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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Cai Chen Ningling Zhang Danyang Lv Yunjie Zhang Zhi Li Chongxuan Tian Xiangwei Meng Fulai Peng Fengxia Wu Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China Institute of Biomedical Engineering School of Control Science and Engineering Shandong University Jinan China School of Basic Medical Sciences Shandong University Jinan China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Traditional polysomnography monitoring is complicated and time-consuming, and bracelets and other sleep monitoring devices cannot directly detect respiratory airflow. In this work, we designed a portable sleep monitoring equipment that connects to the forehead and captures data on air flow directly. The sleep monitoring system can gather data from the following modules: blood oxygen, respiration, acceleration sensor, bioelectrical signal collection, and microphone. In addition, this work describes a method for identifying respiratory peak using a dynamic threshold to determine respiratory frequency. Fourteen healthy individuals took part in the investigation, wearing both our portable sleep monitoring equipment and a reference device at night. Due to the obvious phenomenon of baseline drift, the smooth filtering begins after baseline correction. For respiratory events such as oral respiration, apnea, and hypopnea, we set the maximum threshold and minimum threshold update time. The results reveal that the portable sleep monitoring system can accurately identify the breathing peaks.

关键词： Forehead Heuristic algorithms Signal processing algorithms Nose Signal processing Sleep apnea Internet of Things Wearable devices Monitoring Microphones

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Siamese Network for EEG Classification of Motor Execution and Imagery

Siamese Network for EEG Classification of Motor Execution an...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Zhi Li Cai Chen Fulai Peng Danyang Lv Chongxuan Tian Xiangwei Meng Ningling Zhang Fengxia Wu Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China Biomedical Engineering Institute School of Control Science and Engineering Shandong University Jinan China School of Basic Medical Sciences Shandong University Jinan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Motor imagery (MI), the mental simulation of movement without physical execution, is a pivotal aspect of brain-computer interface (BCI) research, facilitating direct communication between the brain and external devices. Non-invasive BCIs, particularly those utilizing electroencephalogram (EEG) signals, face inherent challenges, including high-dimensional data, temporal dependencies, and variability across subjects. This study investigates the potential of Siamese networks–a deep learning framework grounded in contrastive learning–for single-channel EEG classification in MI tasks. Through a series of comparative experiments employing identical parameter settings, including ten-fold cross-validation, we demonstrate that Siamese networks outperform convolutional neural networks (CNNs), yielding an average classification accuracy of approximately 10% higher. These findings underscore the superior capability of Siamese networks in capturing subtle temporal patterns and mitigating inter-subject variability, positioning them as a promising alternative to traditional CNN-based approaches in EEG-based MI classification.

关键词： Accuracy Time series analysis Motors Feature extraction Solids Electroencephalography Brain-computer interfaces Stability analysis Convolutional neural networks Thermal stability

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Excess Mean Squared Error of Empirical Bayes Estimators

arXiv

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arXiv 2025年

作者： Ju, Yue Wahlberg, Bo Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Competence Centre for Advanced BioProduction by Continuous Processing AdBIOPRO Sweden

Empirical Bayes estimators are based on minimizing the average risk with the hyper-parameters in the weighting function being estimated from observed data. The performance of an empirical Bayes estimator is typically evaluated by its mean squared error (MSE). However, the explicit expression for its MSE is generally unavailable for finite sample sizes. To address this issue, we define a high-order analytical criterion: the excess MSE. It quantifies the performance difference between the maximum likelihood and empirical Bayes estimators. An explicit expression for the excess MSE of an empirical Bayes estimator employing a general data-dependent hyper-parameter estimator is derived. As specific instances, we provide excess MSE expressions for kernel-based regularized estimators using the scaled empirical Bayes, Stein’s unbiased risk estimation, and generalized cross-validation hyper-parameter estimators. Moreover, we propose a modification to the excess MSE expressions for regularized estimators for moderate sample sizes and show its improvement on accuracy in numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Correction to: An advanced reinforcement learning control method for quadruped robots in typical urban terrains (International Journal of Machine Learning and Cybernetics, (2024), 10.1007/s13042-024-02478-9)

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International Journal of Machine Learning and Cybernetics 2025年 1-2页

作者： Yan, Chi Wang, Ning Gao, Hongbo Wang, Xinmiao Tang, Chao Zhou, Lin Li, Yuehua Wang, Yue School of Information Science and Technology University of Science and Technology of China No. 96 Jinzhai Road Anhui Hefei230026 China School of Information and Security Chongqing College of Mobile Communication No. 36 Dengying Avenue Qijiang District Sichuan Chongqing401520 China Institute of Advanced Technology University of Science and Technology of China No. 5089 Wangjiang West Road Anhui Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Nanyang639798 Singapore Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Zhejiang Hangzhou311121 China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China

In this article, the affiliation details for Authors "Ning Wang2, Hongbo Gao1,3,4, Yuehua Li5, Yue Wang6" were interchanged and it is corrected now. Incorrect affiliation: 2institute of advanced technology, University of Science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 3School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 4Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 5College of control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China 6School of Information and Security, Chongqing College of Mobile Communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China Correct affiliation: 2School of Information and Security, Chongqing College of Mobile Communication, No. 36 Dengying Avenue, Qijiang District, Chongqing 401520, Sichuan, China 3institute of advanced technology, University of Science and technology of China, No. 5089 Wangjiang West Road, Hefei 230088, Anhui, China 4School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Nanyang, Singapore 5Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Hangzhou 311121, Zhejiang, China 6College of control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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Broadband Optical Nonlinearities and Ultrafast Carrier Dynamics of GO-CuS Hybrid Film

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Journal of Lightwave technology 2025年

作者： Zhang, Xuefeng Bai, Xuli Liu, Xiaofei Wang, Dan Han, Hong Xiao, Zhengguo Zhang, Jianguo Nie, Zhongquan Taiyuan University of Technology College of Materials Science and Engineering Institute of New Carbon Materials Taiyuan030024 China Taiyuan University of Technology Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Electronic Information and Optical Engineering Taiyuan030024 China Taiyuan Institute of Technology Department of Science Taiyuan030008 China Tongren University Department of Physics and Electronic Engineering Tongren China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha410073 China

We report the broadband optical nonlinearities and ultrafast carrier dynamics of GO doped with CuS nanoparticles (GO-CuS) following the femtosecond pulse lasers. Both the pure GO and GO-CuS hybrid films are first synthesized by the vacuum filter deposition method and their optical nonlinearities are then examined in the wide wavelength range (400 nm-1064 nm) based on the femtosecond Z-scan technique. The results reveal that, compared to the pristine GO, the introduction of CuS not only makes the GO-CuS hybrid film switched from saturated absorption to reverse saturated absorption possible, but also achieves strong broadband optical nonlinear absorption responses and optical limiting effects. It is demonstrated that the strongest nonlinear absorption coefficient (∼3.6×10-7 m/W) appears at 400 nm excitation, which is dozens of times larger than that under the illumination of 1064 nm. By leveraging the femtosecond pump-probe technique, we further study the ultrafast carrier dynamics of the GO-CuS hybrid film carrying the fast and slow relaxation processes. Moreover, the physical mechanisms behind these excellent nonlinear optical responses are elucidated. The findings presented here indicate that the GO-CuS hybrid film can be regarded as a potential promising candidate in various ultrafast photonics devices. © 1983-2012 IEEE.

关键词： Femtosecond lasers

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Co-exposure to ozone and polystyrene nanoplastic exacerbates cognitive impairment and anxiety-like behavior by regulating neuronal pyroptosis in mice

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Environment International 2025年 199卷

作者： Dai, Anqi Liu, Xudong Chen, Yinuo Wang, Yougang Qi, Haomin Zeng, Yan Li, Jinquan Hubei Provincial Clinical Research Center for Alzheimer's Disease Brain Science and Advanced Technology Institute School of Medicine Wuhan University of Science and Technology Wuhan China Department of Food Science and Engineering Moutai Institute Renhuai564507 China Hubei Province Key Laboratory of Occupational Hazard Identification and Control Wuhan University of Science and Technology Wuhan China

Ozone (O3) and nanoplastics (NPs) are pervasive environmental pollutants that frequently co-occur in our heavily industrialized era. While it has been documented that exposure to O3 or NPs individually has neurotoxic effects, studies investigating their combined impact and the hazardous mechanisms resulting from co-exposure are limited. In this study, we established a mouse model co-exposure to polystyrene nanoparticles (PS-NPs) and O3, focusing on the prefrontal cortex (PFC), a brain region crucial for cognition and emotion. We examined the effects of O3 and PS-NPs on behavioral changes related to learning, memory, and anxiety, employing transcriptome sequencing alongside molecular and histopathological methods. Our findings indicate that combined exposure to O3 and PS-NPs disrupts the integrity of the blood–brain barrier, reducing Claudin 5 expression and leading to increased accumulation of PS-NPs in the PFC. Transcriptome sequencing demonstrated the involvement of the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway and oxidative stress in the pathological changes observed in the PFC. Through immunohistochemical and immunofluorescence analysis, we observed enhanced microglial activation, which correlates with increased production of inflammatory factors. Additionally, western blot and immunofluorescence co-labeling analyses revealed elevated expression levels of GSDMD-N, caspase-1, IL-1β, and IL-18 proteins, which are associated with neuronal pyroptosis. Finally, immunofluorescence co-labeling confirmed that the activation of the p38 MAPK pathway in neurons is involved in co-exposure-induced pyroptosis. Meanwhile, N-Acetylcysteine (NAC), a common antioxidant, can alleviate neuroinflammation and neuronal pyroptosis in the PFC, and it rescued the cognitive deficits and anxiety-like behaviors observed in the co-exposed mice. Our study illustrates that co-exposure to O3 and NPs can aggravate damage to the blood–brain barrier and elevate oxidative st

关键词： Neurons

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Decoupling Representation Learning for Remote Sensing Semantic Segmentation

IEEE Journal on Miniaturization for Air and Space Systems

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IEEE Journal on Miniaturization for Air and Space Systems 2025年

作者： He, Yun Li, LinJuan Xie, Gang Zhang, Haoxue Chen, Feng Liu, Sida Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China Shanxi Key Laboratory Of Advanced Control and Industrial Intelligence Taiyuan030024 China Shanxi Institute of Surveying Mapping and Geoinformation Taiyuan030024 China

Semantic segmentation plays a pivotal role in the interpretation of remote sensing entities. However, the complexity of the environments, diversity of objects, and richness of details make semantic segmentation even more challenging. Existing methods have limitations in inter-class edge continuity and intra-class completeness within segmentation results. To address these issues, a feature decoupling guided Network is developed for learning the discriminative representation. In which the feature decoupling module separates encoded features into homogeneous information for primary object features and distinct information for object boundaries. Another, the semantic-aware integration unit is employed to strengthen semantic consistency during decomposition. To facilitate practical application, we created the Taiyuan Land Cover (TYLC) dataset for semantic segmentation to analyze land resource utilization. Extensive experiments on the TYLC dataset achieved a mean intersection over the union of 54.2%, the mean F1 score of 67.7%, and the mean recall of 66.6%. Quantitative results demonstrate the algorithm’s superiority, and visualizations indicate that the segmentation output has excellent completeness and edge continuity. © 2025 IEEE. All rights reserved.

关键词： Semantic Segmentation

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MKMGCN-DDI: Predicting Drug-Drug Interactions via Magnetic Graph Convolutional Network With Multiple Kernels

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 469-480页

作者： Ming Chen Yunhan Pan Xiujuan Lei Chunyan Ji Yinglong Dai Yi Pan Department of Artificial Intelligence Hunan Normal University Changsha Hunan China School of Computer Science Shaanxi Normal University Xi’an China Computer Science Department BNU-HKBU United International College Zhuhai China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

关键词： Drugs Predictive models Magnetic separation Laplace equations Bioinformatics Sodium Market research Graph convolutional networks Computer science Computational modeling

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Multi-frequency formation mechanism and modulation strategy of self-priming enhanced submerged pulsed waterjet

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International Journal of Mining Science and technology 2025年第2期35卷 175-189页

作者： Haojie Jia Yanwei Liu Weiqin Zuo Hongkai Han Ping Chang Mohammad Waqar Ali Asad Guozhong Hu Jian Miao Hani SMitri College of Safety Science and Engineering Henan Polytechnic UniversityJiaozuo 454003China Henan International Joint Laboratory of Coal Mine Safety and Occupational Hazards Prevention and Control Jiaozuo 454003China Zhengzhou Institute for Advanced Research of Henan Polytechnic University Zhengzhou 451464China WA School of Mines:Minerals Energy and Chemical EngineeringCurtin UniversityKalgoorlie WA 6430Australia School of Mines China University of Mining and TechnologyXuzhou 221116China School of Intelligent Engineering Shandong Management UniversityJinan 250357China Department of Mining and Materials Engineering McGill UniversityMontreal H3A0E8Canada

Under submerged conditions, compared with traditional self-excited oscillating pulsed waterjets(SOPWs), annular fluid-enhanced self-excited oscillating pulsed waterjets(AFESOPWs) exhibit a higher surge pressure through self-priming. However, their pressure frequency and cavitation characteristics remain unclear, resulting in an inability to fully utilize resonance and cavitation erosion to break coal and rock. In this study, high-frequency pressure testing, high-speed photography, and large eddy simulation(LES) are used to investigate the distribution of the pressure frequency band, evolution law of the cavitation cloud, and its regulation mechanism of a continuous waterjet, SOPW, and AFESOPW. The results indicated that the excitation of the plunger pump, shearing layer vortex, and bubble collapse corresponded to the three high-amplitude frequency bands of the waterjet pressure. AFESOPWs have an additional self-priming frequency that can produce a larger amplitude under a synergistic effect with the second high-amplitude frequency band. A better cavitation effect was produced after self-priming the annulus fluid, and the shedding frequency of the cavitation clouds of the three types of waterjets was linearly related to the cavitation number. The peak pressure of the waterjet and cavitation erosion effect can be improved by modulating the waterjet pressure oscillation frequency and cavitation shedding frequency.

关键词： Multi-frequency Modulation Self-priming Submerged waterjet Cavitation

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