbon emissions, while population played a bidirectional role in carbon emissions. This study not only strengthens the understanding of the patterns and drivers of the carbon budget but also establishes a theoretical framework and operational tools for policymakers to formulate solutions to mitigate the carbon crisis.

To measure the equity of urban park green space, spatial matching between service supply and user group demand should be taken into consideration. However, if the demographic data, with the administrative division as the basic unit, are directly applied to characterize the spatial distribution of a user group, it may introduce inevitable deviation into the evaluation results due to the low-resolution nature and modifiable areal unit problem of such data. Taking the central area of Wuhan as an example, the population data spatialization method based on land use modeling was used to build a geographically weighted regression (GWR) model of land cover type and demographic data, and the spatial distribution of the population of the 150 m grid was obtained by inversion. Then, the equity of park green space in Wuhan central city was evaluated by population spatial data and network accessibility. The results showed that (1) the range of park green space in the central urban area of Wuhan was within a walking distance of 15 min, accounting for 25.8% of the total study area and covering 54.2% of the population in the study area; (2) the equity of park green space in Hongshan District was the worst; (3) and the use of population spatial data can measure equity on a more precise scale.

The composition of bioactive polyphenols from grape canes, an important viticultural byproduct, was shown to be varietal-dependent; however, the influence of soil-related terroir factors remains unexplored. Using spatial metabolomics and correlation-based networks, we investigated how continuous changes in soil features and topography may impact the polyphenol composition in grape canes. Soil properties, topography, and grape cane extracts were analyzed at georeferenced points over 3 consecutive years, followed by UPLC-DAD-MS-based metabolomic analysis targeting 42 metabolites. Principal component analyses on intra-vintage metabolomic data presented a good reproducibility in relation to geographic coordinates. A correlation-driven approach was used to explore the combined influence of soil and topographic variables on metabolomic responses. As a result, a metabolic cluster including flavonoids was correlated with elevation and curvature. Spatial metabolomics driven by correlation-based networks represents a powerful approach to spatialize field-omics data and may serve as new field-phenotyping tool in precision agriculture.

Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at −52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.

PurposeThe planetary boundaries framework contains regional boundaries in addition to global boundaries. Geographically resolved methods to assess regional environmental impacts are therefore needed. Existing planetary boundaries-based life cycle assessment (PB-LCA) methods have limited geographical resolution or are not applicable to full product systems, due to high spatial requirements on inventory data. Here, we enable PB-LCA of full product systems across a comprehensive set of regional and global PB impact categories.MethodsWe propose comparing environmental impacts of individual processes within a product system to assigned shares of regional or global safe operating space (SOS). This is followed by aggregation of process-level results so that accumulated exceedance of assigned SOS is derived across the entire life cycle. We then present a procedure for aggregating geographically resolved characterization factors (CFs) and SOS to country, continent and global levels, ensuring compatibility with typical life cycle inventory results. We then apply the new techniques to a model laundry case study. It involves 61 selected processes, two geographically resolved PB-LCA methods, related to impacts from freshwater use and nitrogen emissions, and a largely spatially generic PB-LCA method that covers a comprehensive set of impact categories.Results and discussionThe calculation of accumulated exceedance of assigned SOS may help inform decisions about where in a life cycle to focus impact reduction efforts most urgently. The number of case study processes that exceed their assigned SOS differs when applying the geographically resolved methods, as opposed to the largely spatially generic method. Case study results differ greatly across PB-LCA impact categories, reaffirming the importance of covering a comprehensive set. Geographically resolved methods are needed for all regional impact categories and software support would be advantageous. Existing methods will require periodic updates to reflect ongoing advancements in PB science. Best practice approaches or a consensus for assigning regional SOS to processes are needed.Conclusions and outlookOur study provides a step towards greater operability of geographically resolved PB-LCA methods by enabling application to a full product system within an assessment that covers a comprehensive set of impact categories. The case study application shows potential advantages of the process-level approach and points to the need for quantifying uncertainties in such assessments. Future studies should seek to explore the potential role of PB-LCA in decision support compared with conventional LCA.