Bio-inspired visual systems have garnered significant attention in robotics owing to their energy efficiency,rapid dynamic response,and environmental adaptability.Among these,event cameras-bio-inspired sensors that as...Bio-inspired visual systems have garnered significant attention in robotics owing to their energy efficiency,rapid dynamic response,and environmental adaptability.Among these,event cameras-bio-inspired sensors that asynchronously report pixel-level brightness changes called’events’,stand out because of their ability to capture dynamic changes with minimal energy consumption,making them suitable for challenging conditions,such as low light or high-speed motion.However,current mapping and localization methods for event cameras depend primarily on point and line features,which struggle in sparse or low-feature environments and are unsuitable for static or slow-motion scenarios.We addressed these challenges by proposing a bio-inspired vision mapping and localization method using active LED markers(ALMs)combined with reprojection error optimization and asynchronous Kalman fusion.Our approach replaces traditional features with ALMs,thereby enabling accurate tracking under dynamic and low-feature conditions.The global mapping accuracy significantly improved by minimizing the reprojection error,with corner errors reduced from 16.8 cm to 3.1 cm after 400 iterations.The asynchronous Kalman fusion of multiple camera pose estimations from ALMs ensures precise localization with a high temporal efficiency.This method achieved a mean translation error of 0.078 m and a rotational error of 5.411°while evaluating dynamic motion.In addition,the method supported an output rate of 4.5 kHz while maintaining high localization accuracy in UAV spiral flight experiments.These results demonstrate the potential of the proposed approach for real-time robot localization in challenging environments.展开更多
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary...Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.展开更多
In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is di...In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is difficult to navigate deep regions of complex organ structures.This work proposes a kind of bio-inspired magnetic soft robot(BMSR)to address these challenges.The design of the BMSRs is inspired by the rolling motion of the golden wheel spider.Two six-degree-of-freedom(6-DOF)robotic arms are used,where one arm is responsible for real-time manipulation of the BMSRs,and the other is dedicated to monitoring their status.Under the actuation of an external rotating magnetic field,the BMSRs can flexibly climb on inclined surfaces at any angle,involving the inverted surface.Through the powerful output force,the BMSRs can overcome the mobility barrier induced by different human organs,including mucus,folds,and height differences of up to 8 cm.Such an exceptional mobility enables the BMSRs to deliver drugs in the targeted complex GI environment.Moreover,in combination with an endoscope,it provides real-time visual feedback for precise navigation.In vitro animal experiments validate the feasibility of BMSRs,paving a way for their usage in minimally invasive GI treatment.This work advances the potential applications of magnetic soft robots in the biomedical field.展开更多
Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance ...Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance and scalability,as current trends require the distribution of computation across network nodes/points.In this paper,we survey a large number of mapping and scheduling techniques designed for NoC architectures.This time,we concentrated on 3D systems.We take a systematic literature review approach to analyze existing methods across static,dynamic,hybrid,and machine-learning-based approaches,alongside preliminary AI-based dynamic models in recent works.We classify them into several main aspects covering power-aware mapping,fault tolerance,load-balancing,and adaptive for dynamic workloads.Also,we assess the efficacy of each method against performance parameters,such as latency,throughput,response time,and error rate.Key challenges,including energy efficiency,real-time adaptability,and reinforcement learning integration,are highlighted as well.To the best of our knowledge,this is one of the recent reviews that identifies both traditional and AI-based algorithms for mapping over a modern NoC,and opens research challenges.Finally,we provide directions for future work toward improved adaptability and scalability via lightweight learned models and hierarchical mapping frameworks.展开更多
Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined ...Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.展开更多
Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods...Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequ...Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Objective:To retrospectively evaluate the diagnostic efficacy of traditional MRI and T2 Mapping quantitative imaging technology for knee joint cartilage injury,clarify the differences in diagnostic value of the two im...Objective:To retrospectively evaluate the diagnostic efficacy of traditional MRI and T2 Mapping quantitative imaging technology for knee joint cartilage injury,clarify the differences in diagnostic value of the two imaging methods in different injury grades and different cartilage subregions,and provide evidence-based basis for the accurate diagnosis of clinical cartilage injury.Methods:Clinical and imaging data of 286 patients with knee joint lesions admitted to the Affiliated Hospital of Xiangtan Medicine and Health Vocational College from January 2020 to June 2023 were collected retrospectively.All patients underwent both traditional MRI sequences and T2 Mapping sequences.The knee joint cartilage was divided into 14 subregions.Two senior radiologists independently diagnosed the images of the two imaging technologies using a blind method and recorded the cartilage injury grades.The sensitivity,specificity,accuracy,positive predictive value,negative predictive value,and area under the receiver operating characteristic curve(AUC)of the two technologies for diagnosing cartilage injury were calculated and compared,and the differences in their diagnostic efficacy in different injury grades and different subregions were analyzed.Results:A total of 4004 cartilage subregions from 286 patients were included in the analysis,including 1836 injured subregions and 2168 normal subregions.The overall sensitivity(89.7%),accuracy(91.2%),and AUC(0.946)of T2 Mapping quantitative imaging for diagnosing cartilage injury were significantly higher than those of traditional MRI(76.3%,82.5%,and 0.852 respectively),with statistically significant differences(p<0.001);there was no significant difference in specificity between the two(93.5%vs 90.8%,p=0.062).Subgroup analysis showed that T2 Mapping had the most significant diagnostic advantage in early cartilage injury(Grade 1),with sensitivity(78.5%)33.2%higher than that of traditional MRI(45.3%)(p<0.001).Conclusion:The diagnostic efficacy of T2 Mapping quantitative imaging for knee joint cartilage injury is significantly superior to that of traditional MRI,especially in the detection of early cartilage injury and accurate evaluation of weight-bearing area injury.Data verify its clinical applicability and reliability.It can be used as an important supplementary method to traditional MRI,and is recommended for the early diagnosis,grading evaluation,and clinical follow-up of cartilage injury.展开更多
Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC rec...Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.展开更多
User identity linkage(UIL)across online social networks seeks to match accounts belonging to the same real-world individual.This cross-platformmapping enables accurate user modeling but also raises serious privacy ris...User identity linkage(UIL)across online social networks seeks to match accounts belonging to the same real-world individual.This cross-platformmapping enables accurate user modeling but also raises serious privacy risks.Over the past decade,the research community has developed a wide range of UIL methods,from structural embeddings tomultimodal fusion architectures.However,corresponding adversarial and defensive approaches remain fragmented and comparatively understudied.In this survey,we provide a unified overview of both mapping and antimappingmethods for UIL.We categorize representativemappingmodels by learning paradigmand datamodality,and systematically compare them with emerging countermeasures including adversarial injection,structural perturbation,and identity obfuscation.To bridge these two threads,we introduce amodality-oriented taxonomy and a formal gametheoretic framing that casts cross-network mapping as a contest between mappers and anti-mappers.This framing allows us to construct a cross-modality dependency matrix,which reveals structural information as themost contested signal,identifies node injection as the most robust defensive strategy,and points to multimodal integration as a promising direction.Our survey underscores the need for balanced,privacy-preserving identity inference and provides a foundation for future research on the adversarial dynamics of social identity mapping and defense.展开更多
Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv...Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.展开更多
基金Supported by Beijing Natural Science Foundation(Grant No.L231004)Young Elite Scientists Sponsorship Program by CAST(Grant No.2022QNRC001)+2 种基金Fundamental Research Funds for the Central Universities(Grant No.2025JBMC039)National Key Research and Development Program(Grant No.2022YFC2805200)National Natural Science Foundation of China(Grant No.52371338).
文摘Bio-inspired visual systems have garnered significant attention in robotics owing to their energy efficiency,rapid dynamic response,and environmental adaptability.Among these,event cameras-bio-inspired sensors that asynchronously report pixel-level brightness changes called’events’,stand out because of their ability to capture dynamic changes with minimal energy consumption,making them suitable for challenging conditions,such as low light or high-speed motion.However,current mapping and localization methods for event cameras depend primarily on point and line features,which struggle in sparse or low-feature environments and are unsuitable for static or slow-motion scenarios.We addressed these challenges by proposing a bio-inspired vision mapping and localization method using active LED markers(ALMs)combined with reprojection error optimization and asynchronous Kalman fusion.Our approach replaces traditional features with ALMs,thereby enabling accurate tracking under dynamic and low-feature conditions.The global mapping accuracy significantly improved by minimizing the reprojection error,with corner errors reduced from 16.8 cm to 3.1 cm after 400 iterations.The asynchronous Kalman fusion of multiple camera pose estimations from ALMs ensures precise localization with a high temporal efficiency.This method achieved a mean translation error of 0.078 m and a rotational error of 5.411°while evaluating dynamic motion.In addition,the method supported an output rate of 4.5 kHz while maintaining high localization accuracy in UAV spiral flight experiments.These results demonstrate the potential of the proposed approach for real-time robot localization in challenging environments.
基金the Natural Science Foundation of China(Project for Young Scientists:Grant No.52105010,Regular Project:Grant No.62173096)Natural Science Foundationof Guangdong Province(Regular Project:Grant No.2025A1515012124,Grant No.2022A1515010327)Guangdong-Hong Kong-Macao Key Laboratory of Multi-scaleInformation Fusion and Collaborative Optimization Control Manufacturing Process.
文摘Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.
基金supported in part by the National Natural Science Foundation of China under grant 52175556the Macao Science and Technology Development Fund under grant 0004/2022/AKP,0102/2022/A2,and 0078/2023/RIB3+1 种基金the Research Committee of the University of Macao under grants MYRG2022-00068-FST and MYRG-CRG202200004-FST-ICIthe Guangdong Basic and Applied Basic Research Foundation under grant 2023A1515011178。
文摘In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is difficult to navigate deep regions of complex organ structures.This work proposes a kind of bio-inspired magnetic soft robot(BMSR)to address these challenges.The design of the BMSRs is inspired by the rolling motion of the golden wheel spider.Two six-degree-of-freedom(6-DOF)robotic arms are used,where one arm is responsible for real-time manipulation of the BMSRs,and the other is dedicated to monitoring their status.Under the actuation of an external rotating magnetic field,the BMSRs can flexibly climb on inclined surfaces at any angle,involving the inverted surface.Through the powerful output force,the BMSRs can overcome the mobility barrier induced by different human organs,including mucus,folds,and height differences of up to 8 cm.Such an exceptional mobility enables the BMSRs to deliver drugs in the targeted complex GI environment.Moreover,in combination with an endoscope,it provides real-time visual feedback for precise navigation.In vitro animal experiments validate the feasibility of BMSRs,paving a way for their usage in minimally invasive GI treatment.This work advances the potential applications of magnetic soft robots in the biomedical field.
文摘Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance and scalability,as current trends require the distribution of computation across network nodes/points.In this paper,we survey a large number of mapping and scheduling techniques designed for NoC architectures.This time,we concentrated on 3D systems.We take a systematic literature review approach to analyze existing methods across static,dynamic,hybrid,and machine-learning-based approaches,alongside preliminary AI-based dynamic models in recent works.We classify them into several main aspects covering power-aware mapping,fault tolerance,load-balancing,and adaptive for dynamic workloads.Also,we assess the efficacy of each method against performance parameters,such as latency,throughput,response time,and error rate.Key challenges,including energy efficiency,real-time adaptability,and reinforcement learning integration,are highlighted as well.To the best of our knowledge,this is one of the recent reviews that identifies both traditional and AI-based algorithms for mapping over a modern NoC,and opens research challenges.Finally,we provide directions for future work toward improved adaptability and scalability via lightweight learned models and hierarchical mapping frameworks.
基金Nguyen Tat Thanh University,Ho Chi Minh City,Vietnam for supporting this study。
文摘Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.
基金National Key Scientific Instrument and Equipment Development Project under Grant No.61827801the open research fund of State Key Laboratory of Integrated Services Networks,No.ISN22-11+1 种基金Natural Science Foundation of Jiangsu Province,No.BK20211182open research fund of National Mobile Communications Research Laboratory,Southeast University,No.2022D04。
文摘Spectrum map construction,which is crucial in cognitive radio(CR)system,visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation.Traditional reconstruction methods are generally for twodimensional(2D)spectrum map and driven by abundant sampling data.In this paper,we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional(3D)spectrum map under multi-radiation source scenarios.We firstly design a maximum and minimum path loss difference(MMPLD)clustering algorithm to detect the number of radiation sources in a 3D space.Then,we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm.Considering the variation of electromagnetic environment,we self-learn the path loss(PL)model based on the sampling data.Finally,the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources.Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment,but also achieves high spectrum construction accuracy even when the sampling rate is very low.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
基金supported by the National Key Research and Development Plan of China(2021YFD2200202)the Key Research and Development Project of Jiangsu Province,China(BE2021366).
文摘Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
基金Application Research of MRI Physiological Quantitative Imaging Technology in the Diagnosis of Cartilage Injury(Project No.:RCYJ2021-04)。
文摘Objective:To retrospectively evaluate the diagnostic efficacy of traditional MRI and T2 Mapping quantitative imaging technology for knee joint cartilage injury,clarify the differences in diagnostic value of the two imaging methods in different injury grades and different cartilage subregions,and provide evidence-based basis for the accurate diagnosis of clinical cartilage injury.Methods:Clinical and imaging data of 286 patients with knee joint lesions admitted to the Affiliated Hospital of Xiangtan Medicine and Health Vocational College from January 2020 to June 2023 were collected retrospectively.All patients underwent both traditional MRI sequences and T2 Mapping sequences.The knee joint cartilage was divided into 14 subregions.Two senior radiologists independently diagnosed the images of the two imaging technologies using a blind method and recorded the cartilage injury grades.The sensitivity,specificity,accuracy,positive predictive value,negative predictive value,and area under the receiver operating characteristic curve(AUC)of the two technologies for diagnosing cartilage injury were calculated and compared,and the differences in their diagnostic efficacy in different injury grades and different subregions were analyzed.Results:A total of 4004 cartilage subregions from 286 patients were included in the analysis,including 1836 injured subregions and 2168 normal subregions.The overall sensitivity(89.7%),accuracy(91.2%),and AUC(0.946)of T2 Mapping quantitative imaging for diagnosing cartilage injury were significantly higher than those of traditional MRI(76.3%,82.5%,and 0.852 respectively),with statistically significant differences(p<0.001);there was no significant difference in specificity between the two(93.5%vs 90.8%,p=0.062).Subgroup analysis showed that T2 Mapping had the most significant diagnostic advantage in early cartilage injury(Grade 1),with sensitivity(78.5%)33.2%higher than that of traditional MRI(45.3%)(p<0.001).Conclusion:The diagnostic efficacy of T2 Mapping quantitative imaging for knee joint cartilage injury is significantly superior to that of traditional MRI,especially in the detection of early cartilage injury and accurate evaluation of weight-bearing area injury.Data verify its clinical applicability and reliability.It can be used as an important supplementary method to traditional MRI,and is recommended for the early diagnosis,grading evaluation,and clinical follow-up of cartilage injury.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077242 and 42171407)the Graduate Innovation Fund of Jilin University.
文摘Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.
基金funded by the National Key R&D Program of China under Grant(No.2022YFB3102901)National Natural Science Foundation of China(Nos.62072115,62102094)Shanghai Science and Technology Innovation Action Plan Project(No.22510713600).
文摘User identity linkage(UIL)across online social networks seeks to match accounts belonging to the same real-world individual.This cross-platformmapping enables accurate user modeling but also raises serious privacy risks.Over the past decade,the research community has developed a wide range of UIL methods,from structural embeddings tomultimodal fusion architectures.However,corresponding adversarial and defensive approaches remain fragmented and comparatively understudied.In this survey,we provide a unified overview of both mapping and antimappingmethods for UIL.We categorize representativemappingmodels by learning paradigmand datamodality,and systematically compare them with emerging countermeasures including adversarial injection,structural perturbation,and identity obfuscation.To bridge these two threads,we introduce amodality-oriented taxonomy and a formal gametheoretic framing that casts cross-network mapping as a contest between mappers and anti-mappers.This framing allows us to construct a cross-modality dependency matrix,which reveals structural information as themost contested signal,identifies node injection as the most robust defensive strategy,and points to multimodal integration as a promising direction.Our survey underscores the need for balanced,privacy-preserving identity inference and provides a foundation for future research on the adversarial dynamics of social identity mapping and defense.
基金Under the auspices of the National Natural Science Foundation of China(No.42101342,U2243205)the Third Comprehensive Scientific Expedition to Xinjiang(No.2021XJKK1403)。
文摘Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.
