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Abstract Branching networks are key elements in natural landscapes and have attracted sustained research interest across the geosciences and numerous intersecting fields. The prevailing consensus has long held that branching networks are optimized and exhibit fractal properties adhering to power-law scaling relationships. However, tidal networks in coastal wetlands and mudflats exhibit scaling properties that defy conventional power-law descriptions, presenting a longstanding enigma. Here we show that the observed atypical scaling represents a universal deviation from an ideal fractal branching network capable of fully occupying the available space. Using satellite imagery of tidal networks from diverse global locations, we identified an inherent “laziness” in this deviation—where the increased ease of channel formation paradoxically decreases the space-filling efficiency of the network. We developed a theoretical model that reproduces the ideal fractal branching network and the laziness phenomenon. The model suggests that branching networks can emerge under a localized competition principle without adhering to conventionally assumed optimization-driven processes. These results reveal the dual nature of branching networks, where “laziness” complements the well-known optimization process. This property provides more flexible strategies for controlling tidal network morphogenesis, with implications for coastal management, wetland restoration, and studies in fluvial and planetary systems.

Offshore wind power is a crucial clean energy source for coastal countries and advances blue economies. Accurate spatial mapping of offshore wind turbines supports energy assessment and the sustainable development of marine resources. Offshore wind power has developed rapidly in China, but the spatiotemporal distribution characteristics of offshore wind turbines and farms remain poorly understood. Extracting nearshore small targets from Sentinel-1 synthetic aperture radar images remain challenging due to noise from the rough sea surface background and the diverse substrate types in offshore wind turbine (OWT) distributions. Here, to address these issues, we first created a dataset of China’s offshore wind turbines (COWTs) that contained typical substrate types (e.g. seawater and tidal flats). Then, a deep learning model integrating an attention mechanism and receptive fields was used to accurately detect COWTs. The well-trained model was used to detect the changes in COWTs from 2015 to 2022. Our model detected an increase in the number of offshore wind farms in China from 7 to 114 (clusters counted as an offshore wind farm), while COWTs increased from 305 to 6451 from 2015 to 2022. The Taiwan Strait exhibited the highest wind speeds, yet the Jiangsu and Guangdong regions had the most installed turbines. Over 90% of COWTs were located in areas with offshore wind energy resources less than 500 W/m3, indicating a mismatch between COWT installation and wind resource distribution. This study demonstrated temporal and geographical generalizability, which is promising for global wind power detection. Our analysis of spatiotemporal variation in COWTs facilitates informed decision-making for future field establishment and sustainable marine planning.

ABSTRACTTidal wetlands provide a variety of ecosystem services to coastal communities but suffer severe losses due to anthropogenic activities in the Yangtze River Estuary (YRE). However, the detailed dynamics of tidal wetlands have not been well studied with sufficient spatiotemporal resolution. Here, we proposed a rapid classification method that integrates the COntinuous monitoring of Land Disturbance (COLD) algorithm and Median Composite (MC) based on the dense Landsat time series to track the dynamic processes of tidal wetlands in the YRE from 1990 to 2020. The results showed that the COLD-MC demonstrated remarkable effectiveness in detecting the change of tidal wetlands and excellent overall accuracy and kappa coefficient ranging from 90% to 96% and 0.89–0.95, respectively. The overall accuracy of change detection was 97% with an absolute error of 0.4 years. We found that the total area of tidal wetlands experienced a net loss of 59.75 km2 in the YRE, but the gain and loss of the study period were 1556.07 and 1615.82 km2, respectively. Land reclamation, sediment reduction, and Spartina alterniflora invasion pose significant threats to tidal wetlands. Sustainable management could be implemented through the establishment of nature reserves and ecological sediment enhancement engineering projects.

Objective: To study the preventive effect of ethanolic extract from Chimonanthus praecox flower on obesity induced by high-fat diet in mice. Methods: A total of 50 C57BL/6 mice were divided into five groups of blank control, model control, low-, medium- and high-dose Chimonanthus praecox flower extract (CPFE) treatment. The mice in the blank control group were provided with a basal diet whereas those in the other groups were given a high-fat diet. The mice in the low-, medium- and high-dose groups were orally administered with CPFE at doses of 0.2, 0.4 and 0.8 g/(kg mb·d), containing 24.81, 49.62 and 99.25 mg/(kg mb·d) of Chimonanthus praecox flower polyphenols, respectively. The blank control, model control groups were administered with 0.9% NaCl. The administration lasted for 4 weeks. Then, body weight, serum biochemical indicators, organ antioxidant capacity were measured and pathological sections of liver tissue were examined. Results: The body weight, liver coefficient, serum levels of TC, LDL-C, organ levels of MDA in the model control group were significantly higher than those in the blank control group (P

Abstract Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.

Estuarine wetland vegetation (EWV) serves as a critical indicator of ecosystem health but is increasingly threatened by human activities and climate change. Though remote sensing (RS) data provide a holistic view of vegetation change with sufficient spatial resolution, temporal changes in EWV are still ambiguous because of the twisted signals between phenological dynamics and multi-scale external disturbances. To address these knowledge gaps, a method integrated with multi-resolution analysis wavelet transform (MRA-WT) and time-series RS images were utilized in this study to uncover detailed multiple-scale EWV dynamics from the perspectives of vegetation phenology and evolution trends. Our results showed that: 1) The MRA-WT effectively decomposes NDVI at multiple scales, revealing intra- and inter-annual dynamics of EWV in complex environments. 2) In the Jiuduansha (JDS) wetland, the phenological characteristics and evolution processes of EWV are determined by vegetation type and geographical location. 3) The EWV in the JDS wetland remained stable with gradual growth from 2000 to 2021, despite the impacts of sediment reduction, estuarine projects, and invasive species. However, a declining trend post-2018 serves as a warning sign of potential ecosystem issues. Moreover, higher spatial and temporal resolutions enable more precise tracking of fine-scale details in the dynamics of EWV with the proposed method.

Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal dynamics of tidal flats. However, there is no cost-effective way to map morphological changes on a large spatial scale due to the inaccessibility of the mudflats. In this study, we proposed a pixel-based multi-indices tidal flat mapping algorithm that precisely characterizes 2D/3D morphological changes in tidal flats in northern China using time-series remote sensing data. An overall accuracy of 0.95 in delineating tidal flats to a 2D extent was achieved, with 11,716 verification points. Our results demonstrate that the reduction in sediment discharge from rivers along the coastlines of the Yellow and Bohai Seas has resulted in an overall decline in the area of tidal flats, from 4856.40 km2 to 4778.32 km2. Specifically, 3D analysis showed that significant losses were observed in the mid-to-high-tidal flat zones, while low-elevation tidal flats experienced an increase in area due to the transformations in mid-to-high-tidal flats. Our results indicate that the sediment inputs from rivers and the succession of native vegetation are the primary drivers leading to 2D/3D morphological changes of tidal flats following the cessation of extensive land reclamation in northern China.

Intertidal vegetation plays an essential role in habitat provision for waterbirds but suffers great losses due to human activities. However, it is challenging in tracking the human-driven loss and degradation of intertidal vegetation due to rapid urbanization in a high temporal resolution. In this study, a methodological framework based on full Landsat time-series (FLTS) is proposed to detect the year of change (YOC) of intertidal vegetation converted to impervious surfaces (ISs) and artificial ponds (APs), and the condition of the remaining intertidal vegetation was also assessed by FLTS, in the Fujian province, a subtropical coastal area lying in southeast China. The accuracies of the YOC detection of intertidal vegetation converted to IS and AP were 91.84% and 72.73%, with mean absolute errors of 0.26 and 1.06, respectively. The total areas of intertidal vegetation encroached by IS and AP were 31.68 km2 and 23.85 km2, respectively. Most ISs were developed later than 2010, and most APs were developed earlier than 2005, which are highly related to the implementation of local policies for economic development. The remaining intertidal vegetation in growing, stable, and degraded conditions were 43.05%, 56.38%, and 0.57%, respectively. The results indicated that areas of intertidal vegetation were reclaimed for anthropogenic uses at a considerable rate, although the intertidal vegetation still increased owing to natural development after the establishment of natural reserves. The study demonstrates that the FLTS has capacities in monitoring the dynamics in coastal zones solely for its dense earth observations.

Photovoltaic (PV) solar energy generation attracts considerable attention to archive carbon neutrality goals worldwide. Geospatial data describing the PV system based on satellite images are critical for PV deployment. However, it remains challenging to obtain relative data in coastal zones due to frequent cloud cover. There have also been relatively few studies on spatial analysis of PV development in terms of solar resources and the energy demands. Therefore, this study took Chinese coastal provinces as example, and proposed a random forest classification method with information on morphology, optics, and SAR to address cloud pollution. The results obtained were analyzed with geographical and socioeconomic factors to determine its development status. The results have shown that (1) by October 2022, the total PV area in Chinese coastal provinces reached 837.3 km2, with an overall accuracy of 96.9 % and a Kappa coefficient of 0.91. Water-based PV represents 33.7 % which tended to be distributed near the shoreline. (2) Bounded by the Huai River, the PV density in the north was 9.0 km2 per 104 km2, which is more than twice that in the south. The development of water-based PV is a key reason for the high PV construction density in coastal areas. (3) PV distribution was slightly mismatched with solar resource and power demand, especially in Liaoning and Guangdong. Liaoning has relatively high potential for PV development. The study findings can play a key role in evaluation of PV power generation, development potential, and ecological environmental impacts.

The rapid, up-to-date, cost-effective acquisition and tracking of intertidal topography are the fundamental basis for timely, high-priority protection and restoration of the intertidal zone. The low cost, ease of use, and flexible UAV-based photogrammetry have revolutionized the monitoring of intertidal zones. However, the capability of the RTK-assisted UAV photogrammetry without ground control points, the impact of flight configuration difference, the presence of surface water in low-lying intertidal areas on the photogrammetric accuracy, and the potential of UAV/satellite Synergy remain unknown. In this paper, we used an RTK-assisted UAV to assess the impact of the above-mentioned considerations quantitatively on photogrammetric results in the context of annual monitoring of the Chongming Dongtan Nature Reserve, China based on an optimal flight combination. The results suggested that (1) RTK-assisted UAVs can obtain high-accuracy topographic data with a vertical RMSE of 3.1 cm, without the need for ground control points. (2) The effect of flight altitude on topographic accuracy was most significant and also nonlinear. (3) The elevation obtained by UAV photogrammetry was overestimated by approximately 2.4 cm in the low-lying water-bearing regions. (4) The integration of UAV and satellite observations can increase the accuracy of satellite-based waterline methods by 51%. These quantitative results not only provide scientific insights and guidelines for the balance between accuracy and efficiency in utilizing UAV-based intertidal monitoring, but also demonstrate the great potential of combined UAV and satellite observations in identifying coastal erosion hotspots. This establishes high-priority protection mechanisms and promotes coastal restoration.

The coastline plays an important role in indicating the conditions of social-economic development in the coastal zone. In this study, an integrated assessment framework was proposed to address the provincial and county-level spatiotemporal dynamics of continental coastlines from the perspectives of length, position, composition, and anthropogenic utilization quantitatively, and to explore the exact impacts of urbanization on coastline changes in the Fujian Province over the period from 1985 to 2020. Results showed that the total length of coastlines decreased first and then increased due to the different patterns of economic development. The proportion of artificial coastlines and the index of coastal utilization degree increased rapidly during the same period. Moreover, the seaward movement of coastlines due to the coastal reclamation projects resulted in a considerable increment in land areas. The pressure brought by the continuous concentration of population, built-up areas, and industrial districts under the rapid urbanization was the primary factor that increased the degree of anthropogenic disturbances in the coastal zone. Furthermore, the policies issued by the local or central government can be critical tipping points for coastline changes in different periods.

It has been stated that the current definitions of High Level Architecture and implementations of Runtime Infrastructure do not deal with all the current issues of interoperability. Hierarchical Federation Community (HFC), in which federations are organized into hierarchies, offers a means of enhancing interoperability of federation for large-scale distributed simulation. Previous emphasis in Time Management (TM) services has been based on time stamp ordering, which is both computation and communication intensive. We present a TM mechanism based on causal ordering for Federate Proxy enabled HFC using divide-and-conquer approach. The TM mechanism can avoid causal anomaly between messages, which is proven. Moreover, some advantages are listed, e.g. potential of performance improvement and supporting interoperability among heterogeneous federations utilizing different internal TM mechanisms. Finally, experiments have been conducted to benchmark the performance of the new TM mechanism with respect to the existing TM mechanism. [ABSTRACT FROM AUTHOR]