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
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
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
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
The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they ...The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.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
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
Directional three-dimensional carbon-based foams are emerging as highly attractive candidates for promising electromagnetic wave absorbing materials(EWAMs)thanks to their unique architecture,but their construction usu...Directional three-dimensional carbon-based foams are emerging as highly attractive candidates for promising electromagnetic wave absorbing materials(EWAMs)thanks to their unique architecture,but their construction usually involves complex procedures and extremely depends on unidirectional freezing technique.Herein,we propose a groundbreaking approach that leverages the assemblies of salting-out protein induced by ammonium metatungstate(AM)as the precursor,and then acquire directional three-dimensional carbon-based foams through simple pyrolysis.The electrostatic interaction between AM and protein ensures well dispersion of WC_(1−x)nanoparticles on carbon frameworks.The content of WC_(1−x)nanoparticles can be rationally regulated by AM dosage,and it also affects the electromagnetic(EM)properties of final carbon-based foams.The optimized foam exhibits exceptional EM absorption performance,achieving a remarkable minimum reflection loss of−72.0 dB and an effective absorption bandwidth of 6.3 GHz when EM wave propagates parallel to the directional pores.Such performance benefits from the synergistic effects of macroporous architecture and compositional design.Although there is a directional dependence of EM absorption,radar stealth simulation demonstrates that these foams can still promise considerable reduction in radar cross section with the change of incident angle.Moreover,COMSOL simulation further identifies their good performance in preventing EM interference among different electronic components.展开更多
Wheat(Triticum aestivum)faces significant threats from diseases such as powdery mildew(Blumeria graminis)and Fusarium head blight(FHB;caused by Fusarium graminearum),which cause severe yield losses.Moreover,the antago...Wheat(Triticum aestivum)faces significant threats from diseases such as powdery mildew(Blumeria graminis)and Fusarium head blight(FHB;caused by Fusarium graminearum),which cause severe yield losses.Moreover,the antagonism between yield-related traits and disease resistance makes yield resistance coordination a major challenge in wheat breeding.The lack of genetic resources combining both disease resistance and high yield constrains the elucidation of underlying resistance-yield trade-off mechanisms,thereby hindering the development of high-yield and disease-resistant wheat cultivars.Remarkably,Yangmai 33(YM33),a notable wheat cultivar with resistance to both powdery mildew and FHB as well as high-yield performance,was recently developed.It offers a unique opportunity to dissect the genomic architecture underlying the coordination between disease resistance and yield.展开更多
With the rapid development of sequencing technologies,especially the maturity of third-generation sequencing technologies,there has been a significant increase in the number and quality of published genome assemblies....With the rapid development of sequencing technologies,especially the maturity of third-generation sequencing technologies,there has been a significant increase in the number and quality of published genome assemblies.The emergence of these high-quality genomes has raised higher requirements for genome evaluation.Although numerous computational methods have been developed to evaluate assembly quality from various perspectives,the selective use of these evaluation methods can be arbitrary and inconvenient for fairly comparing the assembly quality.To address this issue,we have developed the Genome Assembly Evaluating Pipeline(GAEP),which provides a comprehensive assessment pipeline for evaluating genome quality from multiple perspectives,including continuity,completeness,and correctness.Additionally,GAEP includes new functions for detecting misassemblies and evaluating the assembly redundancy,which performs well in our testing.GAEP is publicly available at https://github.com/zyoptimistic/GAEP under the GPL3.0 License.With GAEP,users can quickly obtain accurate and reliable evaluation results,facilitating the comparison and selection of high-quality genome assemblies.展开更多
Assembly precision greatly influences the performance of complex high-end equipment.The traditional industrial assembly process and deviation transfer are implicit and uncertain,causing problems like poor component fi...Assembly precision greatly influences the performance of complex high-end equipment.The traditional industrial assembly process and deviation transfer are implicit and uncertain,causing problems like poor component fit and hard-to-trace assembly stress concentration.Assemblers can only check whether the dimensional tolerance of the component design is exceeded step by step in combination with prior knowledge.Inversion in industrial assembly optimizes assembly and design by comparing real and theoretical results and doing inversion analysis to reduce assembly deviation.The digital twin(DT)technology visualizes and predicts the assembly process by mapping real and virtual model parameters and states simultaneously,expanding parameter range for inversion analysis and improving inversion result accuracy.Problems in improving industrial assembly precision and the significance and research status of DT-driven parametric inversion of assembly tools,processes and object precision are summarized.It analyzes vital technologies for assembly precision inversion such as multi-attribute assembly process parameter sensing,virtual modeling of high-fidelity assembly systems,twin synchronization of assembly process data models,multi-physical field simulation,and performance twin model construction of the assembly process.Combined with human-cyber-physical system,augmented reality,and generative intelligence,the outlook of DT-driven assembly precision inversion is proposed,providing support for DT's use in industrial assembly and precision improvement.展开更多
As the demands for assembly quality and efficiency increase,robot-assisted assembly applications are becoming more widespread.Peg-in-hole assembly,as a typical form of assembly,has been widely researched by scholars.C...As the demands for assembly quality and efficiency increase,robot-assisted assembly applications are becoming more widespread.Peg-in-hole assembly,as a typical form of assembly,has been widely researched by scholars.Currently,robotic peg-in-hole assembly faces challenges such as complex analysis of part contact forces,difficulties in task modeling,and the failure of traditional strategies.Simply controlling the position of the robot's end effector cannot achieve high precision,high efficiency peg-in-hole assembly.Flexible assembly,especially intelligent flexible assembly,is becoming the future development trend.So there is a lack of comprehensive reviews on robotic flexible peg-in-hole assembly.This paper first outlines the basic components of peg-in-hole assembly and summarizes the two basic operational processes of peg-in-hole assembly,along with their related theoretical foundations.We then review and analyze the research on passive compliant assembly,active compliant assembly,and intelligent flexible assembly.Finally,it presents an outlook on the future development directions of robotic peg-in-hole assembly.展开更多
To address the challenges of insufficient visualization in the industrial robot assembly operation system and the limitation of visualizing only geometric attributes of physical properties,a method is proposed for con...To address the challenges of insufficient visualization in the industrial robot assembly operation system and the limitation of visualizing only geometric attributes of physical properties,a method is proposed for constructing an industrial robot assembly system based on virtual reality technology.Focusing on the shaft hole assembly,the mechanical characteristics of the industrial robot shaft hole assembly process are analyzed and a dynamic model is established for shaft hole assembly operations.The key elements of virtual assembly operations for industrial robots are summarized and a five-dimensional model is proposed for industrial robot virtual operations.Utilizing the Unity3D engine based on the 5-D model for industrial robot virtual operations,an industrial robot shaft hole assembly system is developed.This system enables virtual assembly operations,displays physical attributes,and provides valuable references for the research of virtual systems.展开更多
Common bean(Phaseolus vulgaris L.)is a vital source of protein and essential nutrients for human consumption and plays a key role in sustainable agriculture due to its nitrogen-fixing ability(Nadeem et al.,2021).Kidne...Common bean(Phaseolus vulgaris L.)is a vital source of protein and essential nutrients for human consumption and plays a key role in sustainable agriculture due to its nitrogen-fixing ability(Nadeem et al.,2021).Kidney beans,a subcategory of dry common beans,are highly valued for their rich protein,dietary fiber,low fat content,and various trace elements(Garcia-Cordero et al.,2021).Despite the release of several de novo genome assemblies(Goodstein et al.,2012;Schmutz et al.,2014;Vlasova et al.,2016;Cortinovis et al.,2024),existing common bean genomes remain incomplete,particularly in complex regions such as centromeres and telomeres,limiting a comprehensive understanding of the genomic landscape.展开更多
Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly(HRCA),challenges remain in the robot’s ability to understand and predict human assembly intentions.This study ...Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly(HRCA),challenges remain in the robot’s ability to understand and predict human assembly intentions.This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements.We propose a video feature extraction method based on the Temporal Shift Module Network(TSM-ResNet50)to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames.Furthermore,we construct an action recognition and segmentation model based on the Refined-Multi-Scale Temporal Convolutional Network(Refined-MS-TCN)to identify assembly action intervals and accurately acquire action categories.Experiments on our self-built reducer assembly action dataset demonstrate that our network can classify assembly actions frame by frame,achieving an accuracy rate of 83%.Additionally,we develop a HiddenMarkovModel(HMM)integrated with assembly task constraints to predict operator assembly intentions based on the probability transition matrix and assembly task constraints.The experimental results show that our method for predicting operator assembly intentions can achieve an accuracy of 90.6%,which is a 13.3%improvement over the HMM without task constraints.展开更多
Challenges still remain in carrying out assembly modeling efficiently in virtual assembly （VA） fields. One of the root causes is the apparent weakness in effective description of assembly knowledge and information. ...Challenges still remain in carrying out assembly modeling efficiently in virtual assembly （VA） fields. One of the root causes is the apparent weakness in effective description of assembly knowledge and information. The assembly modeling, disassembly modeling, assembly interference inspection, assembly sequence planning and optimization, and assembly simulation display for key techniques is studied theoretically in this paper. An example of product assembly modeling is provided to illustrate the effectiveness of the proposed approach. On the basis of re- search, using assembly simulation techniques and multimedia techniques to finish structure design in linkage design of a large size wind-drive generator. The application of the modeling method has shortened the lead time dramatically.展开更多
Assembly process planning(APP)for complicated products is a time-consuming and difficult work with conventional method.Virtual assembly process planning(VAPP)provides engineers a new and efficiency way.Previous studie...Assembly process planning(APP)for complicated products is a time-consuming and difficult work with conventional method.Virtual assembly process planning(VAPP)provides engineers a new and efficiency way.Previous studies in VAPP are almost isolated and dispersive,and have not established a whole understanding and discussed key realization techniques of VAPP from a systemic and integrated view.The integrated virtual assembly process planning(IVAPP)system is a new virtual reality based engineering application,which offers engineers an efficient,intuitive,immersive and integrated method for assembly process planning in a virtual environment.Based on analysis the information integration requirement of VAPP,the architecture of IVAPP is proposed.Through the integrated structure,IVAPP system can realize information integration and workflow controlling.In order to mode/the assembly process in IVAPP,a hierarchical assembly task list(HATL)is presented,in which different assembly tasks for assembling different components are organized into a hierarchical list.A process-oriented automatic geometrical constraint recognition algorithm(AGCR)is proposed,so that geometrical constraints between components can be automatically recognized during the process of interactive assembling.At the same time,a progressive hierarchical reasoning(PHR)model is discussed.AGCR and PHR will greatly reduce the interactive workload.A discrete control node model(DCNM)for cable harness assembly planning in IVAPP is detailed.DCNM converts a cable harness into continuous flexed line segments connected by a series of section center points,and designs can realize cable harness planning through controlling those control nodes.Mechanical assemblies(such as transmission case and engine of automobile)are used to illustrate the feasibility of the proposed method and algorithms.The application of IVAPP system reveals advantages over the traditional assembly process planning method in shortening the time-consumed in assembly planning and in minimizing the handling difficulty,excessive reorientation and dissimilarity of assembly operations.展开更多
Biological nitrogen fixation(BNF)is a crucial process that provides bioavailable nitrogen and supports primary production in freshwater lake ecosystems.However,the characteristics of diazotrophic community and nitroge...Biological nitrogen fixation(BNF)is a crucial process that provides bioavailable nitrogen and supports primary production in freshwater lake ecosystems.However,the characteristics of diazotrophic community and nitrogenase activity in freshwater lake sediments remain poorly understood.Here,we investigated the diazotrophic communities and nitrogenase activities in the sediments of three large river-connected freshwater lakes in eastern China using 15N-isotope tracing and nifH sequencing.The sediments in these lakes contained diverse nitrogenase genes that were phylogenetically grouped into Clusters I and III.The diazotrophic communities in the sedimentswere dominated by stochastic processes in Hongze Lake and Taihu Lake,which had heterogeneous habitats and shallower water depths,while in Poyang Lake,which had deeper water and a shorter hydraulic retention time,the assembly of the diazotrophic community in the sediments was dominated by homogeneous selection processes.Temperature and water depth were also found the key environmental factors affecting the sediment diazotrophic communities.Sediment nitrogenase activities varied in the three lakes and within distinct regions of an individual lake,ranging from 0 to 14.58 nmol/(kg·hr).Nitrogenase activity was significantly correlated with ferric iron,total phosphorus,and organic matter contents.Our results suggested that freshwater lake sediment contain high diversity of nitrogen-fixing microorganisms with potential metabolic diversity,and the community assembly patterns and nitrogenase activities varied with the lake habitat.展开更多
Drug-eluting magnesium(Mg)alloy stents have a slower degradation rate and lower restenosis rate compared with uncoated stents,demonstrating good clinical efficacy.However,the release of anti-hyperplasia drugs from coa...Drug-eluting magnesium(Mg)alloy stents have a slower degradation rate and lower restenosis rate compared with uncoated stents,demonstrating good clinical efficacy.However,the release of anti-hyperplasia drugs from coatings delays endothelial tissue repair,thus leading to late stent thrombosis.To address these issues,a dual self-healed coating with various biological properties was fabricated on magnesium fluoride/polydopamine(MgF_(2)/PDA)-treated Mg alloys by spraying-assisted layer-by-layer(LBL)self-assembly of chitosan(CS),gallic acid(GA),and 3-aminobenzeneboronic acid-modified hyaluronic acid(HA-ABBA).The LBL coating,approximately 1.50μm thick,exhibited a uniform morphology with good adhesion strength(~1065 mN).The annual corrosion rate(Pi)of LBL samples was~1400 times slower than that of the Mg substrate,due to the physical barrier function provided by MgF_(2)/PDA layers and the dual self-healed ability of LBL layers.The rapid self-healing ability(with a healing period of~4 h under dynamic/static conditions)resulted from the synergistic interplay between the recombination of diverse chemical bonds within the LBL coating and the coordination of LBL-released GA with Mg2+,as corroborated by computer simulations.Compared with the drug-eluting coatings,the LBL sample demonstrated substantial advantages in anti-oxidation,anti-denaturation of fibrinogen,anti-platelet adhesion,anti-inflammation,anti-hyperplasia,and promoted-endothelialization.These benefits effectively address the limitations associated with drug-eluting coatings.展开更多
Membrane electrode assembly(MEA)is widely considered to be the most promising type of electrolyzer for the practical application of electrochemical CO_(2) reduction reaction(CO_(2)RR).In MEAs,a square-shaped cross-sec...Membrane electrode assembly(MEA)is widely considered to be the most promising type of electrolyzer for the practical application of electrochemical CO_(2) reduction reaction(CO_(2)RR).In MEAs,a square-shaped cross-section in the flow channel is normally adopted,the configuration optimization of which could potentially enhance the performance of the electrolyzer.This paper describes the numerical simulation study on the impact of the flow-channel cross-section shapes in the MEA electrolyzer for CO_(2)RR.The results show that wide flow channels with low heights are beneficial to the CO_(2)RR by providing a uniform flow field of CO_(2),especially at high current densities.Moreover,the larger the electrolyzer,the more significant the effect is.This study provides a theoretical basis for the design of high-performance MEA electrolyzers for CO_(2)RR.展开更多
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.
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 in part by the Rosetrees Trust(#CF-2023-I-2_113)by the Israel Ministry of Innovation,Science,and Technology(#7393)(to ES).
文摘The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.
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.
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financially supported by the National Natural Science Foundation of China(Nos.22475057 and No.52373262).
文摘Directional three-dimensional carbon-based foams are emerging as highly attractive candidates for promising electromagnetic wave absorbing materials(EWAMs)thanks to their unique architecture,but their construction usually involves complex procedures and extremely depends on unidirectional freezing technique.Herein,we propose a groundbreaking approach that leverages the assemblies of salting-out protein induced by ammonium metatungstate(AM)as the precursor,and then acquire directional three-dimensional carbon-based foams through simple pyrolysis.The electrostatic interaction between AM and protein ensures well dispersion of WC_(1−x)nanoparticles on carbon frameworks.The content of WC_(1−x)nanoparticles can be rationally regulated by AM dosage,and it also affects the electromagnetic(EM)properties of final carbon-based foams.The optimized foam exhibits exceptional EM absorption performance,achieving a remarkable minimum reflection loss of−72.0 dB and an effective absorption bandwidth of 6.3 GHz when EM wave propagates parallel to the directional pores.Such performance benefits from the synergistic effects of macroporous architecture and compositional design.Although there is a directional dependence of EM absorption,radar stealth simulation demonstrates that these foams can still promise considerable reduction in radar cross section with the change of incident angle.Moreover,COMSOL simulation further identifies their good performance in preventing EM interference among different electronic components.
基金supported by the National Key R&D Program of China(2024YFD1201100)the research program from the Zhongshan Biological Breeding Laboratory(ZSBBL-KY2023-02)the National Natural Science Foundation of China(32341037).
文摘Wheat(Triticum aestivum)faces significant threats from diseases such as powdery mildew(Blumeria graminis)and Fusarium head blight(FHB;caused by Fusarium graminearum),which cause severe yield losses.Moreover,the antagonism between yield-related traits and disease resistance makes yield resistance coordination a major challenge in wheat breeding.The lack of genetic resources combining both disease resistance and high yield constrains the elucidation of underlying resistance-yield trade-off mechanisms,thereby hindering the development of high-yield and disease-resistant wheat cultivars.Remarkably,Yangmai 33(YM33),a notable wheat cultivar with resistance to both powdery mildew and FHB as well as high-yield performance,was recently developed.It offers a unique opportunity to dissect the genomic architecture underlying the coordination between disease resistance and yield.
基金supported by the National Key Research and Development Project Program of China(2022YFC3400300,2019YFE0109600)the China Postdoctoral Science Foundation(2021M701584).
文摘With the rapid development of sequencing technologies,especially the maturity of third-generation sequencing technologies,there has been a significant increase in the number and quality of published genome assemblies.The emergence of these high-quality genomes has raised higher requirements for genome evaluation.Although numerous computational methods have been developed to evaluate assembly quality from various perspectives,the selective use of these evaluation methods can be arbitrary and inconvenient for fairly comparing the assembly quality.To address this issue,we have developed the Genome Assembly Evaluating Pipeline(GAEP),which provides a comprehensive assessment pipeline for evaluating genome quality from multiple perspectives,including continuity,completeness,and correctness.Additionally,GAEP includes new functions for detecting misassemblies and evaluating the assembly redundancy,which performs well in our testing.GAEP is publicly available at https://github.com/zyoptimistic/GAEP under the GPL3.0 License.With GAEP,users can quickly obtain accurate and reliable evaluation results,facilitating the comparison and selection of high-quality genome assemblies.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB3304200)National Natural Science Foundation of China(Grant No.52205288)+1 种基金China Postdoctoral Science Foundation(Grant Nos.2024T170795,2024M762815)Zhejiang Provincial Key Research and Development Program(Grant No.2024C01029)。
文摘Assembly precision greatly influences the performance of complex high-end equipment.The traditional industrial assembly process and deviation transfer are implicit and uncertain,causing problems like poor component fit and hard-to-trace assembly stress concentration.Assemblers can only check whether the dimensional tolerance of the component design is exceeded step by step in combination with prior knowledge.Inversion in industrial assembly optimizes assembly and design by comparing real and theoretical results and doing inversion analysis to reduce assembly deviation.The digital twin(DT)technology visualizes and predicts the assembly process by mapping real and virtual model parameters and states simultaneously,expanding parameter range for inversion analysis and improving inversion result accuracy.Problems in improving industrial assembly precision and the significance and research status of DT-driven parametric inversion of assembly tools,processes and object precision are summarized.It analyzes vital technologies for assembly precision inversion such as multi-attribute assembly process parameter sensing,virtual modeling of high-fidelity assembly systems,twin synchronization of assembly process data models,multi-physical field simulation,and performance twin model construction of the assembly process.Combined with human-cyber-physical system,augmented reality,and generative intelligence,the outlook of DT-driven assembly precision inversion is proposed,providing support for DT's use in industrial assembly and precision improvement.
基金Supported by National Natural Science Foundation of China(Grant Nos.52475509 and U22A20203)Beijing Municipal Natural Science Foundation(Grant No.L248005)Hebei Provincial Natural Science Foundation(Grant No.E2023105059)。
文摘As the demands for assembly quality and efficiency increase,robot-assisted assembly applications are becoming more widespread.Peg-in-hole assembly,as a typical form of assembly,has been widely researched by scholars.Currently,robotic peg-in-hole assembly faces challenges such as complex analysis of part contact forces,difficulties in task modeling,and the failure of traditional strategies.Simply controlling the position of the robot's end effector cannot achieve high precision,high efficiency peg-in-hole assembly.Flexible assembly,especially intelligent flexible assembly,is becoming the future development trend.So there is a lack of comprehensive reviews on robotic flexible peg-in-hole assembly.This paper first outlines the basic components of peg-in-hole assembly and summarizes the two basic operational processes of peg-in-hole assembly,along with their related theoretical foundations.We then review and analyze the research on passive compliant assembly,active compliant assembly,and intelligent flexible assembly.Finally,it presents an outlook on the future development directions of robotic peg-in-hole assembly.
基金Sponsored by the National Natural Science Foundation of China(Grant No.52005003)the Science and Technology Planning Project of Wuhu City(Grant No.2022jc41)。
文摘To address the challenges of insufficient visualization in the industrial robot assembly operation system and the limitation of visualizing only geometric attributes of physical properties,a method is proposed for constructing an industrial robot assembly system based on virtual reality technology.Focusing on the shaft hole assembly,the mechanical characteristics of the industrial robot shaft hole assembly process are analyzed and a dynamic model is established for shaft hole assembly operations.The key elements of virtual assembly operations for industrial robots are summarized and a five-dimensional model is proposed for industrial robot virtual operations.Utilizing the Unity3D engine based on the 5-D model for industrial robot virtual operations,an industrial robot shaft hole assembly system is developed.This system enables virtual assembly operations,displays physical attributes,and provides valuable references for the research of virtual systems.
基金supported by the National Natural Science Foundation of China(32241045,32241046,32241038)the Major Special Science and Technology Projects in Shanxi Province(202101140601027)+3 种基金Shanxi Provincial Agricultural Key Technologies Breakthrough Project(NYGG01)Doctoral Research Starting Project at Shanxi Agricultural University(2024BQ77)the National Key Research and Development Program of China(2023YFD1202705/2023YFD120270503,2023YFD1202703/2023YFD1202703-4)Shanxi HouJi Laboratory Self-proposed Research Project(202304010930003/202304010930003-03).
文摘Common bean(Phaseolus vulgaris L.)is a vital source of protein and essential nutrients for human consumption and plays a key role in sustainable agriculture due to its nitrogen-fixing ability(Nadeem et al.,2021).Kidney beans,a subcategory of dry common beans,are highly valued for their rich protein,dietary fiber,low fat content,and various trace elements(Garcia-Cordero et al.,2021).Despite the release of several de novo genome assemblies(Goodstein et al.,2012;Schmutz et al.,2014;Vlasova et al.,2016;Cortinovis et al.,2024),existing common bean genomes remain incomplete,particularly in complex regions such as centromeres and telomeres,limiting a comprehensive understanding of the genomic landscape.
文摘Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly(HRCA),challenges remain in the robot’s ability to understand and predict human assembly intentions.This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements.We propose a video feature extraction method based on the Temporal Shift Module Network(TSM-ResNet50)to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames.Furthermore,we construct an action recognition and segmentation model based on the Refined-Multi-Scale Temporal Convolutional Network(Refined-MS-TCN)to identify assembly action intervals and accurately acquire action categories.Experiments on our self-built reducer assembly action dataset demonstrate that our network can classify assembly actions frame by frame,achieving an accuracy rate of 83%.Additionally,we develop a HiddenMarkovModel(HMM)integrated with assembly task constraints to predict operator assembly intentions based on the probability transition matrix and assembly task constraints.The experimental results show that our method for predicting operator assembly intentions can achieve an accuracy of 90.6%,which is a 13.3%improvement over the HMM without task constraints.
基金supported by the Foundation of Jiangsu Province for Talented Personnel and the Self-determined Research Program of Jiangnan University
文摘Challenges still remain in carrying out assembly modeling efficiently in virtual assembly （VA） fields. One of the root causes is the apparent weakness in effective description of assembly knowledge and information. The assembly modeling, disassembly modeling, assembly interference inspection, assembly sequence planning and optimization, and assembly simulation display for key techniques is studied theoretically in this paper. An example of product assembly modeling is provided to illustrate the effectiveness of the proposed approach. On the basis of re- search, using assembly simulation techniques and multimedia techniques to finish structure design in linkage design of a large size wind-drive generator. The application of the modeling method has shortened the lead time dramatically.
基金supported by National Natural Science Foundation of China(Grant No.50805009)The Eleventh Five Year Plan Defense Pre-Research Fund,China(Grant No.51318010205)
文摘Assembly process planning(APP)for complicated products is a time-consuming and difficult work with conventional method.Virtual assembly process planning(VAPP)provides engineers a new and efficiency way.Previous studies in VAPP are almost isolated and dispersive,and have not established a whole understanding and discussed key realization techniques of VAPP from a systemic and integrated view.The integrated virtual assembly process planning(IVAPP)system is a new virtual reality based engineering application,which offers engineers an efficient,intuitive,immersive and integrated method for assembly process planning in a virtual environment.Based on analysis the information integration requirement of VAPP,the architecture of IVAPP is proposed.Through the integrated structure,IVAPP system can realize information integration and workflow controlling.In order to mode/the assembly process in IVAPP,a hierarchical assembly task list(HATL)is presented,in which different assembly tasks for assembling different components are organized into a hierarchical list.A process-oriented automatic geometrical constraint recognition algorithm(AGCR)is proposed,so that geometrical constraints between components can be automatically recognized during the process of interactive assembling.At the same time,a progressive hierarchical reasoning(PHR)model is discussed.AGCR and PHR will greatly reduce the interactive workload.A discrete control node model(DCNM)for cable harness assembly planning in IVAPP is detailed.DCNM converts a cable harness into continuous flexed line segments connected by a series of section center points,and designs can realize cable harness planning through controlling those control nodes.Mechanical assemblies(such as transmission case and engine of automobile)are used to illustrate the feasibility of the proposed method and algorithms.The application of IVAPP system reveals advantages over the traditional assembly process planning method in shortening the time-consumed in assembly planning and in minimizing the handling difficulty,excessive reorientation and dissimilarity of assembly operations.
基金supported by the National Natural Science Foundation of China(Nos.51839011,42203079,and U2240208)the Carbon Peak/Neutralization Technology Innovation Project of Jiangsu Province,China(No.BK20220043)the Excellent Postdoctoral Project of Jiangsu Province,China(No.2022ZB452).
文摘Biological nitrogen fixation(BNF)is a crucial process that provides bioavailable nitrogen and supports primary production in freshwater lake ecosystems.However,the characteristics of diazotrophic community and nitrogenase activity in freshwater lake sediments remain poorly understood.Here,we investigated the diazotrophic communities and nitrogenase activities in the sediments of three large river-connected freshwater lakes in eastern China using 15N-isotope tracing and nifH sequencing.The sediments in these lakes contained diverse nitrogenase genes that were phylogenetically grouped into Clusters I and III.The diazotrophic communities in the sedimentswere dominated by stochastic processes in Hongze Lake and Taihu Lake,which had heterogeneous habitats and shallower water depths,while in Poyang Lake,which had deeper water and a shorter hydraulic retention time,the assembly of the diazotrophic community in the sediments was dominated by homogeneous selection processes.Temperature and water depth were also found the key environmental factors affecting the sediment diazotrophic communities.Sediment nitrogenase activities varied in the three lakes and within distinct regions of an individual lake,ranging from 0 to 14.58 nmol/(kg·hr).Nitrogenase activity was significantly correlated with ferric iron,total phosphorus,and organic matter contents.Our results suggested that freshwater lake sediment contain high diversity of nitrogen-fixing microorganisms with potential metabolic diversity,and the community assembly patterns and nitrogenase activities varied with the lake habitat.
基金supported by the National Key Research and Development Program of China(No.2021YFC2400703)the Key Scientific and Technological Research Projects in Henan Province(Nos.232102311155 and 232102230106)Zhengzhou University Major Project Cultivation Special Project(No.125-32214076).
文摘Drug-eluting magnesium(Mg)alloy stents have a slower degradation rate and lower restenosis rate compared with uncoated stents,demonstrating good clinical efficacy.However,the release of anti-hyperplasia drugs from coatings delays endothelial tissue repair,thus leading to late stent thrombosis.To address these issues,a dual self-healed coating with various biological properties was fabricated on magnesium fluoride/polydopamine(MgF_(2)/PDA)-treated Mg alloys by spraying-assisted layer-by-layer(LBL)self-assembly of chitosan(CS),gallic acid(GA),and 3-aminobenzeneboronic acid-modified hyaluronic acid(HA-ABBA).The LBL coating,approximately 1.50μm thick,exhibited a uniform morphology with good adhesion strength(~1065 mN).The annual corrosion rate(Pi)of LBL samples was~1400 times slower than that of the Mg substrate,due to the physical barrier function provided by MgF_(2)/PDA layers and the dual self-healed ability of LBL layers.The rapid self-healing ability(with a healing period of~4 h under dynamic/static conditions)resulted from the synergistic interplay between the recombination of diverse chemical bonds within the LBL coating and the coordination of LBL-released GA with Mg2+,as corroborated by computer simulations.Compared with the drug-eluting coatings,the LBL sample demonstrated substantial advantages in anti-oxidation,anti-denaturation of fibrinogen,anti-platelet adhesion,anti-inflammation,anti-hyperplasia,and promoted-endothelialization.These benefits effectively address the limitations associated with drug-eluting coatings.
基金the National Key R&D Program of China(No.2021YFA1501503)the National Natural Science Foundation of China(Nos.22250008,22121004,22108197)+3 种基金the Haihe Laboratory of Sustainable Chemical Transformations(No.CYZC202107)the Natural Science Foundation of Tianjin City(No.21JCZXJC00060)the Program of Introducing Talents of Discipline to Universities(No.BP0618007)the Xplorer Prize for financial support。
文摘Membrane electrode assembly(MEA)is widely considered to be the most promising type of electrolyzer for the practical application of electrochemical CO_(2) reduction reaction(CO_(2)RR).In MEAs,a square-shaped cross-section in the flow channel is normally adopted,the configuration optimization of which could potentially enhance the performance of the electrolyzer.This paper describes the numerical simulation study on the impact of the flow-channel cross-section shapes in the MEA electrolyzer for CO_(2)RR.The results show that wide flow channels with low heights are beneficial to the CO_(2)RR by providing a uniform flow field of CO_(2),especially at high current densities.Moreover,the larger the electrolyzer,the more significant the effect is.This study provides a theoretical basis for the design of high-performance MEA electrolyzers for CO_(2)RR.
