Among various architectures of polymers,end-group-free rings have attracted growing interests due to their distinct physicochemical performances over the linear counterparts which are exemplified by reduced hydrodynam...Among various architectures of polymers,end-group-free rings have attracted growing interests due to their distinct physicochemical performances over the linear counterparts which are exemplified by reduced hydrodynamic size and slower degradation.It is key to develop facile methods to large-scale synthesis of polymer rings with tunable compositions and microstructures.Recent progresses in large-scale synthesis of polymer rings against single-chain dynamic nanoparticles,and the example applications in synchronous enhancing toughness and strength of polymer nanocomposites are summarized.Once there is the breakthrough in rational design and effective large-scale synthesis of polymer rings and their functional derivatives,a family of cyclic functional hybrids would be available,thus providing a new paradigm in developing polymer science and engineering.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
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
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
This study presents a reflective bibliometric review of 1457 peer-reviewed articles published in the Journal of Psychology in Africa(2008-2024,17 years),using a Meta-Editorial Mapping Framework(MEMF)analysis.The MEMF ...This study presents a reflective bibliometric review of 1457 peer-reviewed articles published in the Journal of Psychology in Africa(2008-2024,17 years),using a Meta-Editorial Mapping Framework(MEMF)analysis.The MEMF integrates citation metrics,keyword novelty ratios,TF-IDF weighting,and cluster-based topic modeling to trace long-term thematic trends and editorial evolution.Findings reveal sustained attention to foundational domains such as mental health,education,and identity,alongside a gradual integration of emergent themes including digital well-being,organizational behavior,and post-pandemic adaptation.Articles with moderate topical novelty(40%-60% new keywords)achieved the highest citation and usage metrics,suggesting that integrative innovation enhances scholarly impact.Clustering analyses indicate that the journal’s content forms overlapping conceptual domains rather than isolated silos.These insights contribute to editorial strategy,authorial positioning,and the future design of regional academic platforms.Moreover,the findings provide evidence supporting the use of the MEMF as a replicable tool for meta-editorial analysis across disciplinary and geographic boundaries.展开更多
Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mec...Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.展开更多
In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense s...In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and tile motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify tim feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field （CRF） inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate tile dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the ex...A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.展开更多
Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale buildin...Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.展开更多
GPS-RTK technology in topographic mapping has a relatively large advantage, this paper studies how to use the technology to carry out large-scale topographic mapping work, research the use of the method of precautions...GPS-RTK technology in topographic mapping has a relatively large advantage, this paper studies how to use the technology to carry out large-scale topographic mapping work, research the use of the method of precautions, surveying and mapping work methods, combined with examples to discuss the specific mapping process, to help surveying and mapping personnel to strengthen the quality control of surveying and mapping.展开更多
冶金尘泥的转底炉处理工艺是目前钢铁行业采用的主要处置工艺,但在实际生产过程中经常出现还原焙烧不均匀的问题。利用微观扫描电子显微镜(scanning electron microscopy,SEM)分析结合宏观Maps统计分析,对冶金尘泥还原焙烧的不均匀性进...冶金尘泥的转底炉处理工艺是目前钢铁行业采用的主要处置工艺,但在实际生产过程中经常出现还原焙烧不均匀的问题。利用微观扫描电子显微镜(scanning electron microscopy,SEM)分析结合宏观Maps统计分析,对冶金尘泥还原焙烧的不均匀性进行详细的可视化、数据化分析。研究结果表明,冶金尘泥在焙烧温度为1250℃、焙烧时间为15 min的条件下,熟球金属化率达到89.04%、脱锌率达到81.66%、抗压强度达到3.03 kN,熟球金属化率和脱锌率会随着焙烧温度提高和焙烧时间延长而进一步提高,但熟球抗压强度在焙烧时间过长时反而逐渐降低;熟球Maps统计分析表明,提高焙烧温度更有利于提高熟球外圈和下部的还原程度,而延长焙烧时间也更有利于提高熟球下部还原程度,但对熟球内部和外圈还原程度的提升作用比较相似;同时,提高焙烧温度也更有利于提升熟球下部的致密化程度,降低熟球上、下孔隙结构的不均匀性,进而显著提高熟球整体抗压强度;但焙烧时间过长会导致熟球中小孔隙融合为大孔隙,反而降低熟球抗压强度。此外,熟球中硅酸盐(渣相)和浮氏体(FexO)更容易破裂,而金属铁(Fe)可延缓裂纹蔓延,因而,适当提高熟球金属化率、降低硅酸盐(渣相)含量也有利于提高其抗压强度。基于Maps统计分析探究了冶金尘泥还原焙烧过程中物相及孔隙的变化规律,分析结果可以为转底炉工艺处理冶金尘泥的生产实践提供指导和建议。展开更多
基金Supported by the National Natural Science Foundation of China(Nos.52293472,22473096 and 22471164)。
文摘Among various architectures of polymers,end-group-free rings have attracted growing interests due to their distinct physicochemical performances over the linear counterparts which are exemplified by reduced hydrodynamic size and slower degradation.It is key to develop facile methods to large-scale synthesis of polymer rings with tunable compositions and microstructures.Recent progresses in large-scale synthesis of polymer rings against single-chain dynamic nanoparticles,and the example applications in synchronous enhancing toughness and strength of polymer nanocomposites are summarized.Once there is the breakthrough in rational design and effective large-scale synthesis of polymer rings and their functional derivatives,a family of cyclic functional hybrids would be available,thus providing a new paradigm in developing polymer science and engineering.
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.
文摘This study presents a reflective bibliometric review of 1457 peer-reviewed articles published in the Journal of Psychology in Africa(2008-2024,17 years),using a Meta-Editorial Mapping Framework(MEMF)analysis.The MEMF integrates citation metrics,keyword novelty ratios,TF-IDF weighting,and cluster-based topic modeling to trace long-term thematic trends and editorial evolution.Findings reveal sustained attention to foundational domains such as mental health,education,and identity,alongside a gradual integration of emergent themes including digital well-being,organizational behavior,and post-pandemic adaptation.Articles with moderate topical novelty(40%-60% new keywords)achieved the highest citation and usage metrics,suggesting that integrative innovation enhances scholarly impact.Clustering analyses indicate that the journal’s content forms overlapping conceptual domains rather than isolated silos.These insights contribute to editorial strategy,authorial positioning,and the future design of regional academic platforms.Moreover,the findings provide evidence supporting the use of the MEMF as a replicable tool for meta-editorial analysis across disciplinary and geographic boundaries.
基金funded by the Norwegian Research Council(NFR project 302701 Climate Smart Forestry Norway).
文摘Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.
基金supported by National Natural Science Foundation of China(Nos.NSFC 61473042 and 61105092)Beijing Higher Education Young Elite Teacher Project(No.YETP1215)
文摘In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and tile motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify tim feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field （CRF） inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate tile dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
基金Sponsored by the Funds for Creative Research Groups of China(Grant No. 60821001)National Natural Science Foundation of China(Grant No.60973108 and 60902050)973 Project of China (Grant No.2007CB310703)
文摘A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.
基金supported by the National Science Foundation[grant numbers 1854502 and 1855902]Publication was made possible in part by support from the HKU Libraries Open Access Author Fund sponsored by the HKU Libraries.USDA is an equal opportunity provider and employer.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S.Department of Agriculture.
文摘Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.
文摘GPS-RTK technology in topographic mapping has a relatively large advantage, this paper studies how to use the technology to carry out large-scale topographic mapping work, research the use of the method of precautions, surveying and mapping work methods, combined with examples to discuss the specific mapping process, to help surveying and mapping personnel to strengthen the quality control of surveying and mapping.
文摘冶金尘泥的转底炉处理工艺是目前钢铁行业采用的主要处置工艺,但在实际生产过程中经常出现还原焙烧不均匀的问题。利用微观扫描电子显微镜(scanning electron microscopy,SEM)分析结合宏观Maps统计分析,对冶金尘泥还原焙烧的不均匀性进行详细的可视化、数据化分析。研究结果表明,冶金尘泥在焙烧温度为1250℃、焙烧时间为15 min的条件下,熟球金属化率达到89.04%、脱锌率达到81.66%、抗压强度达到3.03 kN,熟球金属化率和脱锌率会随着焙烧温度提高和焙烧时间延长而进一步提高,但熟球抗压强度在焙烧时间过长时反而逐渐降低;熟球Maps统计分析表明,提高焙烧温度更有利于提高熟球外圈和下部的还原程度,而延长焙烧时间也更有利于提高熟球下部还原程度,但对熟球内部和外圈还原程度的提升作用比较相似;同时,提高焙烧温度也更有利于提升熟球下部的致密化程度,降低熟球上、下孔隙结构的不均匀性,进而显著提高熟球整体抗压强度;但焙烧时间过长会导致熟球中小孔隙融合为大孔隙,反而降低熟球抗压强度。此外,熟球中硅酸盐(渣相)和浮氏体(FexO)更容易破裂,而金属铁(Fe)可延缓裂纹蔓延,因而,适当提高熟球金属化率、降低硅酸盐(渣相)含量也有利于提高其抗压强度。基于Maps统计分析探究了冶金尘泥还原焙烧过程中物相及孔隙的变化规律,分析结果可以为转底炉工艺处理冶金尘泥的生产实践提供指导和建议。
