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arXiv 2025年

作者： Wu, Ruixin Li, Zihan Wang, Jin Xu, Xiangyu Yu, Huan Zheng, Zhi Huang, Kaixiang Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China State Key Laboratory of Robotics and Systems Department of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird’s eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar. Copyright © 2025, The Authors. All rights reserved.

关键词： Radar imaging

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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One-step electrospun continuous BN/PLA fibrous yarns based flexible and self-cleaning fabrics for personal passive radiative cooling

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Chemical engineering Journal 2025年 515卷

作者： Xing, Lei Chen, Yifan Liu, Xiangyu Shao, Wenxiang Li, Yongrui You, Chaoyu Huang, Xingyi Sun, Bin College of Electronics and Information Qingdao University Qingdao266071 China Department of Polymer Science and Engineering Shanghai Key Laboratory of Electrical Insulation and Thermal Aging Shanghai Jiao Tong University Shanghai200240 China College of Physics Qingdao University Qingdao266071 China College of Fashion and Textiles Key Lab of Material & Technology of Textile Tarim University Alaer XP843300 China The Affiliated Hospital of Qingdao University Cancer Institute Research Center for Intelligent and Wearable Technology College of Textiles and Clothing Qingdao University Shandong Qingdao266071 China Weihai Innovation Research Institute of Qingdao University Weihai264200 China

Passive radiative cooling (PRC) technology has attracted attention for effective personal thermal management as it can achieve cooling effect with zero energy input. Electrospun porous fibrous membrane demonstrate excellent PRC performance. However, the nonwoven 2D structure limit its further application for wearable personal cooling because additional process is required to form yarns and the resultant fabrics. In this study, we provide an environmental-friendly wearable fabric based on hexagonal boron nitride (h-BN)/polylactic acid (PLA) composite fibrous yarns. After fabricating flexible fibrous yarns within only one step of electrospinning, the BN/PLA fabric exhibits a high solar reflectivity of 94.6 % within the solar wavelength range of 0.3–2.5 μm, an emissivity of 95.9 % within the atmospheric transparency window of 8–13 μm wavelength, and an outstanding cooling power of 93.6 W/m2. Comparing to the commercial cotton fabrics, that was measured under the same sunny outdoor conditions, a temperature difference of 6.1 ℃ was observed. Notably, excellent PRC performance was also demonstrated even on cloudy day and at night. For personal cooling, a 6 ℃ temperature drop was achieved, which is 3 ℃ lower than that of commercial cotton fabric. Besides, the fabric demonstrate a superior self-cleaning ability, contributing to both wearable comfort and long-time operation. It is believed that our composite fabric can be an ideal candidate for the future personal thermal management. © 2025 Elsevier B.V.

关键词： Commercial refrigeration

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Environmental and health impacts of reduced PM2.5 and trace metals from ship emissions under low-sulfur fuel oil policy in shanghai, China

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Environmental Pollution 2025年 377卷 126409页

作者： Dong, Xuyang Zhang, Yan Yu, Guangyuan Xiong, Yuqi Han, Zimin Huo, Juntao Huang, Cheng Kan, Haidong Zheng, Mei Ning, Zhi Xie, Bing National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary Department of Environmental Science and Engineering Fudan University Shanghai200438 China Shanghai200062 China MOE Laboratory for National Development and Intelligent Governance Shanghai Institute for Energy and Carbon Neutrality Strategy IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health Fudan University Shanghai200433 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China Shanghai200235 China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment Shanghai Institute of Infectious Disease and Biosecurity Fudan University Shanghai200032 China Children's Hospital of Fudan University National Centre for Children's Health Shanghai China SKL-ESPC and SEPKL-AERM College of Environmental Sciences and Engineering and Centre for Environment and Health Peking University Beijing100871 China Division of Environment and Sustainability The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste School of Ecological and Environmental Sciences East China Normal University Shanghai200241 China

Ship emissions can significantly exacerbate air pollution in coastal cities, threatening public health;however, a low-sulfur fuel oil policy, which restricts sulfur content in marine fuels, can effectively mitigate such pollution-driven challenges. This study employed a high-resolution ship emission inventory to assess the impacts of the fuel oil switch on air quality and public health in shanghai from 2017 to 2021. Results showed a 37.3 % reduction in primary PM2.5 emissions from ships, with even steeper declines of 46.7 and 91.6 % in Ni and V emissions, respectively, leading to notable air quality improvements. Health assessments revealed a reduction in premature mortality attributable to long-term exposure to the contribution of ships to atmospheric PM2.5 concentrations, with deaths decreasing from 630 cases in 2017 to 481 in 2021. Similarly, short-term exposure-related deaths fell from 43 to 29. The port and waterfront areas experienced the most pronounced health benefits. The non-carcinogenic risks posed by trace metals (Ni and V), which were detected along the Huangpu River in 2017, dropped to 0.1 by 2021. Nonetheless, the carcinogenic risk from V persisted as a concern for adults in 2021. An analysis of ship-influenced episodes showed that population-weighted concentrations and short-term premature mortality decreased by over 50 % in densely populated areas and key ports. Despite the low population density in port areas such as Wusongkou and Waigaoqiao, the human health risks linked to ship emissions remained significant. This study demonstrates the effectiveness of low-sulfur fuel oil policy in reducing emissions and health risks, providing a scientific basis for refined pollution control strategies in port cities. © 2025 Elsevier Ltd

关键词： Air quality

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Magnesium promoting OVX rats’ rotator cuff tear repair with relieving stem cell senescence effect

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Experimental Cell research 2025年第2期449卷 114593页

作者： Sun, Minghui Zhang, Weijiao Sun, Xin Yu, Kaixu He, Xiangming Zheng, Liming Ling, Ziao Duan, Kaikai Qi, Xiling Liu, Yan Zhao, Xin Wang, Hui Xu, Jiankun Zhang, Yifeng School of Life Science and Technology ShanghaiTech University Shanghai 201210 China Shanghai Clinical Research and Trial Center Shanghai 200000 China Musculoskeletal Research Laboratory Department of Orthopedics & Traumatology The Chinese University of Hong Kong 999077 Hong Kong Engineering Research Center of Intelligent Rehabilitation for Traditional Chinese Medicine Ministry of Education School of Rehabilitation Science Shanghai University of Traditional Chinese Medicine Shanghai 201203 China Shanghai Key Laboratory of Orthopaedic Implants Department of Orthopaedic Surgery Shanghai Ninth People's Hospital Shanghai JiaoTong University School of Medicine No. 639 Zhizaoju Road Shanghai 200011 China Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou 310000 China

The rotator cuff tear (RCT) repairing of tendon-to-bone junction (enthese) after surgery in osteoporotic patients is mainly hindered by poor bone formation at the damaged humeral tuberosity region. We found that long-term oral supplementation of Magnesium ions (Mg²⁺) can significantly promote humeral tuberosity bone formation and enthese regeneration after RCT surgery in ovariectomized (OVX) rats, attributing to the promotive effect of Mg²⁺ on the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) and its inhibition of new bone mineralization. Meanwhile, the in vitro replicative senescent markers of BMSCs are significantly relieved by Mg²⁺. The BMSCs' RNA-seq data analysis and cell energy metabolism results indicate that Mg²⁺ significantly increase senescent BMSCs’ mitochondrial transmembrane potential and intracellular ATP content, and reduce reactive oxygen species production. © 2025 Elsevier Inc.

关键词： Magnesium Mitochondria function Osteoporosis Rotator cuff tear Stem cell senescence

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Understanding spatial differences in CO2 emissions between passenger and freight transportation

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Transportation research Part D: Transport and Environment 2025年 145卷

作者： Zhang, Yuliang Chen, Yifan Wang, Yichen Yu, Chengcheng Chen, Xiqun [Michael] Lee, Der-Horng Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Intelligent Transportation System Research Center Hangzhou City University Hangzhou310015 China School of Automation Nanjing University of Science and Technology Nanjing210034 China Division of Logistics and Transportation Tsinghua University Beijing100084 China Urban Mobility Institute Tongji University 1239 Siping Road Shanghai200082 China Zhejiang University-University of Illinois Urbana-Champaign Institute Haining314400 China

Road transport represents a significant source of CO2 emissions, presenting challenges to climate change mitigation. This research conducts a comparative examination of CO2 emissions associated with passenger and freight transportation across U.S. counties, highlighting differences in emission mechanisms from a nationwide perspective by combining Gradient Boosting Decision Trees and interpretable machine learning methods. Distinct spatial patterns are identified: passenger emissions concentrate in densely populated metropolitan areas, while freight emissions align with freight corridors. Our findings reveal shared and unique influences of socioeconomic factors, road networks, and industrial composition on passenger and freight emissions. Population density affects passenger emissions more strongly, while industrial activities and the average node degree of the road network have a greater impact on freight emissions. These insights are crucial for developing region-targeted CO2 reduction strategies and informing policy changes to mitigate the environmental effects by addressing the specific needs of each transportation sector. © 2025 Elsevier Ltd

关键词： Freight transportation

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DeepSeek or ChatGPT: Can brain-computer interfaces/brain-inspired computing achieve leapfrog development with large AI models?

Brain-X

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Brain-X 2025年第1期3卷

作者： Bai, Long Chen, Shugeng Wang, Peng Chen, He Yan, Jiaqing Zhu, Xiaojian Song, Enming Tian, Bobo Su, Jiacan Li, Xiaoli Organoid Research Center Institute of Translational Medicine Shanghai University Shanghai China Department of Orthopedics Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China National Center for Translational Medicine (Shanghai) SHU Branch Shanghai University Shanghai China Wenzhou Institute of Shanghai University Zhejiang Wenzhou China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Department of Language Literature and Communication Faculty of Humanities Vrije Universiteit Amsterdam Amsterdam Netherlands School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou China Pazhou Laboratory Guangdong Guangzhou China School of Electrical and Control Engineering North China University of Technology Beijing China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo China Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Key Laboratory of Polar Materials and Devices Department of Electronics Ministry of Education East China Normal University Shanghai China

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Anomize: Better Open Vocabulary Video Anomaly Detection

arXiv

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arXiv 2025年

作者： Li, Fei Liu, Wenxuan Chen, Jingjing Zhang, Ruixu Wang, Yuran Zhong, Xian Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China

Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. To address these challenges, we explore supplementary information from multiple sources to mitigate detection ambiguity by leveraging multiple levels of visual data alongside matching textual information. Furthermore, we propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. The resulting Anomize framework effectively tackles these issues, achieving superior performance on UCF-CRIME and XD-VIOLENCE datasets, demonstrating its effectiveness in OVVAD. Copyright © 2025, The Authors. All rights reserved.

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Tackling Challenges of Low-texture and Illumination Variations for Endoscopy Self-supervised Monocular Depth Estimation

Tackling Challenges of Low-texture and Illumination Variatio...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Luyan Luo, Jingjing Wang, Hongbo Zhao, Shizun Han, Yuan Li, Wenxian Fudan University Academy for Engineering and Technology Shanghai200433 China Intelligent Robot Engineering Research Center of Ministry of Education Shanghai2000433 China Shanghai Intelligent Robot Engineering Technology Research Center Shanghai200433 China Eye & Ent Hospital of Fudan University Shanghai200031 China

ISBN: (纸本)9798350337488

Extracting 3D environmental insights from endoscopy images holds immense value for minimally invasive surgical procedures. The Self-supervised Monocular Depth Estimation (SMDE) framework is promising for achieving this objective. However, existing methods struggle with low-texture and drastic illumination fluctuations in endoscopic images. To tackle this, we incorporate Photometric Aliagnment method based on pixel-wise Color Offset, and propose a carefully designed Color Offset penalty based on Reconstruction Confidence. We furthur apply an Auto-Mask mechanism and a cutting-edge backbone to enhance the performance. Our experiments employ a dataset of airway intubation images captured with a low-resolution and near-field electronic bronchoscope. The experimental results unequivocally highlight the exceptional performance of our approach, achieving root mean square error of 1.64 ± 0.22 mm after point cloud registration, which decreases by 16% than the current SoTA. Furthermore, we introduce an innovative evaluation metric rooted in Photometric Alignment Symmetry, and conduct ablation experiments on this metric. The ablation experiments furthur validate the effectiveness of proposed modules. This study underscores the efficacy of the Reconstruction Confidence-based Color Offset penalty and symmetric alignment evaluations in extracting 3D information from low-resolution and near-field endoscopy images. © 2023 IEEE.

关键词： low-texture photometric alignment reconstruction confidence self-supervised monocular endoscopy depth estimation

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