Spatiotemporal Heterogeneity of Forest Fire Occurrence Based on Remote Sensing Data: An Analysis in Anhui, China

A forest fire is a destructive disaster that is difficult to handle and rescue and can pose a significant threat to ecosystems, society, and humans. Since driving factors and their effects on forest fires change over time and space, exploring the spatiotemporal patterns of forest fire occurrence should be addressed. To better understand the patterns of forest fire occurrence and provide valuable insights for policy making, we employed the Geographically and Temporally Weighted Regression (GTWR) model to investigate the varying spatiotemporal correlations between driving factors (vegetation, topography, meteorology, social economy) and forest fires in Anhui province from 2012 to 2020. Then we identified the dominant factors and conducted the spatiotemporal distribution analysis. Moreover, we innovatively introduced nighttime light as a socioeconomic driving factor of forest fires since it can directly reflect more comprehensive information about the social economy than other socioeconomic factors commonly used in previous studies. This study applied remote sensing data since the historical statistic data were not detailed. Here, we obtained the following results. (1) There was a spatial autocorrelation of forest fires in Anhui from 2012 to 2020, with high-high aggregation of forest fires in eastern cities. (2) The GTWR model outperformed the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression model (GWR), implying the necessity of considering temporal heterogeneity in addition to spatial heterogeneity. (3) The relationships between driving factors and forest fires were spatially and temporally heterogeneous. (4) The forest fire occurrence was mainly dominated by socioeconomic factors, while the dominant role of vegetation, topography, and meteorology was relatively limited. It’s worth noting that nighttime light played the most extensive dominant role in forest fires of Anhui among all the driving factors in the years except 2015

Spatial and Temporal Variability of Upwelling in the West-Central South China Sea and Its Relationship with the Wind Field

The west-central South China Sea upwelling event is a critical process that regulates the climate and marine ecosystem in the region. In this study, we used sea surface temperature (SST) satellite data from 2000 to 2018 to analyze the spatial and temporal characteristics of upwelling in the west-central South China Sea and combined the wind field data to investigate the effects of wind direction and speed on upwelling. We divided the upwelling sea area into three regions based on the different shoreline angles along the eastern coast of the South China Peninsula: OU_1, OU_2, and OU_3. Our results showed that the upwelling events occurred mainly from May to September in the OU_1 and OU_2 waters. The empirical orthogonal function (EOF) decomposition of the monthly mean SST moment level field indicated a cyclical interannual variation of upwelling in the west-central South China Sea. The correlation analysis showed that wind direction changes have a significant impact on the upwelling intensity center, with the upwelling intensity center moving towards high latitudes and away from the coast when the wind direction changes from north to east. When the wind direction changes from east to south, the upwelling intensity center moves towards low latitudes and near the coast. The average lag time of upwelling events to the wind field in the central and western South China Sea was 38.9 h, with OU_2 showing a longer response time than the other seas. Our study provides important insights into the mechanisms governing upwelling in the west-central South China Sea, which can effectively promote the rational use of ecological resources and provide a scientific basis for marine ecological protection in the region

High-spatial-resolution remote sensing image segmentation using adaptive watershed-driven joint MDEDNet

When segmenting high-spatial-resolution remote sensing images using convolution neural networks, it is difficult to balance the number of segmented regions against the contour accuracy. This article proposes a segmentation method that combines the minimum area adaptive watershed transform based on morphological reconstruction with the modified deep edge detection network (MDEDNet). First, the minimum area adaptive watershed algorithm is adopted to reconstruct the gradient features of the image and perform watershed segmentation, giving the initial segmentation results. A ground object contour optimization method and MDEDNet are then used to further improve the accuracy of the segmentation, resulting in the final segmented image. Experiments on image sets of varying complexity indicate that the algorithm proposed in this article can achieve accurate image segmentation with good segmentation results and fast operation efficiency

Forecasting Albacore (<i>Thunnus alalunga</i>) Fishing Grounds in the South Pacific Based on Machine Learning Algorithms and Ensemble Learning Model

To achieve high-precision forecasting of different grades of albacore fishing grounds in the South Pacific Ocean, we used albacore fishing data and marine environmental factors data from 2009 to 2019 as data sources. An ensemble learning model (ELM) for albacore fishing grounds forecasting was constructed based on six machine learning algorithms. The overall accuracy (ACC), fishing ground forecast precision (P) and recall (R) were used as model accuracy evaluation metrics, to compare and analyze the accuracy of different machine learning algorithms for fishing grounds forecasting. We also explored the forecasting capability of the ELM for different grades of fishing grounds. A quantitative evaluation of the effects of different marine environmental factors on the forecast accuracy of albacore tuna fisheries was conducted. The results of this study showed the following: (1) The ELM achieved high accuracy forecasts of albacore fishing grounds (ACC = 86.92%), with an overall improvement of 4.39~19.48% over the machine learning models. (2) A better forecast accuracy (R2 of 81.82–98%) for high-yield albacore fishing grounds and a poorer forecast accuracy (R1 of 47.37–96.15%) for low-yield fishing grounds were obtained for different months based on the ELM; the high-yield fishing grounds were distributed in the sea south of 10° S. (3) A feature importance analysis based on RF found that latitude (Lat) had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades from February to December (0.377), and Chl-a had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades in January (0.295), while longitude (Lon) had the smallest effect on the forecast of different grades of fishing grounds (0.037)

Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap

As a rising task, panoptic segmentation is faced with challenges in both semantic segmentation and instance segmentation. However, in terms of speed and accuracy, existing LiDAR methods in the field are still limited. In this paper, we propose a fast and high-performance LiDAR-based framework, referred to as Panoptic-PHNet, with three attractive aspects: 1) We introduce a clustering pseudo heatmap as a new paradigm, which, followed by a center grouping module, yields instance centers for efficient clustering without object-level learning tasks. 2) A knn-transformer module is proposed to model the interaction among foreground points for accurate offset regression. 3) For backbone design, we fuse the fine-grained voxel features and the 2D Bird's Eye View (BEV) features with different receptive fields to utilize both detailed and global information. Extensive experiments on both SemanticKITTI dataset and nuScenes dataset show that our Panoptic-PHNet surpasses state-of-the-art methods by remarkable margins with a real-time speed. We achieve the 1st place on the public leaderboard of SemanticKITTI and leading performance on the recently released leaderboard of nuScenes

EA-UNet Based Segmentation Method for OCT Image of Uterine Cavity

Optical coherence tomography (OCT) image processing can provide information about the uterine cavity structure, such as endometrial surface roughness, which is important for the diagnosis of uterine cavity lesions. The accurate segmentation of uterine cavity OCT images is a key step of OCT image processing. We proposed an EA-UNet-based image segmentation model that uses a U-Net network structure with a multi-scale attention mechanism to improve the segmentation accuracy of uterine cavity OCT images. The E(ECA-C) module introduces a convolutional layer combined with the ECA attention mechanism instead of max pool, reduces the loss of feature information, enables the model to focus on features in the region to be segmented, and suppresses irrelevant features to enhance the network’s feature-extraction capability and learning potential. We also introduce the A (Attention Gates) module to improve the model’s segmentation accuracy by using global contextual information. Our experimental results show that the proposed EA-UNet can enhance the model’s feature-extraction ability; furthermore, its MIoU, Sensitivity, and Specificity indexes are 0.9379, 0.9457, and 0.9908, respectively, indicating that the model can effectively improve uterine cavity OCT image segmentation and has better segmentation performance

Advanced Material Strategies for Next-Generation Additive Manufacturing

Additive manufacturing (AM) has drawn tremendous attention in various fields. In recent years, great efforts have been made to develop novel additive manufacturing processes such as micro-/nano-scale 3D printing, bioprinting, and 4D printing for the fabrication of complex 3D structures with high resolution, living components, and multimaterials. The development of advanced functional materials is important for the implementation of these novel additive manufacturing processes. Here, a state-of-the-art review on advanced material strategies for novel additive manufacturing processes is provided, mainly including conductive materials, biomaterials, and smart materials. The advantages, limitations, and future perspectives of these materials for additive manufacturing are discussed. It is believed that the innovations of material strategies in parallel with the evolution of additive manufacturing processes will provide numerous possibilities for the fabrication of complex smart constructs with multiple functions, which will significantly widen the application fields of next-generation additive manufacturing

A high-voltage symmetric sodium ion battery using sodium vanadium pyrophosphate with superior power density and long lifespan

Sodium ion batteries have been considered as promising alternatives to lithium ion batteries for large-scale renewable energy and smart grids applications due to their low cost and rich resources. However, critical drawbacks such as low energy density and poor stability are hindering their development and application. In this work, a stable symmetric sodium ion cell using sodium vanadium pyrophosphate Na6·88V2·81(P2O7)4 as the positive and negative electrodes is fabricated. Since the bipolar Na6·88V2·81(P2O7)4 possesses high sodium-ion diffusion ability and stable structure framework, it demonstrates promising rate capability and cycling performance as both the positive and negative electrodes. The symmetric sodium ion cell, with Na6·88V2·81(P2O7)4 as the active material in both the positive and negative electrodes, exhibits a high operating voltage plateau of ≈3.0 V, distinct rate capability (e.g. 45 mAh g−1 at 10 C) and excellent cycling performance (e.g. 71.1% capacity retention after 1000 cycles at 2 C). The results of this work represent a step toward the development of symmetric sodium ion batteries with high operating voltage, good rate capability and long lifespan