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Keywords = TOD blocks

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24 pages, 2440 KiB  
Article
A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images
by Huazhong Jin, Yizhuo Song, Ting Bai, Kaimin Sun and Yepei Chen
Remote Sens. 2025, 17(14), 2415; https://doi.org/10.3390/rs17142415 - 12 Jul 2025
Viewed by 269
Abstract
Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically adjust the [...] Read more.
Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically adjust the contextual scope based on the specific characteristics of each target. To address this issue and improve the detection performance of small objects (typically defined as objects with a bounding box area of less than 1024 pixels), we propose a novel backbone network called the Dynamic Context Branch Attention Network (DCBANet). We present the Dynamic Context Scale-Aware (DCSA) Block, which utilizes a multi-branch architecture to generate features with diverse receptive fields. Within each branch, a Context Adaptive Selection Module (CASM) dynamically weights information, allowing the model to focus on the most relevant context. To further enhance performance, we introduce an Efficient Branch Attention (EBA) module that adaptively reweights the parallel branches, prioritizing the most discriminative ones. Finally, to ensure computational efficiency, we design a Dual-Gated Feedforward Network (DGFFN), a lightweight yet powerful replacement for standard FFNs. Extensive experiments conducted on four public remote sensing datasets demonstrate that the DCBANet achieves impressive mAP@0.5 scores of 80.79% on DOTA, 89.17% on NWPU VHR-10, 80.27% on SIMD, and a remarkable 42.4% mAP@0.5:0.95 on the specialized small object benchmark AI-TOD. These results surpass RetinaNet, YOLOF, FCOS, Faster R-CNN, Dynamic R-CNN, SKNet, and Cascade R-CNN, highlighting its effectiveness in detecting small objects in remote sensing images. However, there remains potential for further improvement in multi-scale and weak target detection. Future work will integrate local and global context to enhance multi-scale object detection performance. Full article
(This article belongs to the Special Issue High-Resolution Remote Sensing Image Processing and Applications)
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20 pages, 4198 KiB  
Article
HiDRA-DCDNet: Dynamic Hierarchical Attention and Multi-Scale Context Fusion for Real-Time Remote Sensing Small-Target Detection
by Jiale Wang, Zhe Bai, Ximing Zhang, Yuehong Qiu, Fan Bu and Yuancheng Shao
Remote Sens. 2025, 17(13), 2195; https://doi.org/10.3390/rs17132195 - 25 Jun 2025
Viewed by 395
Abstract
Small-target detection in remote sensing presents three fundamental challenges: limited pixel representation of targets, multi-angle imaging-induced appearance variance, and complex background interference. This paper introduces a dual-component neural architecture comprising Hierarchical Dynamic Refinement Attention (HiDRA) and Densely Connected Dilated Block (DCDBlock) to address [...] Read more.
Small-target detection in remote sensing presents three fundamental challenges: limited pixel representation of targets, multi-angle imaging-induced appearance variance, and complex background interference. This paper introduces a dual-component neural architecture comprising Hierarchical Dynamic Refinement Attention (HiDRA) and Densely Connected Dilated Block (DCDBlock) to address these challenges systematically. The HiDRA mechanism implements a dual-phase feature enhancement process: channel competition through bottleneck compression for discriminative feature selection, followed by spatial-semantic reweighting for foreground–background decoupling. The DCDBlock architecture synergizes multi-scale dilated convolutions with cross-layer dense connections, establishing persistent feature propagation pathways that preserve critical spatial details across network depths. Extensive experiments on AI-TOD, VisDrone, MAR20, and DOTA-v1.0 datasets demonstrate our method’s consistent superiority, achieving average absolute gains of +1.16% (mAP50), +0.93% (mAP95), and +1.83% (F1-score) over prior state-of-the-art approaches across all benchmarks. With 8.1 GFLOPs computational complexity and 2.6 ms inference speed per image, our framework demonstrates practical efficacy for real-time remote sensing applications, achieving superior accuracy–efficiency trade-off compared to existing approaches. Full article
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13 pages, 1080 KiB  
Article
3-Deoxysappanchalcone Inhibited High Mobility Group Box Protein 1-Mediated Severe Inflammatory Responses
by Jinhee Lee, Gyuri Han and Jong-Sup Bae
Pharmaceuticals 2025, 18(5), 731; https://doi.org/10.3390/ph18050731 - 16 May 2025
Cited by 1 | Viewed by 444
Abstract
Background/Objectives: Phytochemicals are increasingly recognized for their therapeutic potential in treating various diseases, including vascular disorders. High mobility group box 1 (HMGB1), a key mediator of late-stage sepsis, triggers the release of proinflammatory cytokines, leading to inflammation and systemic complications. Elevated plasma levels [...] Read more.
Background/Objectives: Phytochemicals are increasingly recognized for their therapeutic potential in treating various diseases, including vascular disorders. High mobility group box 1 (HMGB1), a key mediator of late-stage sepsis, triggers the release of proinflammatory cytokines, leading to inflammation and systemic complications. Elevated plasma levels of HMGB1 impair diagnosis and prognosis while worsening outcomes in inflammatory conditions. 3-deoxysappanchalcone (3-DSC), a compound derived from Biancaea sappan (L.) Tod., has demonstrated anti-influenza and anti-allergic effects, though its role in HMGB1-mediated severe vascular inflammation remains unclear. This study hypothesized that 3-DSC could modulate lipopolysaccharide-induced HMGB1 activity and its downstream inflammatory pathways in human umbilical vein endothelial cells (HUVECs). Methods: In vitro and in vivo permeability; cell viability, adhesion, and excavation of leukocytes; the development of cell adhesion molecules; and lastly, the production of proinflammatory substances were investigated on human endothelial cells and mouse disease models to investigate the efficacy of 3-DSC in inflammatory conditions. Results: Experiments revealed that 3-DSC inhibited HMGB1 translocation from HUVECs, reduced neutrophil adhesion and extravasation, suppressed HMGB1 receptor formation, and blocked nuclear factor-κB (NF-κB) activation and tumor necrosis factor-α (TNF-α) synthesis. Conclusions: These findings suggest that 3-DSC effectively mitigates HMGB1-driven inflammation, offering promise as a therapeutic candidate for inflammatory diseases. Full article
(This article belongs to the Section Natural Products)
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20 pages, 11254 KiB  
Article
SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images
by Hao Qiang, Wei Hao, Meilin Xie, Qiang Tang, Heng Shi, Yixin Zhao and Xiaoteng Han
Remote Sens. 2025, 17(2), 249; https://doi.org/10.3390/rs17020249 - 12 Jan 2025
Cited by 6 | Viewed by 2646
Abstract
Currently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability. In addition, there is a need [...] Read more.
Currently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability. In addition, there is a need to balance performance and complexity when improving the model. To address these issues, this paper proposes an efficient and lightweight SCM-YOLO detector improved from YOLOv5 with spatial local information enhancement, multi-scale feature adaptive fusion, and global sensing capabilities. The SCM-YOLO detector consists of three innovative and lightweight modules: the Space Interleaving in Depth (SPID) module, the Cross Block and Channel Reweight Concat (CBCC) module, and the Mixed Local Channel Attention Global Integration (MAGI) module. These three modules effectively improve the performance of the detector from three aspects: feature extraction, feature fusion, and feature perception. The ability of SCM-YOLO to detect small objects in complex remote sensing environments has been significantly improved while maintaining its lightweight characteristics. The effectiveness and lightweight characteristics of SCM-YOLO are verified through comparison experiments with AI-TOD and SIMD public remote sensing small object detection datasets. In addition, we validate the effectiveness of the three modules, SPID, CBCC, and MAGI, through ablation experiments. The comparison experiments on the AI-TOD dataset show that the mAP50 and mAP50-95 metrics of SCM-YOLO reach 64.053% and 27.283%, respectively, which are significantly better than other models with the same parameter size. Full article
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23 pages, 34667 KiB  
Article
The Carbon Reduction Mechanism and Adaptive Planning Strategies of TOD Block Form Regulation Oriented to Microclimate Effects
by Peng Dai, Haotian Liu, Song Han, Chuanyan Liu, Guannan Fu and Yanjun Wang
Sustainability 2025, 17(1), 358; https://doi.org/10.3390/su17010358 - 6 Jan 2025
Cited by 2 | Viewed by 1100
Abstract
Adapting to climate change and controlling carbon emissions have emerged as significant challenges faced by the international community. The high-quality pedestrian space system of TOD blocks, as an important means for carbon reduction and carbon sink increase in cities, showcases the effect of [...] Read more.
Adapting to climate change and controlling carbon emissions have emerged as significant challenges faced by the international community. The high-quality pedestrian space system of TOD blocks, as an important means for carbon reduction and carbon sink increase in cities, showcases the effect of green intensification and low-carbon sustainable urban space development. In this study, by combining the research on low-carbon block creation and urban microclimate, focusing on the technical process of the three stages of pre-treatment, core calculation, and post-treatment, comprehensively considering the three elements of microclimate, namely wind, heat, and carbon, and their influencing parameters, and introducing a CFD simulation method for porous media, a CFD simulation technology framework for microclimate improvement in urban design is constructed. Through the spatial visualization of the software solution calculation results and the correlation and comparative analysis of the measured data, we quantitatively analyze the coupling relationship between the block morphology and the comprehensive environment of wind, heat, and carbon. The research results indicate that by rationally adjusting indicator elements such as the height-to-width ratio of streets and entrance forms, it is possible to effectively facilitate cooling, ventilation, and air circulation within blocks and dilute the CO2 concentration. Finally, from the urban design element systems at the micro, meso, and macro levels, the adaptive planning strategies in the three dimensions of the spatial form, constituent elements, and planning guidelines of TOD blocks are summarized and refined, with the aim of achieving the low-carbon transformation of cities through the creation of a healthy microclimate environment. Full article
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14 pages, 4056 KiB  
Article
Research on PoW Protocol Security under Optimized Long Delay Attack
by Tao Feng and Yufeng Liu
Cryptography 2023, 7(2), 32; https://doi.org/10.3390/cryptography7020032 - 16 Jun 2023
Cited by 6 | Viewed by 2200
Abstract
In the blockchain network, the communication delay between different nodes is a great threat to the distributed ledger consistency of each miner. Blockchain is the core technology of Bitcoin. At present, some research has proven the security of the PoW protocol when the [...] Read more.
In the blockchain network, the communication delay between different nodes is a great threat to the distributed ledger consistency of each miner. Blockchain is the core technology of Bitcoin. At present, some research has proven the security of the PoW protocol when the number of delay rounds is small, but in complex asynchronous networks, the research is insufficient on the security of the PoW protocol when the number of delay rounds is large. This paper improves the proposed blockchain main chain record model under the PoW protocol and then proposes the TOD model, which makes the main chain record in the model more close to the actual situation and reduces the errors caused by the establishment of the model in the analysis process. By comparing the differences between the TOD model and the original model, it is verified that the improved model has a higher success rate of attack when the probability of mining the delayable block increases. Then, the long delay attack is improved on the balance attack in this paper, which makes the adversary control part of the computing power and improves the success rate of the adversary attack within a certain limit. Full article
(This article belongs to the Special Issue Emerging Topics in Blockchain Security and Privacy)
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23 pages, 11855 KiB  
Article
SAFF-SSD: Self-Attention Combined Feature Fusion-Based SSD for Small Object Detection in Remote Sensing
by Bihan Huo, Chenglong Li, Jianwei Zhang, Yingjian Xue and Zhoujin Lin
Remote Sens. 2023, 15(12), 3027; https://doi.org/10.3390/rs15123027 - 9 Jun 2023
Cited by 38 | Viewed by 4147
Abstract
SSD is a classical single-stage object detection algorithm, which predicts by generating different scales of feature maps on different convolutional layers. However, due to the problems of its insufficient non-linearity and the lack of semantic information in the shallow feature maps, as well [...] Read more.
SSD is a classical single-stage object detection algorithm, which predicts by generating different scales of feature maps on different convolutional layers. However, due to the problems of its insufficient non-linearity and the lack of semantic information in the shallow feature maps, as well as the fact that small objects contain few pixels, the detection accuracy of small objects is significantly worse than that of large- and medium-scale objects. Considering the above problems, we propose a novel object detector, self-attention combined feature fusion-based SSD for small object detection (SAFF-SSD), to boost the precision of small object detection. In this work, a novel self-attention module called the Local Lighted Transformer block (2L-Transformer) is proposed and is coupled with EfficientNetV2-S as our backbone for improved feature extraction. CSP-PAN topology is adopted as the detection neck to equip feature maps with both low-level object detail features and high-level semantic features, improving the accuracy of object detection and having a clear, noticeable and definitive effect on the detection of small targets. Simultaneously, we substitute the normalized Wasserstein distance (NWD) for the commonly used Intersection over Union (IoU), which alleviates the problem wherein the extensions of IoU-based metrics are very sensitive to the positional deviation of the small objects. The experiments illustrate the promising performance of our detector on many datasets, such as Pascal VOC 2007, TGRS-HRRSD and AI-TOD. Full article
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21 pages, 7779 KiB  
Article
Do Rail Transit Stations Affect the Population Density Changes around Them? The Case of Dallas-Fort Worth Metropolitan Area
by Saad AlQuhtani and Ardeshir Anjomani
Sustainability 2021, 13(6), 3355; https://doi.org/10.3390/su13063355 - 18 Mar 2021
Cited by 20 | Viewed by 5925
Abstract
This study investigates changes in population density in 454 block groups within a one-mile buffer around rail transit stations (the study area) in the Dallas-Fort Worth (DFW) metropolitan area. The research uses three analysis approaches to explore a correlation between proximity to rail [...] Read more.
This study investigates changes in population density in 454 block groups within a one-mile buffer around rail transit stations (the study area) in the Dallas-Fort Worth (DFW) metropolitan area. The research uses three analysis approaches to explore a correlation between proximity to rail stations and population density changes. Changes in population density between 2000 and 2014 are calculated. Changes in population density in the study area are compared to the remainder of the block groups within the four counties served by the same rail transit systems. An innovative approach is employed to select the best regression model using the data of the study area. A relationship between the independent variables and the changes in population density is formulated. The proximity of block groups in the study area to the nearby highway ramps or city centers is also investigated during the study period. Results show that it has a positive impact on population density. Changes in population density within the block groups located beyond the one-mile buffer, especially toward outlying areas, are greater than those within the one-mile buffer. Unexpectedly, it is found that an increase in the percentage of employed and white residents leads to an increase in population density. Other interesting results show that the number of jobs is in inverse proportion to the population density. However, block groups that are developed as part of transit-oriented development (TOD) are dramatically higher in population density than the other block groups. These results represent a beneficial contribution to the field of urban planning. Urban planners and policymakers can also use the findings to adopt specific policies for increasing density, advancing rail transit systems’ success, increasing transit usage, and sustaining station area development. Full article
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16 pages, 4577 KiB  
Article
Low-Carbon Transportation Oriented Urban Spatial Structure: Theory, Model and Case Study
by Yuyao Ye, Changjian Wang, Yuling Zhang, Kangmin Wu, Qitao Wu and Yongxian Su
Sustainability 2018, 10(1), 19; https://doi.org/10.3390/su10010019 - 22 Dec 2017
Cited by 33 | Viewed by 8524
Abstract
Optimising the spatial structure of cities to promote low-carbon travel is a primary goal of urban planning and construction innovation in the low-carbon era. There is a need for basic research on the structural characteristics that help to reduce motor traffic, thereby promoting [...] Read more.
Optimising the spatial structure of cities to promote low-carbon travel is a primary goal of urban planning and construction innovation in the low-carbon era. There is a need for basic research on the structural characteristics that help to reduce motor traffic, thereby promoting energy conservation. We first review the existing literature on the influence of urban spatial structure on transport carbon dioxide emissions and summarise the influence mechanisms. We then present two low-carbon transportation oriented patterns of urban spatial structure including the traditional walking city and the modern transit metropolis, illustrated by case studies. Furthermore, we propose an improved model Green Transportation System Oriented Development (GTOD), which is an extension of traditional transit-oriented development (TOD) and includes the additional features of a walking city and an emphasis on the integration of land use with a green transportation system, consisting of the public transportation and non-auto travel system. A compact urban form, effective mix of land use and appropriate scale of block are the basic structural features of a low-carbon transportation city. However, these features are only effective at promoting low-carbon transportation when integrated with the green traffic systems. Proper integration of the urban structural system with the green space system is also required. The optimal land use/transportation integration strategy is to divide traffic corridors with wedge-shaped green spaces and limit development along the transit corridors. This strategy forms the basis of the proposed urban structural model to promote low-carbon transportation and sustainable urban growth management. Full article
(This article belongs to the Special Issue Methodological Advances in Research on Sustainable Ecosystems)
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