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Long tunnel group driving fatigue detection model based on XGBoost algorithm

Journal of Traffic and Transportation Engineering(English Edition) 2025年第01期 167-179页

作者： Huazhi Yuan Kun Zhao Ying Yan Li Wan Zhending Tian Xinqiang Chen School of Civil Engineering Lanzhou University of Technology School of Transportation Engineering Chang'an University Shandong Transportation Planning and Design Institute group Co. Ltd. China Railway First Survey and Design Institute group Co. Ltd. Institute of Logistics Science and Engineering Shanghai Maritime University

Driving fatigue is one of the important causes of accidents in tunnel(group) sections. In this paper, in order to effectively identify the driving fatigue of tunnel(group) drivers, an eye tracker and other instruments were used to conduct real vehicle tests on long tunnel(group)expressways and thus obtain the eye movement, driving duration, and Karolinska sleepiness scale(KSS) data of 30 drivers. The impacts of the tunnel and non-tunnel sections on drivers were compared, and the relationship between blink indexes, such as the blink frequency,blink duration, mean value of blink duration, driving duration, and driving fatigue, was studied. A paired t-test and a Spearman correlation test were performed to select the indexes that can effectively characterize the tunnel driving fatigue. A driving fatigue detection model was then developed based on the XGBoost algorithm. The obtained results show that the blink frequency, total blink duration, and mean value of blink duration gradually increase with the deepening of driving fatigue, and the mean value of blink duration is the most sensitive in the tunnel environment. In addition, a significant correlation exists between the driving duration index and driving fatigue, which can provide a reference for improving the tunnel safety. Using the mean value of blink duration and driving duration as the characteristic indexes, the accuracy of the driving fatigue detection model based on the XGBoost algorithm reaches 98%. The cumulative and continuous tunnel proportion effectively estimates the driving fatigue state in a long tunnel(group) environment.

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Interpretable Data-Driven Learning With Fast Ultrasonic Detection for Battery Health Estimation

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 267-269页

作者： Kailong Liu Yuhang Liu Qiao Peng Naxin Cui Chenghui Zhang the School of Control Science and Engineering Shandong University the Information Technology Analytics & Operations GroupQueen's University Belfast

Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.

关键词： Electronic health record

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Isothermal reduction kinetics and reduction prediction for iron ore pellets

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Journal of Iron and Steel Research International 2025年第1期32卷 64-72页

作者： Fei Meng Hao Liu Yue-lin Qin Huang-jie Hua Yin Deng Ze-zheng Sun Long-hai Liu School of Metallurgy and Materials Engineering Chongqing University of Science and TechnologyChongqing401331China CISDI group Co. Ltd.Chongqing401122China College of Materials Science and Engineering Chongqing UniversityChongqing400044China Sichuan Desheng group Vanadium&Titanium Co. Ltd.Leshan614000SichuanChina

Iron ore pellets,as one of the main charges of blast furnaces,have a greater impact on the CO_(2)emission reduction and stable operation of blast *** isothermal reduction behavior of the pellets obtained from a Chinese steel plant was studied in the gas mixtures of CO and N_(2).The results showed the reduction process is divided into two *** reduction in the initial stage(time t≤40 min)is cooperatively controlled by internal diffusion and interface chemical reactions with the activation energy of 30.19 and 16.67 kJ/mol,*** controlling step of the reduction in the final stage(t>40 min)is internal diffusion with the activation energy of 34.60 kJ/*** reduction process can be described by two equations obtained from kinetic *** reduction degree can be predicted under different temperatures and time,and the predicted results showed an excellent correlation with the experimental *** reduction mechanisms were confirmed by the analysis of the scanning electron microscope equipped with an energy dispersive spectrometer and optical microscope.

关键词： Pellet Reduction behavior Kinetics Mechanism Reduction prediction

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When AI meets sustainable 6G

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Science China(Information Sciences) 2025年第1期68卷 4-22页

作者： Xiaohu YOU Yongming HUANG Cheng ZHANG Jiaheng WANG Hao YIN Hequan WU National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories Chinese Academy of Military Science China Information Communication Technologies group

Sixth-generation(6G) networks are anticipated to achieve transformative advancements, characterized by extreme connectivity, deep integration with artificial intelligence(AI) and sensing, and airground integration. The evolution of 6G exhibits two major trends: ubiquitous intelligence and sustainability. The former aims to embed state-of-the-art AI technology into the 6G network, from the physical layers to applications, while the latter emphasizes reducing energy consumption while enhancing network performance to address environmental concerns. Despite the amazing progress in recent years, AI advancements come with substantial increases in data and computational overhead, posing critical challenges for integrating AI into sustainable 6G networks. First, high energy consumption from large datasets and heavyweight AI models contradicts6G's green goals. Second, the precise collection of large datasets, message delivery latency, and inference delays in AI models pose challenges for real-time tasks in 6G. Third, the uninterpretability and unpredictability of AI models complicate meeting the stringent requirements for controllable transmission in dynamic wireless environments. Addressing these challenges and achieving sustainable 6G with ubiquitous intelligence calls for a revolutionary design of 6G architecture and AI frameworks. To this end, this paper introduces a novel and practical methodology for green, real-time, and controllable 6G native intelligence,starting with knowledge graph(KG) analysis to extract small but critical datasets, followed by the development of distributed lightweight AI models, and the use of digital twins(DTs) to create precise replicas of physical 6G networks. This leads to a pervasive multi-level(PML)-AI framework supported by a task-centric, three-layer 6G architecture. The AI framework operates through non-real-time and real-time cycles, leveraging three key technologies: wireless data KGs for efficient data management,lightweight AI mode

关键词： artificial intelligence sustainable 6G green real-time controllable

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Construction Monitoring and Analysis of Asymmetric Prestressed Concrete Bridge Crossing Multiple-Line Railways

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Structural Durability & Health Monitoring 2025年第2期19卷 385-398页

作者： Yi Wang Bing Wang Changwen Li Feng Zheng Yong Liu Shaohua He Guangzhou Railway Investment Construction group Co. Ltd.Guangzhou510000China China Railway Third Bureau group Co. LtdTaiyuan030024China School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou510006China

Complex bridge structures designed and constructed by humans often necessitate extensive on-site execution,which carries inherent ***,a variety of engineering practices are employed to monitor bridge *** paper presents a case study of a large-span prestressed concrete(PC)variable-section continuous girder bridge in China,proposing a feedback system for construction monitoring and establishing a finite element(FE)analysis model for the entire *** alignment of the completed bridge adheres to the initial design expectations,with maximum displacement and pre-arch differences from the ideal state measuring 6.39 and 17.7 mm,respectively,which were less than the 20 mm limit required by the ***,the stress monitoring showed that the maximum compressive stress was 10.44 MPa,which was 7.5%different from the finite element results,and better predicted the most unfavorable possible *** results demonstrate that a scientifically rigorous construction monitoring and feedback system can ensure the safety of bridge construction and meet the expected construction *** findings presented in this paper provide valuable insights for bridge construction monitoring practices.

关键词： Continuous girder bridge construction monitoring bridge alignment stress monitoring

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Localizing base stations with measured data:a concatenated image-based deep learning approach

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Science China(Information Sciences) 2025年第2期68卷 385-386页

作者： Yang CHEN Ziyuan LIU Feng JIANG Hong SHEN Wei XU Mengyu LI Ritao CHENG National Mobile Communications Research Laboratory Southeast University China Mobile group Design Institute Co. Ltd.

Localization has become an indispensable function of modern cellular communication systems [1]. In a cloud radio access network(C-RAN), the remote radio head(RRH) is the actual signal transmitter, whose location can assist in wireless network layout optimization and wireless resource management. The measurement of the RRH location can only be done manually on-site due to the lack of global positioning systems(GPS) at the RRH side, which is thus not a cost-effective solution for the widely used C-RAN with a large number of RRH units.

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Force and impulse multi-sensor based on flexible gate dielectric field effect transistor

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 214-220页

作者： Chao Tan Junling Lü Chunchi Zhang Dong Liang Lei Yang Zegao Wang College of Materials Science and Engineering Sichuan UniversityChengdu 610065China The 54th Research Institute of China Electronics Technology group Corporation Shijiazhuang 050081China

Nowadays,force sensors play an important role in industrial production,electronic information,medical health,and many other ***-dimensional material-based filed effect transistor(2D-FET)sensors are competitive with nano-level size,lower power consumption,and accurate ***,few of them has the capability of impulse detection which is a path function,expressing the cumulative effect of the force on the particle over a period of *** we fabricated the flexible polymethyl methacrylate(PMMA)gate dielectric MoS_(2)-FET for force and impulse sensor *** systematically investigated the responses of the sensor to constant force and varying forces,and achieved the conversion factors of the drain current signals(I_(ds))to the detected impulse(I).The applied force was detected and recorded by I_(ds)with a low power consumption of~30 *** sensitivity of the device can reach~8000%and the 4×1 sensor array is able to detect and locate the normal force applied on ***,there was almost no performance loss for the device as left in the air for two months.

关键词： flexible gate dielectric transistor force sensor impulse sensor force sensor array

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Oligodendroglial heterogeneity in health,disease,and recovery:deeper insights into myelin dynamics

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Neural Regeneration Research 2025年第11期20卷 3179-3192页

作者： Pieter-Jan Serneels Julie D.De Schutter Lies De Groef Lieve Moons Steven Bergmans KU Leuven Leuven Brain InstituteDepartment of BiologyAnimal Physiology and Neurobiology DivisionNeural Circuit Development&Regeneration Research GroupLeuvenBelgium KU Leuven Leuven Brain InstituteDepartment of BiologyAnimal Physiology and Neurobiology DivisionCellular Communication&Neurodegeneration Research GroupLeuvenBelgium

Decades of research asserted that the oligodendroglial lineage comprises two cell types:oligodendrocyte precursor cells and ***,recent studies employing single-cell RNA sequencing techniques have uncovered novel cell states,prompting a revision of the existing *** forward,the oligodendroglial lineage should be delineated into five distinct cell states:oligodendrocyte precursor cells,committed oligodendrocyte precursor cells,newly formed oligodendrocytes,myelin-forming oligodendrocytes,and mature *** new classification system enables a deeper understanding of the oligodendroglia in both physiological and pathological *** this uniform terminology will facilitate comparison and integration of data across ***,including the consolidation of findings from various demyelinating models,is essential to better understand the pathogenesis of demyelinating ***,comparing injury models across species with varying regenerative capacities can provide insights that may lead to new therapeutic strategies to overcome remyelination ***,by standardizing terminology and synthesizing data from diverse studies across different animal models,we can enhance our understanding of myelin pathology in central nervous system disorders such as multiple sclerosis,Alzheimer's disease,and amyotrophic lateral sclerosis,all of which involve oligodendroglial and myelin dysfunction.

关键词： animal models demyelination fish mammals multiple sclerosis myelin neurodegeneration oligodendrocytes oligodendroglial lineage remyelination

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Enteromorpha prolifera polysaccharide-Fe(Ⅲ) complex promotes intestinal development as a new iron supplement

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Science China(Life Sciences) 2025年第1期68卷 219-231页

作者： Yingying Feng Yuying Wu Jialu Wang Zhenglin Dong Qian Yu Shuangshuang Xia Chunxue Liu Haihua Wang Xin Wu Tianjin Institute of Industrial Biotechnology Chinese Academy of SciencesTianjin 300308China CAS Key Laboratory of Agro-ecological Processes in Subtropical Region Institute of Subtropical AgricultureChinese Academy of SciencesChangsha 410125China ANYOU Biotechnology group Co.Ltd Taicang 215412China Qingdao Seawin Biotech group Co. LTDQingdao 266071China

Iron is a crucial micronutrient, and its deficiency can have detrimental effects on the health of infants. Dietary polysaccharide-iron(Ⅲ) complexes(PICs) are promising for addressing iron deficiency due to their minimal adverse reactions and high iron absorption rate. This study aimed to investigate the effects of dietary Enteromorpha prolifera polysaccharide-Fe(Ⅲ) complex(EP-Fe) on newborns, using 3-day weaned piglets as the iron-deficiency model. Results showed that EP-Fe improved iron levels and promoted intestinal development in piglets. Transcriptome sequencing revealed that EP-Fe increased the survival of intestinal epithelial cells under hypoxia by upregulating the expression of genes that promote the development of the vascular system. Additionally, EP-Fe enhanced the mucosal barrier functions by inhibiting myosin light chain kinase(MLCK)/phosphorylated myosin light chain(p-MLC) signaling pathway to increase the expression of intestinal tight junction proteins. Furthermore, the 16S r RNA gene sequencing of gut microbiota showed that EP-Fe promoted the enrichment of Bacteroides_fragilis and other gut microbes that can metabolize carbohydrates. In conclusion, EP-Fe is an effective iron supplement for newborns, and it can be developed as a comprehensive nutritional supplement.

关键词： Enteromorpha prolifera polysaccharide-Fe(III)complex iron deficiency intestinal development gut microbiota weaned piglet

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Satellite-assisted 6G wide-area edge intelligence:dynamics-aware task offloading and resource allocation for remote IoT services

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Science China(Information Sciences) 2025年第2期68卷 248-268页

作者： Di ZHAO Rui DING Bin SONG State Key Laboratory of Integrated Services Networks Xidian University Hangzhou Institute of Technology Xidian University China Satellite Network group Co. Ltd.

Future 6G networks will integrate non-terrestrial communication with mobile edge computing to enable wide-area edge intelligence, providing ubiquitous communication and computation services for everyone and everything. In this paper, we introduce the intelligent cloud-edge-device architecture into low Earth orbit(LEO) satellite-supported remote Internet of Things(IoT) networks to facilitate latency-sensitive and compute-intensive IoT applications. However, the dynamic spatio-temporal characteristics inherent in LEO satellite networks, such as the high volatility of task traffic and the high mobility of space nodes, pose severe challenges. For efficient task execution, we investigate the joint optimization of cross-region task offloading and cross-domain resource allocation, and innovatively propose a spatio-temporal attention-based proximal policy optimization(STA-PPO) algorithm. Specifically, the temporal attention-based actor network analyzes the timing dependencies of task arrival and makes time-variant decisions. Simultaneously, the spatial attention-based critic network captures the spatial variations of satellite motion and evaluates the topology-related value. We verify the effectiveness of the cloud-edge-device collaboration through extensive simulations. Numerical results demonstrate that the STA-PPO algorithm outperforms benchmarks, showing the lowest system delay and the highest link throughput.

关键词： 6G wide-area edge intelligence cross-region task offloading cross-domain resource allocation spatio-temporal attention proximal policy optimization

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