Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community asse...Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community assembly in virgin and aging MP plastispheres across different habitats is poorly understood.This study aims to assess the variations in bacterial community assembly across different niches and habitats with an in situ ex-periment,in which constructed forest wetland(FW),natural lake wetland(LW),and lotus pond wetland(LP)were habitats,and plastispheres of virgin and aging low-density polyethylene(LDPE)MPs,as well as surround-ing wetland soils were niches.Significant niche-related differences in bacterial communities were observed,with lower diversity and enrichment of potential plastic-degrading bacteria in the plastisphere than in the soil bacterial communities.Furthermore,habitat-related differences exerted a more pronounced influence on the beta-diversity patterns of the bacterial communities.The linear regression analyses indicated that the local species pool con-tributed more to bacterial community assembly in the LW wetland,whereas the relative abundance of species was the primary factor in the LP wetland.The null model analysis indicated that plastisphere bacterial communi-ties were predominantly driven by the stochastic process,with a more deterministic assembly observed in the LP wetland and soil bacterial communities.Additionally,the primary ecological process shaping plastisphere com-munities shifted from drift in the virgin LDPE to homogenising dispersal in the aging LDPE.This study provides new insights into the fate and ecological impacts of MPs in wetlands,thereby facilitating the effective regulations of plastic pollution.展开更多
Gold nanoclusters(AuNCs)are ultrasmall(<2 nm)aggregates of gold atoms that exhibit discrete electronic states,size-dependent photoluminescence,and exceptional biocompatibility,making them ideal candidates for thera...Gold nanoclusters(AuNCs)are ultrasmall(<2 nm)aggregates of gold atoms that exhibit discrete electronic states,size-dependent photoluminescence,and exceptional biocompatibility,making them ideal candidates for theranostic applications.Their tunable surface chemistry enables targeted delivery,while strong near-infrared emission and environmental responsiveness allow for sensitive detection and deep-tissue imaging.Recent advances have revealed that controlled assembly of AuNCs into higher-order architectures-guided by biological scaffolds such as nucleic acids,peptides,and proteins-can markedly enhance their optical and electronic properties through aggregation-induced emission(AiE)and stabilization of surface ligands.This review summarizes recent progress in the design and biomedical applications of AuNC assemblies generated using biomolecules as structure-directing scaffolds.Covalent and noncovalent interactions with biomolecules enable the formation of well-defined one-,two-,and three-dimensional structures with tunable morphologies and sizes.These assemblies display distinctive photophysical behaviors that have been exploited for biosensing,bioimaging,and therapeutic applications in both cellular and in vivo models.Compared with individual AuNCs,assembled systems offer improved uptake,prolonged circulation,and efficient clearance,while protecting labile cargos such as nucleic acids and proteins.Moreover,their ordered and defined architectures can be engineered for controlled drug release and synergistic photo-or radiotherapeutic effects.Despite these advances,fundamental understanding of how structural organization governs photophysical responses remains limited.Elucidating parameters such as intercluster spacing and loading density will be essential for optimizing performance.Overall,biologically guided AuNC assemblies represent a powerful platform for multifunctional biosensing and therapy,bridging nanoscale design with biological function.展开更多
With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the ch...With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the characteristics and tendency of China's automatic driving technology standards present the situation of high policy relevance coexisting with low normative binding,professionalism coexist with barriers,forefront coexist with ambiguity.Therefore,challenges are presented both theoretically and practically on the determination of criminal responsibility based on automatic driving technology standard..In this regard,the misunderstanding should be clarified in theory:The legal order under the automatic driving technology standard has constitutionality and systematic,and there is a balance between the frontier of automatic driving technology development and the lagging of criminal law.The automatic driving technology risk level system should be built to clarify the boundary of the effectiveness of criminal law norms,seeking fora breakthrough in the application of the establishment of a comprehensive judgment system of the risks and accidents and the system of evidence to prove the system,which clarifies the determination of criminal responsibility under the automatic driving technology standard.This essay hopes to pursue breakthroughs in the application-to establish a comprehensive judgment system of risks and accidents as well as an evidence proof system,so as to clarify the determination of criminal responsibility under automatic driving technology standards.展开更多
Targeted protein degradation(TPD)is an innovative strategy for selectively eliminating pathogenic proteins,enabling precise degradation of once-undruggable targets in cancer therapy.However,current TPD molecules are o...Targeted protein degradation(TPD)is an innovative strategy for selectively eliminating pathogenic proteins,enabling precise degradation of once-undruggable targets in cancer therapy.However,current TPD molecules are often limited by poor tumor targeting and the need for high doses.To overcome these limitations,assembly/disassembly-based TPD systems have been proposed to effectively degrade proteins of interest and enhance therapeutic efficacy.Herein,we summarize the recent advances in such TPD systems and categorize the strategies employed,including nanosphere morphology of assembled TPD systems,nanofiber morphology of assembled TPD systems,carrier-mediated TPD release systems,and stimulus-induced free TPD molecule formation nanosystems.Finally,we outline future directions and identify the remaining challenges in assembly/disassembly-based TPD systems.展开更多
Ecological floating bed is an important biological remediation method for water pollution control.During the removal of excess nutrients and pollutants,changes in environmental factors affect the characteristics of mi...Ecological floating bed is an important biological remediation method for water pollution control.During the removal of excess nutrients and pollutants,changes in environmental factors affect the characteristics of microorganisms in aquatic ecosystems.To understand the influences of ecological floating beds on size-fractionated microorganisms,we investigated the community assembly and nitrogen metabolic characteristics of three size-fractionated microorganism groups in the ecological floating bed area,using 18S rDNA,16S rDNA metabarcoding,and metagenomic sequencing techniques.Firstly,we discovered substantial differences between size-fractionated groups in the diversity and compositions of both microeukaryotic and bacterial communities,as well as the influences of floating beds on specific groups.The floating beds appeared to provide more habitats for heterotrophs and symbiotes while potentially inhibiting the growth of certain phytoplankton(cyanobacteria).Secondly,we observed that microeukaryotic and bacterial communities were predominantly influenced by stochastic and deterministic processes,respectively,and they both exhibited distinct patterns across different size-fractionated groups.Notably,microeukaryotic community assembly demonstrated a greater sensitivity to ecological floating beds,as indicated by an increase in dispersal limitation processes.Finally,the nitrogen metabolism functional genes revealed that microbes associated with large-sized particles played a crucial role in dissimilatory nitrate reduction to ammonium(DNRA)and denitrification processes within the floating bed area,thereby facilitating the removal of excess nitrogen nutrients from the water.In contrast,freeliving microorganisms from small-sized groups were linked mainly to the genes involved in nitrogen assimilation and assimilatory nitrate reduction to ammonium(ANRA)processes.These findings help understand the impact of ecological floating beds on the diversity and functional characteristics of microorganism communities in different size-fractionated groups.展开更多
In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digesti...In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.展开更多
At Beijing Tongren Hospital,an AI-powered retinal screening system can screen for 10 chronic illnesses from just two photos in two minutes.Using one fundus image from each eye,it scans for early signs of diabetic reti...At Beijing Tongren Hospital,an AI-powered retinal screening system can screen for 10 chronic illnesses from just two photos in two minutes.Using one fundus image from each eye,it scans for early signs of diabetic retinopathy,hypertension,atherosclerosis and other conditions,with a reported accuracy of about 90 percent.展开更多
Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Althoug...Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.展开更多
Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical si...Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.展开更多
Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in oper...Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.展开更多
Fungi play crucial roles in nutrient acquisition,plant growth promotion,and the enhancement of resistance to both abiotic and biotic stresses.However,studies on the fungal communities associated with peas (Pisum sativ...Fungi play crucial roles in nutrient acquisition,plant growth promotion,and the enhancement of resistance to both abiotic and biotic stresses.However,studies on the fungal communities associated with peas (Pisum sativum L.) remain limited.In this study,we systematically investigated the ecological effects of host niches (soil,root,stem,leaf,and pod) and genotypes on the diversity and composition of fungal communities in peas using a multi-level approach that encompassed pattern recognition (β-diversity decomposition),mechanism validation (neutral community model testing),and dynamic tracking methods (migration pathway source-tracking).The results revealed that the dominant fungal phyla across niches and genotypes were Ascomycota,Basidiomycota,and Mortierellomycota,and the community structures of the soil–plant continuum were primarily determined by the pea niches rather than genotypes.β-diversity decomposition was largely attributed to species replacement rather than richness differences,indicating strong niche specificity and microbial replacement across microhabitats.Neutral model analysis revealed that stochastic processes influenced genotypeassociated communities,while deterministic processes played a dominant role in niche-based community assembly.Source-tracking analysis identified niche-to-niche fungal migration,with Erysiphe,Fusarium,Cephaliophora,Ascobolus,Alternaria,and Aspergillus as the key genera.Migration rates from exogenous to endogenous niches were low (1.3–61.5%),whereas those within exogenous (64.4–83.7%) or endogenous (73.9–96.4%) compartments were much higher,suggesting that the pea epidermis acts as a selective barrier that filters and enriches microbial communities prior to internal colonization.This study provides comprehensive insights into the mechanisms of host filtering,enrichment and microbial sourcing,which increases our understanding of the assembly rules of the pea-associated fungal microbiome.展开更多
Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication,transportation,and installation.Nonetheless,their conceptual design remains predominantly dependent on enginee...Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication,transportation,and installation.Nonetheless,their conceptual design remains predominantly dependent on engineering experience,and a generally applicable approach is still absent.This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application.A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach,wherein modular constraints and their corresponding sensitivity expressions are explicitly included.The method is applied to a reference wind turbine model to generate modular lattice configurations.The novel structural models are evaluated under three representative design load cases outlined in IEC 61400 by finite element analysis.Compared with the reference structure,the 12-layer self-similar modular design reduces the maximum deformation and von Mises stress by 39.5%and 51.1%,respectively,demonstrating a substantial stiffness improvement while preserving modularity.The suggested approach provides an efficient and practical tool for the conceptual design of modular lattice-type wind turbine towers.展开更多
Adjuvants enhance and prolong the immune response to therapeutic agents,such as drugs and vaccines.However,conventional adjuvants have limitations in terms of immune specificity and duration.Nanoadjuvants can leverage...Adjuvants enhance and prolong the immune response to therapeutic agents,such as drugs and vaccines.However,conventional adjuvants have limitations in terms of immune specificity and duration.Nanoadjuvants can leverage their nanoscale size to increase the capture efficacy of antigens by antigen-presenting cells and improve immunogen presentation for targeted delivery.Furthermore,noninvasive visualization of bifunctional nanoadjuvants with integrated efficacy and imaging postdelivery can provide insights into in vivo distribution and performance,aiding in the optimization and design of new dosage forms.This review systematically summarizes the structure,assembly,and function of nanoadjuvants alongside contrast agents.It delves into the impact of complex structures formed by nanoadjuvant-contrast agent interactions on antigen presentation,migration,imaging tracking,and visualization of immune cell recruitment.It also discusses how imaging can determine optimal immune intervals,vaccine safety,and toxicity while enabling diagnostic and therapeutic integration.Moreover,this paper discusses potential applications of novel adjuvants and promising imaging technologies that could have implications for future vaccine and drug development endeavors.展开更多
IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018...IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018).IR64 has been utilized to develop stress-tolerant(such as drought-adapted and submergenceresistant)near-isogenic lines,underscoring its great potential in agricultural genomics(Tanaka et al.,2020).展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
To improve the accuracy of capacity analysis and prediction for the aircraft assembly stations,an approach for calculating the effective working hour(EWH)of automatic assembly equipment is introduced by using the dyna...To improve the accuracy of capacity analysis and prediction for the aircraft assembly stations,an approach for calculating the effective working hour(EWH)of automatic assembly equipment is introduced by using the dynamic mixed Weibull distribution(DMWD)model.Firstly,according to the features of aircraft assembling,a DMWD model considering the dynamic reliability of multiple subsystems and their synthetic effects on the whole equipment is established.A typical automatic drilling&riveting machine is selected as the research object,and the dynamic weights of reliability of three subsystems are modeled and solved.Subsequently the unknown parameters of the DMWD model are estimated based on maximum likelihood estimation(MLE)and Newton-Raphson method.Finally,the EWH of an automatic station is defined and modeled by using the solved dynamic reliability function.Based on the experimental study on a real automatic drilling&riveting machine from a wing panel assembly station,it is shown that the proposed DMWD and EWH models could effectively calculate the equipment reliability with full consideration of its multiple subsystems.The DMWD model is more suitable for improving the solution precision of EWH than the traditional three-parameter Weibull distribution.展开更多
In the process of generating assembly dimension chain automatically, there are many problems such as complex assembly relationship, variety of constraints of components and complex dimension and tolerance relations of...In the process of generating assembly dimension chain automatically, there are many problems such as complex assembly relationship, variety of constraints of components and complex dimension and tolerance relations of components. These problems lead to many difficulties in search process and need lots of man-machine interaction. Because of these difficulties, this paper presents a method of automatic generation of assembly dimension chain based on tolerance cell. This method is realized through obtaining assembly tolerance cell and part tolerance cell first. These two types of tolerance cell are extracted and expressed in the form of linked list structure in computer further. Then assembly dimension chain is searched automatically based on obtained tolerance cell, and the correct assembly dimension chain is extracted finally. A system of generating assembly dimension chain automatically has been developed based on DELMIA and successfully deployed in aircraft assembly simulation in the engineerine practice.展开更多
The low-stiffness of aircraft skins may results in the differences between aircraft actual parts and their theoretical models,which will consequently affect the accuracy of automatic drilling and riveting in aircraft ...The low-stiffness of aircraft skins may results in the differences between aircraft actual parts and their theoretical models,which will consequently affect the accuracy of automatic drilling and riveting in aircraft assembly.In this paper,a novel approach of hole position correction using laser line scanner(LLS)is proposed to assign a single row of holes on the parts’surfaces.First,we adopt a space circle fitting method and the random sample consensus(RANSAC)to obtain the precise coordinates of center of the datum holes’coordinates.Second,LLS is calibrated by the laser tracker,and the relations between the LLS coordinate system and the tool coordinate system(TCS)can be calculated.Third,the kinematics model of the automatic riveting machine is established based on a two-point referencing strategy proposed in this paper.Thus,the positions of the holes to be drilled can be adjusted.Finally,the experimental results show that in TCS the measurement error of LLS is less than 0.1 mm,and the correction error of the hole position is less than 0.5 mm,which demonstrates the reliability of our method.展开更多
A method of 3-D measuring fixture automatic assembly for auto-body part is presented. Locating constraint mapping technique and assembly rule-based reasoning are applied. Calculating algorithm of the position and pose...A method of 3-D measuring fixture automatic assembly for auto-body part is presented. Locating constraint mapping technique and assembly rule-based reasoning are applied. Calculating algorithm of the position and pose for the part model, fixture configuration and fixture elements in virtual auto-body assembly space are given. Transforming fixture element from itself coordinate system space to assembly space with homogeneous transformation matrix is realized. Based on the second development technique of unigraphics（UG）, the automated assembly is implemented with application program interface （API） function. Lastly the automated assembly of measuring fixture for rear longeron as a case is implemented.展开更多
Manufacturing accuracy, especially position accuracy of fastener holes, directly affects service life and security of aircraft. The traditional modification has poor robustness, while the modification based on laser t...Manufacturing accuracy, especially position accuracy of fastener holes, directly affects service life and security of aircraft. The traditional modification has poor robustness, while the modification based on laser tracker costs too much. To improve the relative position accuracy of aircraft assembly drilling, and ensure the hole-edge distance requirement, a method was presented to modify the coordinates of drilling holes. Based on online inspecting two positions of pre-assembly holes and their theoretical coordinates, the spatial coordinate transformation matrix of modification could be calculated. Thus the straight drilling holes could be modified. The method improves relative position accuracy of drilling on simple structure effectively. And it reduces the requirement of absolute position accuracy and the cost of position modification. And the process technician also can use this method to decide the position accuracy of different pre-assembly holes based on the accuracy requirement of assembly holes.展开更多
基金supported by Shanghai Municipal Natural Science Foundation,China(No.21ZR1446800)the National Natural Science Foundation of China(No.41877425)the Fundamental Research Funds for the Central Universities(No.226-2024-00052)。
文摘Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community assembly in virgin and aging MP plastispheres across different habitats is poorly understood.This study aims to assess the variations in bacterial community assembly across different niches and habitats with an in situ ex-periment,in which constructed forest wetland(FW),natural lake wetland(LW),and lotus pond wetland(LP)were habitats,and plastispheres of virgin and aging low-density polyethylene(LDPE)MPs,as well as surround-ing wetland soils were niches.Significant niche-related differences in bacterial communities were observed,with lower diversity and enrichment of potential plastic-degrading bacteria in the plastisphere than in the soil bacterial communities.Furthermore,habitat-related differences exerted a more pronounced influence on the beta-diversity patterns of the bacterial communities.The linear regression analyses indicated that the local species pool con-tributed more to bacterial community assembly in the LW wetland,whereas the relative abundance of species was the primary factor in the LP wetland.The null model analysis indicated that plastisphere bacterial communi-ties were predominantly driven by the stochastic process,with a more deterministic assembly observed in the LP wetland and soil bacterial communities.Additionally,the primary ecological process shaping plastisphere com-munities shifted from drift in the virgin LDPE to homogenising dispersal in the aging LDPE.This study provides new insights into the fate and ecological impacts of MPs in wetlands,thereby facilitating the effective regulations of plastic pollution.
基金NR SEQUOIA(ANR-22-CE18-0006)Nan0Gold(ANR-22-CE29-0022)+3 种基金SAMURAI(ANR-24-CE19-2073-01)Wilive(ANR-24-CE09-2351-03)EUR CBH-EUR-GS(ANR-17-EURE 0003)for their financial supportthe French National Research Agency(Labex ARCANE,ANR-11-LABX-003 and CBH-EUR-GS,ANR-17-EURE-0003)that supported part of this study.
文摘Gold nanoclusters(AuNCs)are ultrasmall(<2 nm)aggregates of gold atoms that exhibit discrete electronic states,size-dependent photoluminescence,and exceptional biocompatibility,making them ideal candidates for theranostic applications.Their tunable surface chemistry enables targeted delivery,while strong near-infrared emission and environmental responsiveness allow for sensitive detection and deep-tissue imaging.Recent advances have revealed that controlled assembly of AuNCs into higher-order architectures-guided by biological scaffolds such as nucleic acids,peptides,and proteins-can markedly enhance their optical and electronic properties through aggregation-induced emission(AiE)and stabilization of surface ligands.This review summarizes recent progress in the design and biomedical applications of AuNC assemblies generated using biomolecules as structure-directing scaffolds.Covalent and noncovalent interactions with biomolecules enable the formation of well-defined one-,two-,and three-dimensional structures with tunable morphologies and sizes.These assemblies display distinctive photophysical behaviors that have been exploited for biosensing,bioimaging,and therapeutic applications in both cellular and in vivo models.Compared with individual AuNCs,assembled systems offer improved uptake,prolonged circulation,and efficient clearance,while protecting labile cargos such as nucleic acids and proteins.Moreover,their ordered and defined architectures can be engineered for controlled drug release and synergistic photo-or radiotherapeutic effects.Despite these advances,fundamental understanding of how structural organization governs photophysical responses remains limited.Elucidating parameters such as intercluster spacing and loading density will be essential for optimizing performance.Overall,biologically guided AuNC assemblies represent a powerful platform for multifunctional biosensing and therapy,bridging nanoscale design with biological function.
基金The National Social Science Foundation Youth Project of China:Research on the collaborative govemance path of administrative law and criminal law against dangerous driving behaviors in the digital-intelligent society(25CFX108)。
文摘With the continuous progress of automatic driving technology,automatic driving technology standards are gradually affecting the determination of criminal responsibility for traffic accidents in China.At present,the characteristics and tendency of China's automatic driving technology standards present the situation of high policy relevance coexisting with low normative binding,professionalism coexist with barriers,forefront coexist with ambiguity.Therefore,challenges are presented both theoretically and practically on the determination of criminal responsibility based on automatic driving technology standard..In this regard,the misunderstanding should be clarified in theory:The legal order under the automatic driving technology standard has constitutionality and systematic,and there is a balance between the frontier of automatic driving technology development and the lagging of criminal law.The automatic driving technology risk level system should be built to clarify the boundary of the effectiveness of criminal law norms,seeking fora breakthrough in the application of the establishment of a comprehensive judgment system of the risks and accidents and the system of evidence to prove the system,which clarifies the determination of criminal responsibility under the automatic driving technology standard.This essay hopes to pursue breakthroughs in the application-to establish a comprehensive judgment system of risks and accidents as well as an evidence proof system,so as to clarify the determination of criminal responsibility under automatic driving technology standards.
基金supported by National Natural Science Foundation of China(Grant 22407024)the Star-up Research Fund of Southeast University(RF1028624094)(X.W.)+7 种基金the China Postdoctoral Science Foundation(Grant 2025M772911)Natural Science Foundation of Jiangsu Province(Grants BK20251303)(X.L.)Postdoctoral Fellowship Program of CPSF(Grant GZC20251914)(X.L.)Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant 2025ZB052)(X.L.)National Natural Science Foundation of China(Grant 22234002)(G.L.)National Key Research and Development Program of China(Grant 2023YFF0724100)(G.L.)Natural Science Foundation of Jiangsu Province(Grant BK20232007)(G.L.)Jiangsu ShuangChuang Team(Grant JSSCTD202409)(G.L.and X.W.).
文摘Targeted protein degradation(TPD)is an innovative strategy for selectively eliminating pathogenic proteins,enabling precise degradation of once-undruggable targets in cancer therapy.However,current TPD molecules are often limited by poor tumor targeting and the need for high doses.To overcome these limitations,assembly/disassembly-based TPD systems have been proposed to effectively degrade proteins of interest and enhance therapeutic efficacy.Herein,we summarize the recent advances in such TPD systems and categorize the strategies employed,including nanosphere morphology of assembled TPD systems,nanofiber morphology of assembled TPD systems,carrier-mediated TPD release systems,and stimulus-induced free TPD molecule formation nanosystems.Finally,we outline future directions and identify the remaining challenges in assembly/disassembly-based TPD systems.
基金Supported by the National Natural Science Foundation of China(Nos.42141003,42176147)the National Key Research and Development Program of China(No.2022YFF0802204)the Xiamen Key Laboratory of Urban Sea Ecological Conservation and Restoration(USER)(Nos.USER2021-1,USER2021-5)。
文摘Ecological floating bed is an important biological remediation method for water pollution control.During the removal of excess nutrients and pollutants,changes in environmental factors affect the characteristics of microorganisms in aquatic ecosystems.To understand the influences of ecological floating beds on size-fractionated microorganisms,we investigated the community assembly and nitrogen metabolic characteristics of three size-fractionated microorganism groups in the ecological floating bed area,using 18S rDNA,16S rDNA metabarcoding,and metagenomic sequencing techniques.Firstly,we discovered substantial differences between size-fractionated groups in the diversity and compositions of both microeukaryotic and bacterial communities,as well as the influences of floating beds on specific groups.The floating beds appeared to provide more habitats for heterotrophs and symbiotes while potentially inhibiting the growth of certain phytoplankton(cyanobacteria).Secondly,we observed that microeukaryotic and bacterial communities were predominantly influenced by stochastic and deterministic processes,respectively,and they both exhibited distinct patterns across different size-fractionated groups.Notably,microeukaryotic community assembly demonstrated a greater sensitivity to ecological floating beds,as indicated by an increase in dispersal limitation processes.Finally,the nitrogen metabolism functional genes revealed that microbes associated with large-sized particles played a crucial role in dissimilatory nitrate reduction to ammonium(DNRA)and denitrification processes within the floating bed area,thereby facilitating the removal of excess nitrogen nutrients from the water.In contrast,freeliving microorganisms from small-sized groups were linked mainly to the genes involved in nitrogen assimilation and assimilatory nitrate reduction to ammonium(ANRA)processes.These findings help understand the impact of ecological floating beds on the diversity and functional characteristics of microorganism communities in different size-fractionated groups.
文摘In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.
文摘At Beijing Tongren Hospital,an AI-powered retinal screening system can screen for 10 chronic illnesses from just two photos in two minutes.Using one fundus image from each eye,it scans for early signs of diabetic retinopathy,hypertension,atherosclerosis and other conditions,with a reported accuracy of about 90 percent.
基金supported by Key Laboratory of Cyberspace Security,Ministry of Education,China。
文摘Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.
基金financially supported by the National Key Research and Development Program of China (2022YFB3706802)。
文摘Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.
基金supported by the National Natural Science Foundation of China(Grant No.52475543)Natural Science Foundation of Henan(Grant No.252300421101)+1 种基金Henan Province University Science and Technology Innovation Talent Support Plan(Grant No.24HASTIT048)Science and Technology Innovation Team Project of Zhengzhou University of Light Industry(Grant No.23XNKJTD0101).
文摘Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.
基金financial y supported by the National Key Research and Development Program of China (2023YFD1900902)the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China (LLSSZ24C030001)+1 种基金the Earmarked Fund for China Agriculture Research System (CARS-08-G-09)sponsored by the K.C.Wong Magna Fund of Ningbo University,China。
文摘Fungi play crucial roles in nutrient acquisition,plant growth promotion,and the enhancement of resistance to both abiotic and biotic stresses.However,studies on the fungal communities associated with peas (Pisum sativum L.) remain limited.In this study,we systematically investigated the ecological effects of host niches (soil,root,stem,leaf,and pod) and genotypes on the diversity and composition of fungal communities in peas using a multi-level approach that encompassed pattern recognition (β-diversity decomposition),mechanism validation (neutral community model testing),and dynamic tracking methods (migration pathway source-tracking).The results revealed that the dominant fungal phyla across niches and genotypes were Ascomycota,Basidiomycota,and Mortierellomycota,and the community structures of the soil–plant continuum were primarily determined by the pea niches rather than genotypes.β-diversity decomposition was largely attributed to species replacement rather than richness differences,indicating strong niche specificity and microbial replacement across microhabitats.Neutral model analysis revealed that stochastic processes influenced genotypeassociated communities,while deterministic processes played a dominant role in niche-based community assembly.Source-tracking analysis identified niche-to-niche fungal migration,with Erysiphe,Fusarium,Cephaliophora,Ascobolus,Alternaria,and Aspergillus as the key genera.Migration rates from exogenous to endogenous niches were low (1.3–61.5%),whereas those within exogenous (64.4–83.7%) or endogenous (73.9–96.4%) compartments were much higher,suggesting that the pea epidermis acts as a selective barrier that filters and enriches microbial communities prior to internal colonization.This study provides comprehensive insights into the mechanisms of host filtering,enrichment and microbial sourcing,which increases our understanding of the assembly rules of the pea-associated fungal microbiome.
基金funded by the National Key Research and Development Program of China(No.2024YFE0208600)the National Natural Science Foundation of China(No.U24B2090).
文摘Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication,transportation,and installation.Nonetheless,their conceptual design remains predominantly dependent on engineering experience,and a generally applicable approach is still absent.This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application.A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach,wherein modular constraints and their corresponding sensitivity expressions are explicitly included.The method is applied to a reference wind turbine model to generate modular lattice configurations.The novel structural models are evaluated under three representative design load cases outlined in IEC 61400 by finite element analysis.Compared with the reference structure,the 12-layer self-similar modular design reduces the maximum deformation and von Mises stress by 39.5%and 51.1%,respectively,demonstrating a substantial stiffness improvement while preserving modularity.The suggested approach provides an efficient and practical tool for the conceptual design of modular lattice-type wind turbine towers.
基金supported by the National Key R&D Project of China(Nos.2022YFC2304205,2022YFC2304202)Key scientific research project of universities in Guangdong Province(No.2023KCXTD026)the Major Project of Guangzhou National Laboratory(No.GZNL2023A03002).
文摘Adjuvants enhance and prolong the immune response to therapeutic agents,such as drugs and vaccines.However,conventional adjuvants have limitations in terms of immune specificity and duration.Nanoadjuvants can leverage their nanoscale size to increase the capture efficacy of antigens by antigen-presenting cells and improve immunogen presentation for targeted delivery.Furthermore,noninvasive visualization of bifunctional nanoadjuvants with integrated efficacy and imaging postdelivery can provide insights into in vivo distribution and performance,aiding in the optimization and design of new dosage forms.This review systematically summarizes the structure,assembly,and function of nanoadjuvants alongside contrast agents.It delves into the impact of complex structures formed by nanoadjuvant-contrast agent interactions on antigen presentation,migration,imaging tracking,and visualization of immune cell recruitment.It also discusses how imaging can determine optimal immune intervals,vaccine safety,and toxicity while enabling diagnostic and therapeutic integration.Moreover,this paper discusses potential applications of novel adjuvants and promising imaging technologies that could have implications for future vaccine and drug development endeavors.
基金supported by the Natural Science Foundation of Anhui Province(2408085MC058 and 2308085QC91)National Natural Science Foundation of China(32301783and U21A20214)+5 种基金Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS CSIAF-202303)Nanfan special project,CAAS(YYLH2309,YBXM2322,YYLH2401)Scientific Innovation 2030 Project(2022ZD0401703)CAAS Innovative Team Award,Science and Technology of Innovative research program of Anhui Province(202423m1005002)National Key Research and Development Program of China(2023YFD1200900)the Natural Science Foundation General Program of Hebei Province(C2024204242).
文摘IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018).IR64 has been utilized to develop stress-tolerant(such as drought-adapted and submergenceresistant)near-isogenic lines,underscoring its great potential in agricultural genomics(Tanaka et al.,2020).
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金This work was supported in part by the Fundamental Research Funds for the Central Universities(Nos.N170303009,N180703007),China.
文摘To improve the accuracy of capacity analysis and prediction for the aircraft assembly stations,an approach for calculating the effective working hour(EWH)of automatic assembly equipment is introduced by using the dynamic mixed Weibull distribution(DMWD)model.Firstly,according to the features of aircraft assembling,a DMWD model considering the dynamic reliability of multiple subsystems and their synthetic effects on the whole equipment is established.A typical automatic drilling&riveting machine is selected as the research object,and the dynamic weights of reliability of three subsystems are modeled and solved.Subsequently the unknown parameters of the DMWD model are estimated based on maximum likelihood estimation(MLE)and Newton-Raphson method.Finally,the EWH of an automatic station is defined and modeled by using the solved dynamic reliability function.Based on the experimental study on a real automatic drilling&riveting machine from a wing panel assembly station,it is shown that the proposed DMWD and EWH models could effectively calculate the equipment reliability with full consideration of its multiple subsystems.The DMWD model is more suitable for improving the solution precision of EWH than the traditional three-parameter Weibull distribution.
文摘In the process of generating assembly dimension chain automatically, there are many problems such as complex assembly relationship, variety of constraints of components and complex dimension and tolerance relations of components. These problems lead to many difficulties in search process and need lots of man-machine interaction. Because of these difficulties, this paper presents a method of automatic generation of assembly dimension chain based on tolerance cell. This method is realized through obtaining assembly tolerance cell and part tolerance cell first. These two types of tolerance cell are extracted and expressed in the form of linked list structure in computer further. Then assembly dimension chain is searched automatically based on obtained tolerance cell, and the correct assembly dimension chain is extracted finally. A system of generating assembly dimension chain automatically has been developed based on DELMIA and successfully deployed in aircraft assembly simulation in the engineerine practice.
基金supported by the National Natural Science Foundation of China (No.51875287)the National Defense Basic Scientific Research Program of China (No.JCKY2018605C010)the National Key Research and Development Program of China (No.2018YFB1306800)
文摘The low-stiffness of aircraft skins may results in the differences between aircraft actual parts and their theoretical models,which will consequently affect the accuracy of automatic drilling and riveting in aircraft assembly.In this paper,a novel approach of hole position correction using laser line scanner(LLS)is proposed to assign a single row of holes on the parts’surfaces.First,we adopt a space circle fitting method and the random sample consensus(RANSAC)to obtain the precise coordinates of center of the datum holes’coordinates.Second,LLS is calibrated by the laser tracker,and the relations between the LLS coordinate system and the tool coordinate system(TCS)can be calculated.Third,the kinematics model of the automatic riveting machine is established based on a two-point referencing strategy proposed in this paper.Thus,the positions of the holes to be drilled can be adjusted.Finally,the experimental results show that in TCS the measurement error of LLS is less than 0.1 mm,and the correction error of the hole position is less than 0.5 mm,which demonstrates the reliability of our method.
文摘A method of 3-D measuring fixture automatic assembly for auto-body part is presented. Locating constraint mapping technique and assembly rule-based reasoning are applied. Calculating algorithm of the position and pose for the part model, fixture configuration and fixture elements in virtual auto-body assembly space are given. Transforming fixture element from itself coordinate system space to assembly space with homogeneous transformation matrix is realized. Based on the second development technique of unigraphics（UG）, the automated assembly is implemented with application program interface （API） function. Lastly the automated assembly of measuring fixture for rear longeron as a case is implemented.
文摘Manufacturing accuracy, especially position accuracy of fastener holes, directly affects service life and security of aircraft. The traditional modification has poor robustness, while the modification based on laser tracker costs too much. To improve the relative position accuracy of aircraft assembly drilling, and ensure the hole-edge distance requirement, a method was presented to modify the coordinates of drilling holes. Based on online inspecting two positions of pre-assembly holes and their theoretical coordinates, the spatial coordinate transformation matrix of modification could be calculated. Thus the straight drilling holes could be modified. The method improves relative position accuracy of drilling on simple structure effectively. And it reduces the requirement of absolute position accuracy and the cost of position modification. And the process technician also can use this method to decide the position accuracy of different pre-assembly holes based on the accuracy requirement of assembly holes.
