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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： Brain computer interface

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Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

arXiv

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arXiv 2025年

作者： Liu, Dingkun Li, Siyang Wang, Ziwei Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains. To our knowledge, this is the first work to use knowledge distillation in cross-headset transfers. Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Transfer learning

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Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation

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Results in Physics 2025年 73卷

作者： Hou, Tiantian Jiao, Shangbin Wang, Yi Song, Nianlong Li, Jianghua Cui, Wenchuan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China

The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system's dynamic response. The system's non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm's ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system's performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance. This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and signi

关键词： Bearing fault detection Coupled stochastic resonance Independent parameter regulation MFPT SPD

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Multi-View Contrastive Network (MVCNet) for Motor imagery Classification

arXiv

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arXiv 2025年

作者： Wang, Ziwei Li, Siyang Chen, Xiaoqing Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

Objective: An electroencephalography (EEG)-based brain-computer interface (BCI) serves as a direct communication pathway between the human brain and an external device. While supervised learning has been extensively explored for motor imagery (MI) EEG classification, small data quantity has been a key factor limiting the performance of deep feature learning. Methods: This paper proposes a knowledge-driven time-space-frequency based multi-view contrastive network (MVCNet) for MI EEG decoding in BCIs. MVCNet integrates knowledge from the time, space, and frequency domains into the training process through data augmentations from multiple views, fostering more discriminative feature learning of the characteristics of EEG data. We introduce a cross-view contrasting module to learn from different augmented views and a cross-model contrasting module to enhance the consistency of features extracted between knowledge-guided and data-driven models. Results: The combination of EEG data augmentation strategies was systematically investigated for more informative supervised contrastive learning. Experiments on four public MI datasets and three different architectures demonstrated that MVCNet outperformed 10 existing approaches. Significance: Our approach can significantly boost EEG classification performance beyond designated networks, showcasing the potential to enhance the feature learning process for better EEG decoding. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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An Integrated intelligent Autonomous Driving Bulldozer System: Pose Estimation, Object Detection, and Work Planning for Dumping Operations

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Journal of Field Robotics 2025年第0期

作者： Peng, Gang Gao, Qiang Liu, Xingyu Zhou, Yicheng Duan, Hangqi Wu, ZhanGang Hu, Bin Zhu, Xukang Xu, Daosheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan China Shantui Construction Machinery Co. Ltd. Jining China

In recent years, the rapid advancement of automation control and intelligent sensing technologies has positioned autonomous driving as a focal point of interest for both academia and industry. As core equipment in modern construction and industrial production, engineering machinery urgently requires intelligent transformation. To promote the intelligent development of engineering machinery, we have designed an integrated intelligent autonomous driving bulldozer system, which can be extended to various types of engineering machinery. For the specific mine dumping operation environment, we propose a multisensor fusion pose estimation algorithm framework based on the Global Navigation Satellite System, inertial measurement unit, visual cameras, and light detection and ranging (LiDAR) to address issues arising from sensor failures under harsh construction conditions. Furthermore, to ensure safety during task execution, we design a three-dimensional object detection method based on the optimal observation plane from LiDAR data. By integrating pose estimation and environmental perception results, we develop a comprehensive work planning and path-tracking algorithm to maintain task efficiency. Experimental results demonstrate that our intelligent autonomous driving bulldozer system performs excellently under various working conditions. The accuracy of its pose estimation, object detection, and path tracking meets the requirements of actual construction environments, showcasing its significant potential in engineering applications. © 2025 Wiley Periodicals LLC.

关键词： Construction equipment

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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

Patterns

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Patterns 2025年

作者： Li, Yi-Fan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs. © 2025 The Author(s)

关键词： Deep learning

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Edible prodelphinidins that improve postprandial hyperglycemia were discovered in Chinese bayberry(Myrica rubra *** Zucc.)fruits

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Food Science and Human Wellness 2025年第3期14卷 1008-1016页

作者： Yi Wang Laiming Zhang Jiaxiong Wu Chengxiao Yu Xingqian Ye Haibo Pan Shiguo Chen College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and EquipmentZhejiang Key Laboratory for Agro-food ProcessingIntegrated Research Base of Southern Fruit and Vegetable Preservation TechnologyZhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality ControlZhejiang UniversityHangzhou 310058China Ningbo Research Institute Zhejiang UniversityNingbo 315100China Zhejiang University Zhongyuan Institute Zhengzhou 450000China Innovation Center of Yangtze River Delta Zhejiang UniversityJiaxing 314102China

Plant polyphenols are potential substitutes for clinicalα-glucosidase *** previous studies indicated that prodelphinidins from Chinese bayberry leaves(BLPs)have strongerα-glucosidase inhibitory activity than other common polyphenols,but they have no safe history of *** is a reasonable prospect that prodelphinidins from Chinese bayberry fruits(BFPs)can improve postprandial hyperglycemia,though known active components are only myricetin and ***,the aim of this study was to analyze structure and hypoglycemic effect of BFPs of“Biqi”(BQPs)and“Dongkui”(DKPs)cultivars,and compare their difference with *** contents of BQPs and DKPs were(221.73±0.60)and(136.82±4.33)mg epicatechin(EC)equivalents/g dry weight,*** most subunits were epigallocatechin gallate along with a small amount of epigallocatechin,epicatechin gallate and EC connected by B-type linkages and a small portion of A-type linkages with mean polymerization degree of 4.25 and 4.08,***,BQPs and DKPs wereα-glucosidase inhibitors with half inhibitory concentration of 11.91 and 9.47μg/m L respectively,which were significantly stronger than *** could also improve postprandial hyperglycemia of normal mice and high fat diet-induced type II diabetic ***,edible prodelphinidins,which have stronger hypoglycemic effect than BLPs,were first found in Chinese bayberry fruits.

关键词： Chinese bayberry fruit Prodelphinidins Structure α-Glucosidase Postprandial hyperglycemia

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MASS FLOW DETECTION TECHNOLOGY FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural Engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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Antifungal activity of curcumin-mediated photodynamic inactivation against Fusarium graminearum on maize

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Grain & Oil Science and Technology 2025年第1期8卷 21-31页

作者： Shuliang Dong Lu Chen Shaojie Li Konglong Feng Guang Liu Hao Dong Guizhi Xu Huajian Ou Yang Liu Yong Zhao Jingjing Wang Guangdong Provincial Key Laboratory of Intelligent Food Manufacturing Foshan UniversityFoshan 528225China College of Food Science and Technology Shanghai Ocean UniversityShanghai 201306China Shanghai Sixth People’s Hospital Affiliated to Shanghai JiaoTong University Shanghai 201306China Sericultural&Agri-Food Research Institute Guangdong Academy of Agricultural Sciences/Key Laboratory of Functional Foods Ministry of Agriculture and Rural Affairs/Guangdong Key Laboratory of Agricultural Products ProcessingGuangzhou 510610China College of Light Industry and Food Sciences Zhongkai University of Agriculture and EngineeringGuangzhou 510225China National Technical Center(Foshan)for Quality Control of Famous and Special Agricultural Products Foshan 528225China

The natural curcumin-mediated photodynamic inactivation(PDI)was developed,and its inactivation potency against Fusarium graminearum in vitro and in vivo was systematically investigated by fluorescence probe assay,trypan blue staining,scanning electron microscope(SEM),confocal laser scanning microscopy(CLSM),*** showed that under the irradiation of blue LED,the photosensitizer of curcumin was excited to generate massive reactive oxygen species(ROS)in the cells of ***,and the PDI completely inactivated their mycelia and spores under the treatment of 150μM curcumin and 10.8 J/cm^(2)*** analysis found that the PDI ruptured the cellular microstructures,damaged the cell membrane by increasing its permeability and oxidizing the lipids,degraded the intracellular DNA and proteins inside the spores of ***,the PDI also potently killed>99.99%spores of *** on maize under the treatment of 200μM curcumin and 10.8 J/cm^(2)***,the PDI suppressed the production of zearalenone(ZEN),and residual ZEN could not be detected after the storage of maize for 10 ***,this study systematically explored the inactivation efficiency of curcumin-mediated PDI against both the mycelia and spores of ***,which provides a valid and promising method to control the fungal hazards in grains.

关键词： Fusarium graminearum Photodynamic inactivation(PDI) Spores Mycelia Zearalenone(ZEN)

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Resilience-promoting day-ahead schedule in coastal integrated energy system considering subjective uncertainty

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Energy 2025年 328卷

作者： Yu, De-Qi Xiao, Jiang-Wen Wang, Yan-Wu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Wuhan 430074 China

This paper studies the resilience enhancement of the energy and water supply in coastal multi-energy system under extreme weather events. A resilience-promoting day-ahead scheduling model is proposed for coastal integrated power-gas-water system under windstorms. Energy coupling and fragility curves related to wind speed are applied into the proposed model to improve system resilience. Specially, subjective uncertainties of operators are described by the mental accounting theory to address the impact of operators’ subjective perceptions on decision analysis, and to enhance psychological satisfaction of operators. The alternating direction method of multipliers algorithm with alternating optimization procedure is employed to obtain a feasible solution for the proposed model while preserving customer privacy. Case studies validate the effectiveness of the proposed model and method by comparative tests. © 2025 Elsevier Ltd

关键词： Day-ahead schedule Integrated power-gas-water system Mental accounting theory Resilience

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