Lithium–sulfur (Li–S) batteries are regarded as the most promising next‐generation energy storage systems due to their high energy density and cost‐effectiveness. However, their practical ...applications are seriously hindered by several inevitable drawbacks, especially the shuttle effects of soluble lithium polysulfides (LiPSs) which lead to rapid capacity decay and short cycling lifespan. This review specifically concentrates on the shuttle path of LiPSs and their interaction with the corresponding cell components along the moving way, systematically retrospect the recent advances and strategies toward polysulfides diffusion suppression. Overall, the strategies for the shuttle effect inhibition can be classified into four parts, including capturing the LiPSs in the sulfur cathode, reducing the dissolution in electrolytes, blocking the shuttle channels by functional separators, and preventing the chemical reaction between LiPSs and Li metal anode. Herein, the fundamental aspect of Li–S batteries is introduced first to give an in‐deep understanding of the generation and shuttle effect of LiPSs. Then, the corresponding strategies toward LiPSs shuttle inhibition along the diffusion path are discussed step by step. Finally, general conclusions and perspectives for future research on shuttle issues and practical application of Li–S batteries are proposed.
This review summarizes the recent advances and strategies to suppress the shuttle effect of lithium polysulfides (LiPSs) in lithium–sulfur batteries. These strategies are composed of using the modified sulfur hosts to immobilize LiPSs, electrolyte systems to alleviate shuttle behavior, functional separator to intercept LiPSs, and anode surface engineering to avoid the chemical reaction between LiPSs and Li.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
To establish a new model for estimating ground-level PM2.5 concentration over Beijing, China, the relationship between aerosol optical depth (AOD) and ground-level PM2.5 concentration was derived and ...analysed firstly. Boundary layer height (BLH) and relative humidity (RH) were shown to be two major factors influencing the relationship between AOD and ground-level PM2.5 concentration. Thus, they are used to correct MODIS AOD to enhance the correlation between MODIS AOD and PM2.5. When using corrected MODIS AOD for modelling, the correlation between MODIS AOD and PM2.5 was improved significantly. Then, normalized difference vegetation index (NDVI), surface temperature (ST) and surface wind speed (SPD) were introduced as auxiliary variables to further improve the performance of the corrected regression model. The seasonal and annual average distribution of PM2.5 concentration over Beijing from 2014 to 2016 were mapped for intuitively analysing. Those can be regarded as important references for monitoring the ground-level PM2.5 concentration distribution. It is obviously that the PM2.5 concentration distribution over Beijing revealed the trend of "southeast high and northwest low", and showed a significant decrease in annual average PM2.5 concentration from 2014 to 2016.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The image labeling task of remote sensing image scene classification (RSSC) is based on the semantic content of remote sensing images. The semantic information within remote sensing photographs has ...become more complicated and difficult to detect as remote sensing technology has progressed. As a result, extracting more important semantic elements could aid in the completion of the RSSC assignment. Thus, in this research, we offer MLFC-Net, a multi-level semantic feature clustering attention model based on deep convolution neural networks (DCNNs) that extracts more accurate feature information. The concept of MLFC-Net stems from the utilization of rich spatial information found in remote sensing photos, but few approaches in the RSSC application considered merging general semantic feature information with clustered semantic feature information. By rearranging the weight of corresponding information, such as feature maps and tensor blocks of the feature map, we implemented the attention mechanism. To build a model with minimal computational cost and good portability, we use a channel-wise attention mechanism and an ensemble structure. We were able to improve the representation of several critical semantic aspects using the MLFC model. In the EuroSAT, UCM, and NWPU-RESISC45 RSSC datasets, the MLFC model's performance is demonstrated. And, on average, the MLFC model enhanced accuracy by 2.56 percent, 1.25 percent and 2.00 percent, respectively, producing results that were equivalent to the state-of-the-art.
•A novel attention module is proposed for remote sensing scene classification.•The mechanism relies on extracting features from different levels.•The proposed method achieved competitive performance.•The proposed method implemented a small computational complexity.•The proposed method is with high portability.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
As a result of the difficulties brought by COVID-19 and its associated lockdowns, many individuals and companies have turned to robots in order to overcome the challenges of the pandemic. Compared ...with traditional human labor, robotic and autonomous systems have advantages such as an intrinsic immunity to the virus and an inability for human-robot-human spread of any disease-causing pathogens, though there are still many technical hurdles for the robotics industry to overcome. This survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry. In each chapter, we cover both the advantages and the challenges for each robot, finding that robotics systems are overall apt solutions for dealing with many of the problems brought on by COVID-19, including: diagnosis, screening, disinfection, surgery, telehealth, care, logistics, manufacturing and broader interpersonal problems unique to the lockdowns of the pandemic. By discussing the potential new robot capabilities and fields they applied to, we expect the robotics industry to take a leap forward due to this unexpected pandemic.
Points-of-interest (POIs) are an important carriers of location text information in smart cities and have been widely used to extract and identify urban functional regions. However, it is difficult ...to model the relationship between POIs and urban functional types using existing methods due to insufficient POIs information mining. In this study, we propose a Global Vectors (GloVe)-based, POI type embedding model (GPTEM) to extract and identify urban functional regions at the scale of traffic analysis zones (TAZs) by integrating the co-occurrence information and spatial context of POIs. This method has three main steps. First, we utilize buffer zones centered on each POI to construct the urban functional corpus. Second, we use the constructed corpus and GPTEM to train POI type vectors. Third, we cluster the TAZs and annotate the urban functional types in clustered regions by calculating enrichment factors. The results are evaluated by comparing them against manual annotations and food takeout delivery data, showing that the overall identification accuracy of the proposed method (78.44%) is significantly higher than that of a baseline method based on word2vec. Our work can assist urban planners to efficiently evaluate the development of and changes in the functions of various urban regions.
Capturing finer and discriminative difference features (DF) is key to obtaining a high-quality change detection (CD) map. However, there is still significant scope for further study on fine-grained ...detection, especially concerning terms of improving structural integrity and reducing internal holes or sticking in DF. To this end, we propose a progressive difference amplification network (PDANet) with edge sensitivity to detect changed areas in optical remote sensing images (RSI), where the key point is to amplify DF and reinforce edge detail to improve CD accuracy. The edge sensitivity encoder (ES) is designed to capture the long-distance dependency, which compensates for the limited receptive fields of the convolutional neural network with fixed kernels. Meanwhile, we introduce the prior edge in the network training stage, which collaborates with the ESE to improve the structural integrity of the changed areas. On the other hand, the difference amplification decoder is proposed to enhance the representation of the changed areas, and it is achieved by integrating multi-scale DF and reconstructing the original single RSI using DF as full-stage guidance. Finally, the CD map and edge map are predicted based on the reconstructed feature and the maximum scale DF. Extensive experiments on one instance dataset and three CD benchmark datasets demonstrate that PDANet outperforms the state-of-the-art CD competitors both qualitatively and quantitatively.
ABSTRACTIn the realm of geospatial services and applications, the accuracy of address information is of utmost importance. Traditional methods of data collection, being both labor-intensive and ...costly, have prompted researchers to turn to Volunteered Geographic Information (VGI) for the extraction of Geographical Named Entity (GNE).Notwithstanding, prior studies have predominantly concentrated on enhancing extraction accuracy, while often overlooking the critical aspect of GNE quality. This study addresses this gap by employing a multifaceted approach. Initially, a Geographical Named Entity Semantic Model (GNESM) was constructed by improving the BERT framework and conducting ablation experiments on multiple influencing factors to verify its feasibility. Based on GNESM, a Geographical Named Entity Recognition Model (GNERM) was constructed by incremental pre-training with social media text data and fine-tuning to achieve a recognition accuracy of 90.9%. Subsequently, a Geographical Named Entity Error Correction Model (GNEECM) was constructed by training GNESM with standard GNE data and incorporating error detection and correction modules, achieving a remarkable accuracy of 96.6% in error detection and correction tasks. The experimental results convincingly demonstrate that the proposed identification and correction methods outperform all compared methods. Through the identification and correction process, this study successfully obtained high-quality GNE data, providing a reference for expanding standard address libraries and subsequent research on geographic named entity.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
AbstractExisting deep learning-based change detection networks encounter challenges related to the temporal dependency inherent in dual-temporal images. In this study, a weight-shared dual-difference ...change detection network(DDCDNet) model is proposed based on feature extraction networks. The model employs feature discrimination modules fused with spatial and channel attention mechanisms at different hierarchical levels of the backbone network. In the encoding phase, the tiny version of the Swin Transformer is utilized as the backbone network, with a weight-sharing strategy applied to extract feature information from bi-temporal remote sensing images. The proposed model in this paper is experimented on the LEVIR-CD + and DSIFN datasets, achieving F1-scores (F1) of 87.71% and 85.79%,recalls of 83.87% and 81.17%, and IoU (Intersection over Union) scores of 78.11% and 75.12%, respectively. These results indicate that the proposed model significantly outperforms other comparative models, demonstrating a better capability of identifying temporal changes in buildings, excellent generalization capability.
To define the deposition and morphology changes of the droplets on fabric medium clearly in spraying printing the electrical circuits on fabric surface. The Navier-Stokes (N-S) equation and volume of ...fluid (VOF) boundary tracking model are adopted to set up a micro-scale three-dimensional (3D) numerical model for plain weave fabric. Simulation focused on the evolution process of phase field, pressure field and speed field during the droplets deposition. The results show that axial momentum controls the droplets outward spread quickly and synchronously with a ring shape at early stage. The control of unbalanced-Young driving force causes the three-phase contact line fixes on fiber cylinder, droplets are in an instantaneous stable state. Capillary pressure gradient controls the three-phase contact line pinning and slippage forward lead to differences in spreading direction. The rebound effect of droplets promotes further penetration of droplets into fabric. Experiment analysis displays the well match with the simulation results, indicating the rationality of the proposed micro-scale 3D plain weave fabric modeling method. The findings of this paper would contribute to the future works for flexible electronic devices on fabric surface research studies.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The pixel-wise post-classification comparison (PCC) method is widely used in remote sensing images change detection. However, it is affected by the significant cumulative error caused by single image ...classification error. What's more, the pixel-wise change detection method always produces "salt and pepper" effect. To solve the excessive evaluation of changed types and quantity caused by cumulative error and "salt and pepper" effect, a novel remote sensing image change detection method called entropy query-by fuzzy ARTMAP object-wise joint classification comparison (EQFAM-OBJCC) is presented in this article. Firstly, entropy query-by measurement of active learning is integrated with the fuzzy ARTMAP neural network to choose training samples which contain large amounts of information to improve the classification accuracy. Secondly, joint classification comparison is introduced to obtain the pixel-wise classification results. Finally, the object-wise classification and change detection results are produced by superpixel segmentation method, majority voting rule, and comparison of each superpixels. Experimental results demonstrate the validity of the proposed method. The classification and change detection results show that the proposed method can reduce the cumulative error with an average classification accuracy of 94.12% and a total detection error of 27.03%, and effectively resolve the "salt and pepper" problem. The proposed method was used to monitor the reclamation status of Liaohe estuary wetland via 10 time series remote sensing images from 1987 to 2014.
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