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Generative Semantic Communication:Architectures,Technologies,and Applications 认领 引用 被引量：4
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作者 Jinke Ren Yaping Sun +7 位作者 Hongyang Du Weiwen Yuan Chongjie Wang Xianda Wang Yingbin Zhou Ziwei Zhu Fangxin Wang Shuguang Cui 《Engineering》 SCIE EI CSCD 2026年第1期45-61,共17页
Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the so... Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the source data rather than the complete set of bits(symbols).However,traditional deep-learning-based SemCom systems present challenges such as limited generalization,low robustness,and inadequate reasoning capabilities,primarily due to the inherently discriminative nature of deep neural networks.To address these limitations,generative artificial intelligence(GAI)is seen as a promising solution,offering notable advantages in learning complex data distributions,transforming data between high-and low-dimensional spaces,and generating high-quality content.This paper explores the applications of GAI in SemCom and presents a comprehensive study.It begins by introducing three widely used SemCom systems enabled by classical GAI models:variational autoencoders,generative adversarial networks,and diffusion models.For each system,the fundamental concept of the GAI model,the corresponding SemCom architecture,and a literature review of recent developments are provided.Subsequently,a novel generative SemCom system is proposed,incorporating cutting-edge GAI technology—large language models(LLMs).This system features LLM-based artificial intelligence(AI)agents at both the transmitter and receiver,which act as“brains”to enable advanced information understanding and content regeneration capabilities,respectively.Unlike traditional systems that focus on bitstream recovery,this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter.As a result,the communication paradigm shifts from“information recovery”to“information regeneration,”marking a new era in generative SemCom.A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system,showing a 99.98%reduction in communication overhead and a 53%improvement in average retrieval accuracy compared to traditional communication systems.Furthermore,four typical application scenarios for generative SemCom are described,followed by a discussion of three open issues for future research.In summary,this paper provides a comprehensive set of guidelines for applying GAI in SemCom,laying the groundwork for the efficient deployment of generative SemCom in future wireless networks. 展开更多
关键词 Semantic communication Generative artificial intelligence Large language model Variational autoencoder Generative adversarial network Diffusion model
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Epigenetic mechanisms of heterosis in tea plants:insights into m6A RNA methylation and miRNA-mediated regulation 认领 引用 被引量：2
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作者 Xiansheng Liao Mengting Tang +4 位作者 Wenlong Lei Hongzheng Lin Shan Jin Pengjie Wang Naixing Ye 《Horticultural Plant Journal》 SCIE CAS CSCD 2026年第6期1479-1491,共13页
Heterosis is fundamental to modern tea breeding practices and is widely used in tea production.However,the epigenetic regulatory mechanisms underlying heterosis in tea plants remain relatively unknown.In this study,we... Heterosis is fundamental to modern tea breeding practices and is widely used in tea production.However,the epigenetic regulatory mechanisms underlying heterosis in tea plants remain relatively unknown.In this study,we integrated transcriptome,RNA methylation,and miRNA profile information to understand the epigenetic basis of heterosis in tea plants.We found that although m6A methylation modifications and miRNAs are highly conserved in tea plants,a significant number of differentially methylated and differentially expressed genes and differentially expressed miRNAs showed non-additive genetic effects in the hybrid.This non-additive expression is closely related to the synthesis of secondary metabolites and plant stress responses,implying that the regulation of these genes plays a key role in enhancing the quality and stress tolerance of hybrids.Through comprehensive analyses of the transcriptome,m6A methylation modifications,and miRNAs,we established a multi-level regulatory network,revealing the molecular mechanisms by which m6A methylation modifications and miRNAs drive heterosis in tea plants from the transcriptional to the post-transcriptional regulatory level.This multi-level regulatory network not only deepens our understanding of tea plant heterosis but also provides novel insights for further studies on the role of m6A methylation and miRNAs in tea plant heterosis. 展开更多
关键词 Camellia sinensis Heterosis Epigenetic Post-transcriptional regulation Multi-level regulatory network
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Effect of dominant fractures on triaxial behavior of 3D-printed rock analogs with internal fracture networks 认领 引用 被引量：2
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作者 Lishuai Jiang Pimao Li +3 位作者 Xin He Yang Zhao Quansen Wu Ye Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1390-1412,共23页
Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly a... Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly affecting the macromechanical properties and failure modes.These fractures affect the instability and failure of the surrounding rock,significantlyimpacting the overall stability of engineering structures.Herein,sand-powder three-dimensional(3D)printing technology was used to prepare rock-like specimens with internal fracture networks.Triaxial compression testing,post-failure fracture mapping,and fractal dimension analysis of the fracture surfaces were conducted to investigate the effects of dominant fracture angles on the strength and deformation of rocks with internal fracture networks under triaxial stress.The results indicate that the dominant fracture angle has a pronounced effect on the mechanical behavior of rock.With increasing angle,both compressive strength and elastic modulus exhibit an initial decline followed by an increase.Moreover,higher confiningpressure significantlyimproves the compressive strength of fractured rock.This enhancement weakens as the confiningpressure further increases.Moreover,with increasing confiningpressure,the differences between the maximum and minimum values of elastic moduli and lateral strain ratios in fractured rock gradually decrease.Thus,the impact of the dominant fracture angle on rock mass deformation decreases with increasing confiningpressure.This research elucidates the effects of dominant fracture angles on the mechanical and failure properties of complex fractured rock masses and the influenceof the confiningpressure on these relationships.It provides valuable theoretical insights and practical guidance for stability analyses in engineering rock masses. 展开更多
关键词 Sand powder three-dimensional(3D) printing Internal fracture networks Triaxial compression Rock mechanics Fractal dimension
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A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks 认领 引用 被引量：2
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作者 Li Zhou Hao Yin +3 位作者 Haitao Zhao Jibo Wei Dewen Hu Victor C.M.Leung 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期561-583,共23页
This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication sys... This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication systems,the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication.The paper first outlines the background and motivation behind AI integration,highlighting the potential for enhanced network performance,autonomy,and adaptability.It then delves into the key AI applications across different network layers,including data sensing and collection,placement and trajectory optimization,radio resource management,routing and topology control,edge computing and caching,as well as security and privacy enhancement.For each application,the paper discusses relevant AI techniques,main findings,optimization objects,and the potential benefits and challenges.The survey also identifies open issues,such as the practical implementation gap,standardization issues,and real-world application barriers,and proposes future directions to address these challenges and further advance the field.In conclusion,the integration of AI with UAV-enabled Wireless Networks(UWNs)holds tremendous potential for transforming wireless communication,enabling new applications and services with unprecedented capabilities. 展开更多
关键词 Artificial intelligence(AI) Machine learning(ML) Unmanned aerial vehicle(UAV) Wireless network
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Multimodal Trajectory Generation for Robotic Motion Planning Using Transformer-Based Fusion and Adversarial Learning 认领 引用 被引量：1
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作者 Shtwai Alsubai Ahmad Almadhor +3 位作者 Abdullah Al Hejaili Najib Ben Aoun Tahani Alsubait Vincent Karovic 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期848-869,共22页
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel... In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics. 展开更多
关键词 Multimodal trajectory generation robotic motion planning transformer networks sensor fusion reinforcement learning generative adversarial networks
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A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices:A cross-sectional study 认领 引用 被引量：3
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作者 Miaoran Guo Hu Liu +6 位作者 Long Gao Hongmei Yu Yan Ren Yingmei Li Huaguang Yang Chenghao Cao Guoguang Fan 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第4期1621-1627,共7页
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice... Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease. 展开更多
关键词 amplitude of low-frequency fluctuation degree centrality feedforward neural network freezing of gait machine learning parahippocampal gyrus Parkinson's disease receiver operating characteristic regional homogeneity resting-state functional magnetic resonance imaging
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Research on the visualization method of lithology intelligent recognition based on deep learning using mine tunnel images 认领 引用 被引量：2
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作者 Aiai Wang Shuai Cao +1 位作者 Erol Yilmaz Hui Cao 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第1期141-152,共12页
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction... An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects. 展开更多
关键词 rock picture recognition convolutional neural network intelligent support for roadways deep learning lithology determination
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Hypoxia and TTR dysregulation in astrocytes from Parkinson’s disease with a specific mitochondrial haplogroup:A single-cell analysis 认领 引用 被引量：1
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作者 Junhao Wang Wenxuan Du +2 位作者 Xinyi Chen Hao Wu Ganqiang Liu 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第8期3807-3814,共8页
Mitochondrial DNA variants have been linked to cognitive progression in Parkinson’s disease;however,the mechanisms by which mitochondrial DNA variants or haplogroups contribute to this process remain unclear.In the p... Mitochondrial DNA variants have been linked to cognitive progression in Parkinson’s disease;however,the mechanisms by which mitochondrial DNA variants or haplogroups contribute to this process remain unclear.In the present study,we analyzed single-nucleus RNA sequencing data from 241 post-mortem brain samples across five regions to investigate the dysregulatory mechanisms associated with mitochondrial DNA haplogroup H and haplogroups J,T,and U#.Our findings revealed significant alterations in the proportions of astrocyte subtypes CHI3L1 and GRM3 in the neocortical regions of haplogroup H.Notably,TTR was markedly downregulated in the dorsal motor nucleus of the Xth nerve region of patients with haplogroup H.Pathway analysis highlighted abnormal hypoxic and reactive oxygen species environments in astrocytes,whereas protein complex analysis revealed a consistent and significant elevation in ribosomal subunit complexes within the astrocyte subtypes.By constructing weighted and directed transcriptome-wide gene regulatory networks,we identified significant changes in transcription factor SP1 and homeobox protein HOXA5 activity in the astrocyte subtypes of individuals with haplogroup H.Additionally,widespread dysregulation was observed in the transcriptional control of TTR by multiple transcription factors.Parkinson’s disease patients with haplogroup H also exhibited increased network functional connectivity in specific brain regions.This data-driven study underscores the potential mechanisms by which mitochondrial DNA haplogroups contribute to cognitive progression in Parkinson’s disease,involving cellular composition changes,differential gene expression,pathway disruption,and gene regulatory networks.Our findings suggest that mitochondrial DNA haplogroup H may drive Parkinson’s disease cognitive progression through aberrant TTR expression and a hypoxic environment. 展开更多
关键词 astrocyte brain network functional connectivity cognitive progression gene regulatory network haplogroup H mitochondrial DNA variants nerve regeneration Parkinson’s disease reactive oxygen species single-nucleus RNA sequencing
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Spatial morphology optimization for reconciling urban expansion with ecological integrity based on a multi-level ecological network framework 认领 引用 被引量：2
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作者 LU Jie JIAO Sheng CHEN Xingli 《Journal of Geographical Sciences》 SCIE CSCD 2026年第2期399-420,共22页
Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecol... Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability. 展开更多
关键词 urban spatial morphology ecological network multi-level coupling scenarios simulation urban expansion
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AI-driven interpretation and prediction of embedded printability based on rheology 认领 引用 被引量：1
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作者 Xianhao Zhou Zhenrui Zhang +7 位作者 Jintian Yu Lixi Ma Sicheng Ma Bingyan Wu Zixuan Wang Ting Zhang Yongcong Fang Zhuo Xiong 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第1期63-79,I0003-I0012,共17页
Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the ... Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the complex interactions between inks and support baths.Here,we present an artificial intelligence(AI)-driven framework that interprets and predicts embedded printability using rheological data.Using a standardized workflow,we extracted 21 rheological descriptors and established 12 indicators to evaluate structural continuity and geometric fidelity.Interpretable machine learning models revealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress,support bath zero shear viscosity,flow behavior index,and time constant.To enable the prediction of printability in a generalizable manner,we further developed a cascaded neural network,which achieved mean relative prediction errors below 15%across all indicators.Experimental validation using three-dimensional(3 D)-printed constructs and micro-computed tomography(μCT)reconstructions confirmed a strong correlation between predicted and actual fidelity.This work establishes a physics-informed,data-driven paradigm for decoding and optimizing embedded printing,offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations. 展开更多
关键词 Embedded bioprinting Printability Rheology Machine learning Neural network
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Diversified rotation reduced N2O emissions by shaping N-cycling microbial communities and increasing their network complexity 认领 引用 被引量：1
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作者 Taobing Yu Lang Cheng +5 位作者 Pin Wang Lei Yang Tengxiang Lian Huadong Zang Zhaohai Zeng Yadong Yang 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第4期593-602,共10页
Increased crop diversity can alter soil nitrogen(N)levels,soil properties,and functional microbial communities,leading to changes in potential nitrous oxide(N2O)emissions.However,our understanding on relationships ... Increased crop diversity can alter soil nitrogen(N)levels,soil properties,and functional microbial communities,leading to changes in potential nitrous oxide(N2O)emissions.However,our understanding on relationships between N2O emissions and related microbes in diversified rotation systems is still limited.Here,we established a long-term field experiment to investigate the response of N2O emissions regulated by five N-cycling genes in three rotation systems.Our results showed that N2O emissions in wheat and maize seasons in diversified rotations(spring maize→winter wheat–summer maize and spring peanut→winter wheat–summer maize)were 15.5%-51.1%and 15.9%-53.3%lower than that in winter wheat–summer maize rotation(P<0.05),respectively.Diversified rotations decreased abundance of ammonia-oxidizing archaea(AOA)amoA,AOB amoA,nirK and nirS genes in both wheat and maize seasons,while increased abundance of nosZ gene in maize season,leading to lower soil N2O emissions.Changes in these functional genes correlated significantly with soil moisture,nitrogen availability,and enzyme activity(L-leucine aminopeptidase and Urease).Besides,diversified rotations increased number of nodes,edges and degree of the co-occurring network and sub-network,while reduced average path length and betweeness.These microbial co-occurrence network complexity indicators were significantly correlated with N2O emissions.This indicates that increase in aboveground crop diversity drives the increase in complexity of belowground N-cycling related microbial interaction networks,which leads to lower N2O emissions.In summary,diversified rotations show promising potentials to lower N2O emissions in agricultural soils. 展开更多
关键词 Diversified rotation Nitrous oxide Ammonia oxidation Denitrification Microbial network complexity
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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Scalable,binder-free,ultrathin,and outdoor stable passive cooling paints engineered by cellulose-weaved topological scattering network 认领 引用 被引量：1
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作者 Ting Yang Siying Guo +5 位作者 Xin Zhao Bianjing Sun Ruey Shan Chen Sinyee Gan Jonathan Woon-Chung Wong Chenyang Cai 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第4期584-594,I0015,共11页
Paints with passive daytime radiative cooling capability hold significant promise for energy-efficient buildings owing to their ease of processing.However,conventional radiative cooling paints require substantial thic... Paints with passive daytime radiative cooling capability hold significant promise for energy-efficient buildings owing to their ease of processing.However,conventional radiative cooling paints require substantial thickness to achieve effective outdoor cooling and must be combined with binders to enhance adhesion to the substrate.Meanwhile,their long-term outdoor durability remains poor.In this work,we proposed a scattering network-enhanced ultrathin photonic cooling paint(thickness of 78μm)fabricated without traditional binders through a universal,scalable solution-assembly strategy under a low-carbon production process.Cellulose nanofiber and cellulose nanocrystal were employed to wrap and entangle TiO2,forming a topological scattering network that prevents near-field coupling.Together with hierarchical pores,this structure enables high solar reflectance(96.4%)and an infrared emissivity of 0.94.This novel paint achieves temperature reduction of~5.6 and 3.8℃ under low and high-humidity conditions of midday,respectively,while maintaining long-term outdoor stability.Importantly,the cellulose-weaved topological scattering network can also be engineered with alternative photonic cooling pigments(Al2O3,SiO2,BaSO4,and mica),demonstrating its universality.In addition,life cycle assessment reveals that the obtained cooling paint offers very low carbon emissions and minimal environmental impacts.This work provides an economically viable and environmentally sustainable alternative to existing passive cooling materials. 展开更多
关键词 Weaved network Radiative cooling Cellulose Ultrathin structure
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Intrusion Detection Systems in Industrial Control Systems:Landscape,Challenges and Opportunities 认领 引用 被引量：1
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作者 Tong Wu Dawei Zhou +1 位作者 Qingyu Ou Fang Luo 《Computers, Materials & Continua》 SCIE EI 2026年第3期162-200,共39页
The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has dri... The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations. 展开更多
关键词 Industrial control system industrial control system network security intrusion detection cyberspace security ICS network network security
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Environmental DNA reveals multi-trophic biodiversity patterns and community assembly mechanisms across contrasting river systems on the Qinghai-Xizang Plateau 认领 引用 被引量：1
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作者 Qi Lu Si-Yu Zhang +2 位作者 Jianqing Du Yongqin Liu Meng Yao 《Journal of Systematics and Evolution》 SCIE CSCD 2026年第1期138-155,共18页
The Hindu Kush Himalayan(HKH)region sustains the headwaters of major Asian rivers and harbors unique alpine biodiversity,and yet,it is highly sensitive to climate change.Biodiversity organization and community assembl... The Hindu Kush Himalayan(HKH)region sustains the headwaters of major Asian rivers and harbors unique alpine biodiversity,and yet,it is highly sensitive to climate change.Biodiversity organization and community assembly processes across trophic levels within its river ecosystems remain poorly understood.Here,we used multi-marker environmental DNA(eDNA)metabarcoding targeting four biological groups(cyanobacteria,diatoms,invertebrates,and vertebrates)to assess multi-trophic biodiversity patterns in two alpine rivers of the region:the Yellow River source region(YR)and the middle-upper Nujiang River(NJ).These two systems differ markedly in geography,hydroclimate,vegetation,and human disturbance intensity.We identified 1695 operational taxonomic units and revealed pronounced differences in biodiversity pattern and community composition between rivers.Phototrophs and invertebrates showed higherαdiversity in NJ,whereas vertebrates were richer in YR.βdiversity was mainly driven by species turnover in both rivers,with a stronger distance-decay pattern in YR.Correlations between environmental variables andαandβdiversity varied across groups and rivers,with geographic and climatic factors exerting stronger effects in NJ.iCAMP and pNST analyses revealed that stochastic processes dominated community assembly in both rivers,whereas deterministic processes were relatively stronger in YR compared to NJ.Accordingly,co-occurrence networks revealed cohesive communities in YR but more modular ones in NJ,indicating contrasting ecological stability regimes.Overall,our study provides an integrated,multi-trophic perspective on how environmental gradients shape riverine biodiversity and ecological interactions,informing adaptive conservation strategies under accelerating environmental change in the HKH region. 展开更多
关键词 alpine freshwater biodiversity community assembly ecological networks hydroclimatic gradients
The Agentic-AI Core:An AI-Empowered,Mission-Oriented Core Network for Next-Generation Mobile Telecommunications 认领 引用 被引量：1
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作者 Xu Li Weisen Shi +3 位作者 Hang Zhang Chenghui Peng Shaoyun Wu Wen Tong 《Engineering》 SCIE EI CSCD 2026年第1期104-119,共16页
While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easi... While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easily be imagined that,without proper design,the enrichment of 6G will further increase system complexity.To address this issue,we propose the Agentic-AI Core(A-Core),an artificial intelligence(AI)-empowered,mission-oriented core network architecture for next-generation mobile telecommunications.In A-Core,network capabilities can be added and updated on the fly and further programmed into missions for enabling and offering diverse services to customers.These missions are created and executed by autonomous network agents according to the customer's intent,which may be expressed in natural language.The agents resolve intents from customers into workflows of network capabilities by leveraging a large-scale network AI model and follow the workflows to execute the mission.As an open,agile system architecture,A-Core holds promise for accelerating innovation and greatly reducing standard release times.The advantages of A-Core are demonstrated through two use cases. 展开更多
关键词 Sixth generation Core network Generative artificial intelligence Artificial intelligence agent
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Collision risk assessment for constellation satellites based on a space debris environment topological network model 认领 引用 被引量：1
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作者 Yurun YUAN Jingrui ZHANG +2 位作者 Keying YANG Lincheng LI Hao WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期472-484,共13页
In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This s... In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites. 展开更多
关键词 Collision probability Computing resource Constellation Space debris Topological network model
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用 被引量：1
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling 认领 引用 被引量：1
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作者 Lei Liu Wei Li +7 位作者 Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong Jia-Gen Hou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1754-1772,共19页
Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we... Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning,which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data.Specifically,we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives.Then,during simulation,to enhance the capability of the network model for finely characterizing complex heterogeneous models,cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features.Additionally,through systematic feature map visualization analysis,we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction,intuitively demonstrating the functional mechanisms of each module.Finally,systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method.The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators.Quantitative comparisons reveal remarkable performance of the method,achieving low Wasserstein distance(0.09),Kernel Inception Distance(0.0017)and Kernel Maximum Mean Discrepancy(0.21).These findings further confirm the high realism of the generated realizations regarding pattern features.This study offers a reliable and practical method for geological reservoir modeling,thereby advancing quantitative,precise geological research with broad application prospects. 展开更多
关键词 Sedimentary facies models Attention-guided generative adversarial network Interpretable framework Sedimentary patterns Multi-condition modeling
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Reconstructing flow fields from sparse measurements using a convolutional autoencoder integrated with an Informer model 认领 引用 被引量：1
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作者 Yulu Liu Jiakun Long +2 位作者 Bofu Wang Tienchong Chang Xiang Qiu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期116-130,共15页
This paper explores the use of sparse time-series data from flow systems,acquired through sensors or other means,to predict flow fields using deep learning techniques.This area of research holds substantial scientific... This paper explores the use of sparse time-series data from flow systems,acquired through sensors or other means,to predict flow fields using deep learning techniques.This area of research holds substantial scientific significance and practical application value.The time-series data measured from different points typically contain spatial correlation and temporal features,which,when utilized effectively,can contribute to reconstructing flow fields.In this study,a convolutional autoencoder is applied to reduce the dimensionality of the flow field.Subsequently,an Informer neural network and a convolutional neural network are employed to extract low-dimensional representations of the flow field from the measurement data.A specially designed loss function bridges these latent features to establish a mapping between measurement point sequences and flow fields.The hybrid model is validated using data from both numerical simulations and experimental measurements.Results demonstrate that this method effectively predicts velocity and pressure fields from sparse data,showcasing its potential for practical flow field reconstruction tasks. 展开更多
关键词 Flow around cylinder Flow prediction Neural network
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