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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU) corresponding to changed areas in RSI. The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multi-level features, leading to challenges in extracting LCLU features in complex changing scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multi-Level Feature Interaction module (TML-FI), and a Multi-Level Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multi-level features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multi-scale features, thus improving the model's capability to interpret complex changing scenes. Finally, ML-FFD integrates the multi-level features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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YTHDF1 shapes immune-mediated hepatitis via regulating inflammatory cell recruitment and response

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Genes & Diseases 2025年第3期12卷 550-566页

作者： Hao Li Kailun Yu Xiandan Zhang Jiawen Li Huilong Hu Xusheng Deng Siyu Zeng Xiaoning Dong Junru Zhao Yongyou Zhang State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkSchool of Life SciencesXiamen UniversityXiamenFujian 361102China National Institute for Data Science in Health and Medicine Engineering Faculty of Medicine and Life SciencesXiamen UniversityXiamenFujian 361102China School of Pharmaceutical Sciences Xiamen UniversityXiamenFujian 361102China

Severe immune responses regulate the various clinical hepatic injuries, includingautoimmune hepatitis and acute viral hepatitis. N6-methyladenosine (m6A) modification is acrucial regulator of immunity and inflammation. However, the precise role of YTHDF1 in Tcell-mediated hepatitis remains incompletely characterized. To address this, we utilizedConcanavalin A (ConA)-induced mouse liver damage as an experimental model for T cell-mediated hepatitis. Our findings found that hepatic YTHDF1 protein rapidly decreased during ConAinduced hepatitis, and YTHDF1-deficient (Ythdf1^(-/-)) mice showed more susceptibility toConA-induced liver injury, along with an intensified inflammatory storm accompanied by aggravated hepatic inflammatory response via ERK and NF-κB pathways. Interestingly, hepatic-specific over-expression or deletion of YTHDF1 exhibited redundancy in ConA-induced liver *** in bone marrow chimeric mice confirmed the necessity of YTHDF1 in hematopoieticcells for controlling the response to ConA-induced hepatitis. Additionally, our data revealedthat YTHDF1 deletion in macrophages exacerbated the inflammatory response induced by lipopolysaccharide. In summary, our study uncovered that YTHDF1 deficiency exacerbates the immune response in ConA-induced hepatitis by modulating the expression of inflammatorymediators, highlighting the potential of YTHDF1 as a therapeutic target for clinical hepatitis.

关键词： Concanavalin A Hepatitis Inflammatory response N6-methyladenine YTHDF1

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Area Coverage Reliability Evaluation for Collaborative Intelligence and Meta-Computing of Decentralized Industrial Internet of Things

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IEEE Internet of Things Journal 2025年第10期12卷 13734-13745页

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao Yin, Duan Gao, Hanjun Yang, Laurence T. Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Mobile Communications Group Terminal Co. Beijing Department Beijing China CNNC Wuhan Nuclear Power Operation Technology Co. Cyber Security Technology Department Wuhan China

Industrial Internet of Things (IIoT) is evolving toward decentralization and autonomous operation. Nodes in decentralized IIoT collaboratively sense data and communicate to participate in meta-computing and provide diverse intelligent services. Area coverage reliability evaluates the information collaborative intelligence sensing and communication capabilities of nodes in decentralized IIoT for a target area. It serves as a crucial performance evaluation metric for the meta-computing and intelligent services of decentralized IIoT. Existing area coverage reliability evaluation algorithms do not consider the dynamic node states in decentralized IIoT, and overlook the capability of nodes to collaborate in meta-computing. To address these issues, this article proposes a novel confident information coverage (CIC)-based area coverage reliability metric (CACREL), which comprehensively considers node multistate, node collaboration, network coverage rate, and network connectivity. To effectively evaluate CACREL, a CIC-based coverage reliability evaluation algorithm (CACR) is proposed. Specifically, CACR transforms complex networks into grid networks and utilizes a coverage table (CT) to describe different coverage states of each grid, which reduces the complexity of the area coverage reliability evaluation. Additionally, CACR employs a reliable path algorithm to converge the CT of each grid to the sink node based on grid connectivity. Simulation results demonstrate that the proposed CACR can accurately evaluate the area coverage reliability of decentralized IIoT and enhance its service performance. © 2014 IEEE.

关键词： Decentralized finance

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Chirp-free hundred-picosecond pulse fiber laser based on carbon nanotubes mode-locker

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Optics Express 2025年第7期33卷 14850-14857页

作者： Li, Weixi Jiang, Kailin Dai, Lilong Huang, Qianqian Zou, Meng Luo, Siyu Chen, Siyu Song, Yingxiong Liu, Yunqi Yan, Zhijun Mou, Chengbo Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access System Huazhong University of Science and Technology Wuhan430074 China

We demonstrate a narrow bandwidth passively erbium-doped mode-locked fiber laser based on single-wall carbon nanotubes (SWCNTs). The center wavelength is 1546.61 nm. The strong filtering effect and low saturable absorber (SA) modulation depth provide advantages for a long pulse duration of 323 ps. An overall spectral bandwidth of 1.3 GHz (0.01 nm) is measured by the optical heterodyne method, representing the narrowest spectral width based on SWCNT under a single filter structure. The obtained spectral width is very close to the transform-limited bandwidth of 8 pm, indicating that the pulse is chirp-free. Meanwhile, the generation of beat signals reveals the strong coherence between pulses. The repetition frequency of the laser is 7.8 MHz, and the number of longitudinal mode locks is only 166, which is the minimum number of longitudinal mode-locking in passively mode-locked laser based on material SA. At the same time, numerical simulations further support our experimental results and reveal the pulse evolution, providing evidence that SA and filter are the key factors in the generation of chirp-free long pulse. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Saturable absorbers

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Contactless Vital Signs Monitoring for Animals

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IEEE Internet of Things Journal 2025年第11期12卷 16789-16796页

作者： Pitafi, Zaid Farooq Song, Yingjian Xie, Zaipeng Brainard, Benjamin Song, Wenzhan University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China University of Georgia College of Veterinary Medicine AthensGA30602 United States

Monitoring vital signs such as Heart Rate (HR) and Respiratory Rate (RR) is critical for veterinary medicine. The existing contact based systems are difficult to use on animals for longer periods as they may cause movement restriction. Contactless solutions have recently gained more popularity due to their ease-of-use. However, there are very few validated systems for animals. In this study, we propose a contactless vital signs monitoring system, CageDot for animals. Our system provides continuous real-time monitoring of HR and RR during hospitalization. The CageDot is placed under the animal cage and detects the heart vibrations using a geophone sensor. The CageDot also has a signal quality control algorithm to address the problem of obtaining high-quality cardiac data in real-life noisy environments such as hospitals. Compared to the existing works that use controlled environments, this is especially significant. The algorithm includes several steps that include background noise/movement removal, subject movement detection, heartbeat extraction and vital signs estimation. The experimental results on 16 hospitalized dogs and cats show that this system can achieve high accuracy for vital signs monitoring with a Mean Absolute Error (MAE) of 4.71 for Heart Rate (3.8 percent error rate) and 1.28 (8 percent error rate) for Respiratory Rate. © 2014 IEEE.

关键词： Heart

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FasterSal: Robust and Real-Time Single-Stream Architecture for RGB-D Salient Object Detection

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IEEE Transactions on Multimedia 2025年 27卷 2477-2488页

作者： Zhang, Jin Zhang, Ruiheng Xu, Lixin Lu, Xiankai Yu, Yushu Xu, Min Zhao, He Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control School of Mechatronical Engineering Beijing100081 China Shandong University School of Software Jinan250101 China University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Liverpool Department of Eye and Vision Sciences LiverpoolL69 7ZX United Kingdom

RGB-D Salient Object Detection (SOD) aims to segment the most prominent areas and objects in a given pair of RGB and depth images. Most current models adopt a dual-stream structure to extract information from both RGB and depth images. However, this leads to an exponential increase in the number of parameters and computations in the model. Moreover, the discrepancy between RGB pretrained and the 3D geometric relationships in depth maps present a challenge for the encoder in capturing spatial structural details. These issues impact the model's accuracy in locating salient objects and distinguishing edge details. To address these, we propose a novel early feature fusion network, named FasterSal, which enables more efficient RGB-D SOD. FasterSal uses a single stream structure to receive RGB images and depth maps, extracting features based on the 3D geometric relationships in the depth map while fully leveraging the pretrained RGB encoder. This approach effectively avoids the inconsistencies between depth modality and the RGB pretrained encoder. It also significantly reduces the number of network parameters while maintaining efficient feature encoding capabilities. To achieve finer edge learning, the detail-aware loss and texture enhancement module are introduced. These modules are designed to extract latent details in high-frequency component features and to enhance the edge learning capability of the model using distance information. Experimental results on several benchmark datasets confirm the effectiveness and superiority of our method over the state-of-the-art approaches, achieving a good balance between performance and speed with only 3.4 million parameters and a CPU operating speed of 63 FPS. © 1999-2012 IEEE.

关键词： Image segmentation

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Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering 6th

Enhancing Weakly Supervised Medical Segmentation via Hetero...

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6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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AAMB: a cross-domain identity authentication scheme based on multilayered blockchains in IoMT

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Journal of Supercomputing 2025年第7期81卷 1-39页

作者： Zhang, Zheqing Li, Hongzhi Li, Dun Li, Kuan-Ching College of Big Data and Artificial Intelligence Chizhou University Chizhou247000 China Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and Technology Chizhou247000 China Department of Industrial Engineering Tsinghua University Beijing China Department of Computer Science and Information Engineering Providence University Taichung City Taiwan

The internet of medical things (IoMT) integrates various sensors, smart devices, and fully utilizes medical data to provide high-quality medical services through the Internet of Things (IoT). Smart medical devices can be cross-domain shared in IoMT to provide health monitoring, disease diagnosis, rehabilitation training, etc. In IoMT, conventional authentication schemes struggle to meet cross-domain medical service sharing requirements due to resource constraints (computational/communication), confidentiality challenges, and strict latency demands. To address these issues, we propose a cross-domain authentication scheme based on dynamic accumulators and two-layer blockchains. First, a blockchain-based collaborative authentication framework is designed to realize secure and reliable intradomain and cross-domain authentication. Second, certificateless cryptography and dynamic accumulators are combined to guarantee both the confidentiality and efficiency of cross-domain authentication. Furthermore, we analyze the potential security threats that threaten the system’s stability and formally demonstrate the security of the proposed scheme using Burrows–Abadi–Needham logic. The formal and informal security analyses indicate that the proposed scheme is secure and can resist against known security threats. Finally, we evaluate the performance of the proposed authentication protocols in terms of computational and communication overhead. The corresponding performance analysis shows that compared with related protocols, it can reduce the computational and communication costs by more than 42.3% and 21.4%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diagnosis

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Contribution of Transmembrane Protein 68 to Triglyceride Synthesis and Lipid Droplet Formation Differs From Diacylglycerol Acyltransferase

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生物化学与生物物理进展 2025年第3期52卷 705-715页

作者： YU Qing FU Yang-Yang ZENG Fan-Si PANG Hui-Min HUANG Fei-Fei CHANG Ping-An Chongqing Key Laboratory of Big Data for Bio-Intelligence School of Life Health Information Science and EngineeringChongqing University of Posts and TelecommunicationsChongqing 400065China

Objective To characterize transmembrane protein 68(TMEM68)in an alternative triacylglycerol(TAG)biosynthesis pathway,and determine the interplay between TMEM68 and the canonical TAG synthesis enzyme acyl-CoA:diacylglycerol acyltransferase(DGAT).Methods Effects of exogenous fatty acid and monoacylglycerol on TAG synthesis and lipid droplet(LD)formation in TMEM68 overexpression and knockout cells treated with DGAT inhibitor or not were investigated by comparing LD morphology,Oil Red O staining,and measurement of TAG *** were stained with fluorescence dye and observed by confocal fluorescence *** levels were determined with an enzyme-based triglyceride assay *** of TMEM68 and DGAT1 was detected by co-expression and confocal fluorescence microscopy and their interaction was determined by ***-qPCR and immunoblotting assay were used to detect the expression of *** The synthesis of TAG catalyzed by TMEM68 was independent of DGAT *** exogenous fatty acids and monoacylglycerol promoted TAG synthesis mainly through DGAT in human neuroblastoma *** LDs formed by TMEM68 were different in morphology from those by *** addition,TMEM68 and DGAT1 colocalized in the same endoplasmic reticulum(ER)compartment but did not interact ***68 overexpression reduced the expression of DGAT1,the major DGAT enzyme involved in TAG synthesis,while TMEM68 knockout had little *** The TMEM68-mediated TAG synthesis pathway has distinct features from the canonical DGAT pathway,however,TMEM68 and DGAT may coregulate intracellular TAG levels.

关键词： triacylglycerol lipid droplet TMEM68 GAT

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T2R-GAN: A CGAN-based model for rural thematic road extraction 27th

T2R-GAN: A CGAN-based model for rural thematic road extracti...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ni, Zixiang Zhai, Weixin College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural Affairs Beijing100083 China

ISBN: (纸本)9783031783883

Rural roads extracted from agricultural machinery trajectories have significant research value. Due to the intricate network of rural roads and the large difference in the density of agricultural machinery trajectories, traditional road extraction methods struggle to perform effectively on rural roads with complex topology and on agricultural machinery trajectories with obscure road features. Therefore, this paper proposes a CGAN-based model named T2R-GAN (Trajectory to Road-GAN) for rural thematic road extraction, which learns the trajectory-to-road feature mapping through continuous adversarial training between the ELAU-Net generator and the PatchGAN discriminator to adapt to trajectories of various densities. ELAU-Net is an efficient network that utilizes an encoder-decoder structure and ELA modules to enhance the capture of obscure road features between sparse trajectories. To enhance model performance and reduce the risk of overfitting, bilateral hinge loss is designed to train our model to enhances the discriminator’s discriminative ability to facilitate the generator to generate more realistic roads improve the generalization of the model. To verify the effectiveness of T2R-GAN in extracting roads from agricultural trajectory data, this paper selects the real agricultural trajectory data from Henan Province, china in June 2021 as the dataset for verification. The experimental results show that the proposed method achieves 79.23% F1score, which is 5.53% higher than the previous state-of-art. The proposed T2R-GAN provides a novel and effective approach for extracting rural thematic roads from agricultural machinery trajectories for the first time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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