This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in ...This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.展开更多
In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order relia...In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.展开更多
Combining the phase-field method and the moving boundary method,a three-dimensional phase-field simulation was conducted for the growth and grain evolution of Ti films deposited by physical vapor deposition under diff...Combining the phase-field method and the moving boundary method,a three-dimensional phase-field simulation was conducted for the growth and grain evolution of Ti films deposited by physical vapor deposition under different deposition rates and grain orientations.The evolution of grain morphology and grain orientation was also taken into consideration.Simulation results show that at lower deposition rates,the surface of the formed Ti film exhibits a distinct oriented texture structure.The surface roughness of the Ti film is positively correlated with the grain misorientation.Moreover,the surface roughness obtained from the simulation is in good agreement with the experiment results.展开更多
While methodology for determining the mode of evolution in coding sequences has been well established,evaluation of adaptation events in emerging types of phenotype data needs further development.Here,we propose an an...While methodology for determining the mode of evolution in coding sequences has been well established,evaluation of adaptation events in emerging types of phenotype data needs further development.Here,we propose an analysis framework(expression variance decomposition,EVaDe)for comparative single-cell expression data based on phenotypic evolution theory.After decomposing the gene expression variance into separate components,we use two strategies to identify genes exhibiting large between-taxon expression divergence and small within-cell-type expression noise in certain cell types,attributing this pattern to putative adaptive evolution.In a dataset of primate prefrontal cortex,we find that such humanspecific key genes enrich with neurodevelopment-related functions,while most other genes exhibit neutral evolution patterns.Specific neuron types are found to harbor more of these key genes than other cell types,thus likely to have experienced more extensive adaptation.Reassuringly,at the molecular sequence level,the key genes are significantly associated with the rapidly evolving conserved non-coding elements.An additional case analysis comparing the naked mole-rat(NMR)with the mouse suggests that innateimmunity-related genes and cell types have undergone putative expression adaptation in NMR.Overall,the EVaDe framework may effectively probe adaptive evolution mode in single-cell expression data.展开更多
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc...To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.展开更多
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention...The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
Developing catalysts with excellent stability while significantly reducing the overpotential of the oxygen evolution reaction(OER) is crucial for advancing overall water splitting(OWS) systems.In this study,we synthes...Developing catalysts with excellent stability while significantly reducing the overpotential of the oxygen evolution reaction(OER) is crucial for advancing overall water splitting(OWS) systems.In this study,we synthesized the electrode material Ce-NiCo-LDHs@SnO_(2)/NF through a two-step hydrothermal reaction,where Ce-doped NiCo-LDHs are grown on nickel foam modified by a SnO_(2) layer.Ce doping adjusts the internal electronic distribution of Ni Co-LDHs,while the introduction of the SnO_(2) layer enhances electron transfer capability.Together,these factors contribute to the reduction of the OER energy barrier and experimental evidence confirms that the reaction proceeds via the lattice oxygen evolution mechanism(LOM).Consequently,Ce-NiCo-LDHs@SnO_(2)/NF exhibits high level electrochemical performance in OER,requiring only 234 m V overpotential to achieve a current density of 10 m A/cm^(2),with a Tafel slope of just 27.39 m V/dec.When paired with Pt/C/NF,an external potential of only 1.54 V is needed to drive OWS to attain a current density amounting to 10 m A/cm^(2).Furthermore,the catalyst demonstrates stability for 100 h during the OWS stability test.This study underscores the feasibility of enhancing the OER performance through Ce doping and the introduction of a conductive SnO_(2) layer.展开更多
Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activ...Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.展开更多
Current quantitative characterization methods for the mechanical response and damage evolution of coal seams at different burial depths under mining-induced stress remains insufficient.To address this,this study estab...Current quantitative characterization methods for the mechanical response and damage evolution of coal seams at different burial depths under mining-induced stress remains insufficient.To address this,this study establishes a quantitative characterization model for the evolution of mechanical properties in gas-bearing coal masses at varying burial depths.It innovatively introduces a dual damage quantification technique and develops a coupled damage evolution model that comprehensively considers energy evolution,effective mining-induced stress,permeability,and a damage sensitivity coefficient,followed by extensive analysis.Key findings include:coal damage exhibits heterogeneous evolutionary characteristics under mining-induced stress;based on the theory of irreversible deformation,the proposed damage characterization equation can effectively determine the critical damage threshold of coal;the three-parameter EXP function model is more suitable for characterizing the time-dependent damage process of coal under mining-induced stress;a new characterization method for the coal brittleness evaluation index is proposed,revealing an 800 m burial depth boundary for the coal brittleness index;at the microscopic level,achieving quantitative characterization of the correlation between peak stress and the average reduction in functional groups during mining-induced failure of coal at different burial depths.Finally,the mapping relationship between laboratory experimental parameters and field monitoring indicators for early warning of coal mine dynamic disasters is established.展开更多
To realize the practical application of anion exchange membrane water electrolysis(AEMWE),it is essential to develop highly active,durable,and cost-effective electrocatalyst for oxygen evolution reaction(OER).Herein,w...To realize the practical application of anion exchange membrane water electrolysis(AEMWE),it is essential to develop highly active,durable,and cost-effective electrocatalyst for oxygen evolution reaction(OER).Herein,we report a hollow-structured Ni_(x)Co_(1−x)O/Ni_(3)S_(2)/Co_(9)S_(8)heterostructure synthesized via sequential template-assisted growth,thermal oxidation,and controlled sulfidation process.The abundant bimetallic heterointerfaces not only provide additional active sites but also promote electronic modulation via charge redistribution.Additionally,the porous and hollow architecture enhances active surface area and mass transfer ability,thereby increasing the number of accessible active sites for alkaline OER.As a result,the prepared electrocatalyst achieves low overpotential of 310 mV at 10 mA cm^(−2)and small Tafel slope of 55.94 mV dec^(−1),demonstrating the exceptional electrocatalytic performance for alkaline OER.When integrated as the anode in an AEMWE cell,it delivers outstanding performance with only 1.657 V at 1.0 A cm^(−2)and reaches high current density of 5.0 A cm^(−2)at 1.989 V,surpassing those of commercial RuO_(2).The cell also shows excellent long-term durability over 100 h with minimal degradation.This study highlights the strong potential of rationally engineered oxide/sulfide heterostructures for next-generation alkaline water electrolysis.展开更多
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ...ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.展开更多
Understanding the evolution and mechanisms of livestock industry agglomeration provides valuable policy insights for reconciling growing meat demand with constrained resource endowments. This study analyzes the spatia...Understanding the evolution and mechanisms of livestock industry agglomeration provides valuable policy insights for reconciling growing meat demand with constrained resource endowments. This study analyzes the spatial agglomeration of livestock industry at the county level across China from 2000 to 2022 using the localization quotient and Moran's I. An interpretable machine learning approach is employed to test hypotheses concerning the driving mechanisms underlying the spatial distribution of livestock industry. The results show that the agglomeration of China's livestock industry is intensifying, with the agro-pastoral transitional zone(APTZ) emerging as a prominent agglomeration area and distinct agglomeration patterns observed within the zone as well as in its eastern and western regions. Proximity to markets has become an increasingly important determinant of livestock industry agglomeration in China. This market-driven shift has heightened the demand for agricultural feed, prompting the livestock industry to relax its dependence on local natural resource endowments and gradually relocate eastward. Regionally, the agglomeration within the APTZ is shaped by the joint effects of natural and social factors. Natural factors dominate agglomeration dynamics in the western regions of the zone, whereas social factors are more influential in its eastern regions.展开更多
Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity ver...Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity verification for distributed networking of a drone cluster is limited.Therefore,a lightweight blockchainbased identity authentication model for UAV swarms is designed,and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance(CG-PBFT)algorithm is proposed.CG-PBFT introduces a reputation score evaluation mechanism,classifies the reputation levels of nodes in the network,and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology.Experimental results demonstrate that under identical experimental conditions,compared with the PBFT algorithm,CG-PBFT achieves a 250%increase in average throughput,a 70%reduction in average latency,and simultaneous enhancement in security,thus making it more suitable for UAV swarm networks.展开更多
As vital hydraulic infrastructures,concrete dams demand uncompromising safety assurance.Seismic effect commonly serves as a potential factor contributing to the damage of concrete dams,making seismic performance analy...As vital hydraulic infrastructures,concrete dams demand uncompromising safety assurance.Seismic effect commonly serves as a potential factor contributing to the damage of concrete dams,making seismic performance analysis crucial for structural integrity.Numerical simulation based on damage mechanics is usually considered as the approach for investigating the seismic damage behavior of concrete dams.To address the limitations of existing studies and extract the key dynamic characteristics of concrete arch dams,a concrete elastoplastic damage mechanics model is adopted,a seismic load input technique involving the viscoelastic boundary along with equivalent nodal forces is generated,and a spring-contact pair simulation technique formodeling the transverse joints of arch dams is developed.The damage process of an arch dam under the classic Koyna seismic load is simulated,with the damage evolution process under seismic action being characterized.The middle sections of the arch dam near the upper portion are considered regions prone to damage under seismic action.Furthermore,the nonlinear dynamic characteristics caused by the opening and closing collision between transverse joints of the arch damunder strong seismic action are extracted.The extracted dynamic characteristic provides a manifestation for the dynamic damage diagnosis of arch dams based on seismic responses.展开更多
In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention a...In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation.展开更多
To address the issue that static densest subgraph mining algorithms often exhibit low efficiency when handling large scale dynamic graphs,this paper proposes a heuristic approximation algorithm.The algorithm approxima...To address the issue that static densest subgraph mining algorithms often exhibit low efficiency when handling large scale dynamic graphs,this paper proposes a heuristic approximation algorithm.The algorithm approximates the densest k-subgraphs of the entire graph through four steps:partitioning the large-scale dynamic graph,constructing a partial set of the densest k-subgraphs,heuristically merging the subgraph sets,and finally extracting the densest k-subgraphs.This approach significantly reduces the computational time for large-scale dynamic graphs while simultaneously improving the quality of the resulting subgraphs.This algorithm is applicable to various definitions of“density”and can accommodate diverse requirements on the number of edges.When integrated with existing static densest subgraph detection algorithms,it achieves scalability and computational efficiency.Theoretical analysis demonstrates that the optimal density of the densest k-subgraphs extracted by the proposed algorithm reaches 0.9.To evaluate the performance of the algorithm,experiments were conducted on four billion-scale datasets:Friendster,Orkut,YouTube,and DBLP.The results indicate that the proposed algorithm outperforms static methods in both runtime efficiency and subgraph quality on large-scale dynamic graphs.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
We report for the first time the combination of directed evolution focused on enhancing and reversing the stereoselectivity of an enzyme with Cu(I)-mediated click chemistry(CuAAC),providing an asymmetric click approac...We report for the first time the combination of directed evolution focused on enhancing and reversing the stereoselectivity of an enzyme with Cu(I)-mediated click chemistry(CuAAC),providing an asymmetric click approach for versatile chiral triazoles products.In this study,the halohydrin dehalogenase HheG was used as the enzyme which was evolved to induce a stereoselective ring-opening reaction of cyclic epoxides in the presence of NaN3 with the formation of chiral azido products.Two mutants of opposite stereopreference were generated,which convert cyclohexene oxide as well as cycloheptene oxide to(1S,2S)-2-azidocyclohexanol,(1R,2R)-2-azidocyclohexanol,(1S,2S)-2-azidocycloheptanol and(1R,2R)-2-azidocycloheptanol with essentially high stereoselectivity.The chiral products were then subjected to CuAAC in reactions with structurally different alkynes.Since HheG was found to be compatible with Cu(I),the process was also performed successfully in a unique 2-step one-pot process leading to various chiral triazoles.In order to understand the enhancement and reversal of the evolved enantioselectivity,QM and MD computations were performed.This approach harnesses the modifiability and high stereoselectivity of the evolved biocatalysts in combination with click chemistry.It holds great potential for diverse fields,particularly in the area of pharmaceuticals.展开更多
基金supported by the P.G.Senapathy Center for Computing Resources at IIT Madrasfunding provided by the Ministry of Education,Government of Indiasupported by the National Natural Science Foundation of China(Grant Nos.12388101,12472224 and 92252104).
文摘This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.
基金National Natural Science Foundation of China(No.52375236)Fundamental Research Funds for the Central Universities of China(No.23D110316)。
文摘In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.
基金National MCF Energy R&D Program of China(2018YFE0306100)Natural Science Foundation of Hunan Province for Distinguished Young Scholars(2021JJ10062)+1 种基金National Natural Science Foundation of China(52101028)China Postdoctoral Science Foundation(2021M703628)。
文摘Combining the phase-field method and the moving boundary method,a three-dimensional phase-field simulation was conducted for the growth and grain evolution of Ti films deposited by physical vapor deposition under different deposition rates and grain orientations.The evolution of grain morphology and grain orientation was also taken into consideration.Simulation results show that at lower deposition rates,the surface of the formed Ti film exhibits a distinct oriented texture structure.The surface roughness of the Ti film is positively correlated with the grain misorientation.Moreover,the surface roughness obtained from the simulation is in good agreement with the experiment results.
文摘While methodology for determining the mode of evolution in coding sequences has been well established,evaluation of adaptation events in emerging types of phenotype data needs further development.Here,we propose an analysis framework(expression variance decomposition,EVaDe)for comparative single-cell expression data based on phenotypic evolution theory.After decomposing the gene expression variance into separate components,we use two strategies to identify genes exhibiting large between-taxon expression divergence and small within-cell-type expression noise in certain cell types,attributing this pattern to putative adaptive evolution.In a dataset of primate prefrontal cortex,we find that such humanspecific key genes enrich with neurodevelopment-related functions,while most other genes exhibit neutral evolution patterns.Specific neuron types are found to harbor more of these key genes than other cell types,thus likely to have experienced more extensive adaptation.Reassuringly,at the molecular sequence level,the key genes are significantly associated with the rapidly evolving conserved non-coding elements.An additional case analysis comparing the naked mole-rat(NMR)with the mouse suggests that innateimmunity-related genes and cell types have undergone putative expression adaptation in NMR.Overall,the EVaDe framework may effectively probe adaptive evolution mode in single-cell expression data.
文摘To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.
基金supported by the NSFC(Grant Nos.62176273,62271070,62441212)The Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)under Grant SKLNST-2024-1-062025Major Project of the Natural Science Foundation of Inner Mongolia(2025ZD008).
文摘The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金supported by the National Natural Science Foundation of China (No.52274304)。
文摘Developing catalysts with excellent stability while significantly reducing the overpotential of the oxygen evolution reaction(OER) is crucial for advancing overall water splitting(OWS) systems.In this study,we synthesized the electrode material Ce-NiCo-LDHs@SnO_(2)/NF through a two-step hydrothermal reaction,where Ce-doped NiCo-LDHs are grown on nickel foam modified by a SnO_(2) layer.Ce doping adjusts the internal electronic distribution of Ni Co-LDHs,while the introduction of the SnO_(2) layer enhances electron transfer capability.Together,these factors contribute to the reduction of the OER energy barrier and experimental evidence confirms that the reaction proceeds via the lattice oxygen evolution mechanism(LOM).Consequently,Ce-NiCo-LDHs@SnO_(2)/NF exhibits high level electrochemical performance in OER,requiring only 234 m V overpotential to achieve a current density of 10 m A/cm^(2),with a Tafel slope of just 27.39 m V/dec.When paired with Pt/C/NF,an external potential of only 1.54 V is needed to drive OWS to attain a current density amounting to 10 m A/cm^(2).Furthermore,the catalyst demonstrates stability for 100 h during the OWS stability test.This study underscores the feasibility of enhancing the OER performance through Ce doping and the introduction of a conductive SnO_(2) layer.
基金supported by Science and Technology Program from the Forestry Administration of Guangdong Province(2024KJQT0012)the Guangdong Provincial Key R&D Program(2022B1111040001)+2 种基金the National Forestry Administration rare and endangered species field rescue and breeding project(Gui lin hu yu O10)the National Natural Science Foundation of China(32200337)a fellowship from the China Postdoctoral Science Foundation(2022M712003).
文摘Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.
文摘Current quantitative characterization methods for the mechanical response and damage evolution of coal seams at different burial depths under mining-induced stress remains insufficient.To address this,this study establishes a quantitative characterization model for the evolution of mechanical properties in gas-bearing coal masses at varying burial depths.It innovatively introduces a dual damage quantification technique and develops a coupled damage evolution model that comprehensively considers energy evolution,effective mining-induced stress,permeability,and a damage sensitivity coefficient,followed by extensive analysis.Key findings include:coal damage exhibits heterogeneous evolutionary characteristics under mining-induced stress;based on the theory of irreversible deformation,the proposed damage characterization equation can effectively determine the critical damage threshold of coal;the three-parameter EXP function model is more suitable for characterizing the time-dependent damage process of coal under mining-induced stress;a new characterization method for the coal brittleness evaluation index is proposed,revealing an 800 m burial depth boundary for the coal brittleness index;at the microscopic level,achieving quantitative characterization of the correlation between peak stress and the average reduction in functional groups during mining-induced failure of coal at different burial depths.Finally,the mapping relationship between laboratory experimental parameters and field monitoring indicators for early warning of coal mine dynamic disasters is established.
基金supported by the Korea Institute for Advancement of Technology (KIAT)the Ministry of Trade,Industry&Energy (MOTIE) of the Republic of Korea (No. P0022130)by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government (MSIT)(IITP-2025-RS-2023-00259678)
文摘To realize the practical application of anion exchange membrane water electrolysis(AEMWE),it is essential to develop highly active,durable,and cost-effective electrocatalyst for oxygen evolution reaction(OER).Herein,we report a hollow-structured Ni_(x)Co_(1−x)O/Ni_(3)S_(2)/Co_(9)S_(8)heterostructure synthesized via sequential template-assisted growth,thermal oxidation,and controlled sulfidation process.The abundant bimetallic heterointerfaces not only provide additional active sites but also promote electronic modulation via charge redistribution.Additionally,the porous and hollow architecture enhances active surface area and mass transfer ability,thereby increasing the number of accessible active sites for alkaline OER.As a result,the prepared electrocatalyst achieves low overpotential of 310 mV at 10 mA cm^(−2)and small Tafel slope of 55.94 mV dec^(−1),demonstrating the exceptional electrocatalytic performance for alkaline OER.When integrated as the anode in an AEMWE cell,it delivers outstanding performance with only 1.657 V at 1.0 A cm^(−2)and reaches high current density of 5.0 A cm^(−2)at 1.989 V,surpassing those of commercial RuO_(2).The cell also shows excellent long-term durability over 100 h with minimal degradation.This study highlights the strong potential of rationally engineered oxide/sulfide heterostructures for next-generation alkaline water electrolysis.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Provincial Postgraduate Research Innovation Project under grant numberCX20251611Liye Qin Bamboo Slips Research Special Project of JishouUniversity 25LYY03.
文摘ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.
基金National Natural Science Foundation of China,No.42277488Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA26010301。
文摘Understanding the evolution and mechanisms of livestock industry agglomeration provides valuable policy insights for reconciling growing meat demand with constrained resource endowments. This study analyzes the spatial agglomeration of livestock industry at the county level across China from 2000 to 2022 using the localization quotient and Moran's I. An interpretable machine learning approach is employed to test hypotheses concerning the driving mechanisms underlying the spatial distribution of livestock industry. The results show that the agglomeration of China's livestock industry is intensifying, with the agro-pastoral transitional zone(APTZ) emerging as a prominent agglomeration area and distinct agglomeration patterns observed within the zone as well as in its eastern and western regions. Proximity to markets has become an increasingly important determinant of livestock industry agglomeration in China. This market-driven shift has heightened the demand for agricultural feed, prompting the livestock industry to relax its dependence on local natural resource endowments and gradually relocate eastward. Regionally, the agglomeration within the APTZ is shaped by the joint effects of natural and social factors. Natural factors dominate agglomeration dynamics in the western regions of the zone, whereas social factors are more influential in its eastern regions.
基金supported by the following projects:Fund for technical areas of infrastructure strengthening plan projects under Grant 2023-JCJQ-JJ-0772.
文摘Aiming at the challenges of low throughput,excessive consensus latency and high communication complexity in the Practical Byzantine Fault Tolerance(PBFT)algorithm in blockchain networks,its application in identity verification for distributed networking of a drone cluster is limited.Therefore,a lightweight blockchainbased identity authentication model for UAV swarms is designed,and a Credit-score and Grouping-mechanism Practical Byzantine Fault Tolerance(CG-PBFT)algorithm is proposed.CG-PBFT introduces a reputation score evaluation mechanism,classifies the reputation levels of nodes in the network,and optimizes the consensus process based on grouping consensus and BLS aggregate signature technology.Experimental results demonstrate that under identical experimental conditions,compared with the PBFT algorithm,CG-PBFT achieves a 250%increase in average throughput,a 70%reduction in average latency,and simultaneous enhancement in security,thus making it more suitable for UAV swarm networks.
基金supported by the Fundamental Research Funds for the Central Universities(No.B250201286)the Jiangsu-Czech Bilateral Co-Funding R&D Project(No.BZ2023011)+2 种基金the Jiangsu School-Enterprise Cooperation R&D Project(No.24880047-D01-001)the Anhui International Joint Research Center of Data Diagnosis and Smart Maintenance on Bridge Structures(No.2021AHGHZD03)the Key Research Project of Natural Science in Colleges and Universities of Anhui Province(No.2024AH051404).
文摘As vital hydraulic infrastructures,concrete dams demand uncompromising safety assurance.Seismic effect commonly serves as a potential factor contributing to the damage of concrete dams,making seismic performance analysis crucial for structural integrity.Numerical simulation based on damage mechanics is usually considered as the approach for investigating the seismic damage behavior of concrete dams.To address the limitations of existing studies and extract the key dynamic characteristics of concrete arch dams,a concrete elastoplastic damage mechanics model is adopted,a seismic load input technique involving the viscoelastic boundary along with equivalent nodal forces is generated,and a spring-contact pair simulation technique formodeling the transverse joints of arch dams is developed.The damage process of an arch dam under the classic Koyna seismic load is simulated,with the damage evolution process under seismic action being characterized.The middle sections of the arch dam near the upper portion are considered regions prone to damage under seismic action.Furthermore,the nonlinear dynamic characteristics caused by the opening and closing collision between transverse joints of the arch damunder strong seismic action are extracted.The extracted dynamic characteristic provides a manifestation for the dynamic damage diagnosis of arch dams based on seismic responses.
基金Supported by School-level Project of Shaoyang Industry Polytechnic College(SKY24A06)Science and Technology Plan(Special Fund Subsidy)of Shaoyang City(2024PT4070)General Research Project of Hunan Provincial Department of Education in 2025(25C1457).
文摘In the context of rural revitalization and the development of smart agriculture, image classification technology based on deep learning has emerged as a crucial tool for digital monitoring and intelligent prevention and control of agricultural diseases. This paper provides a systematic review of the evolutionary development of algorithms within this field. Addressing challenges such as domain drift and limited global awareness in classical convolutional neural networks (CNNs) applied to complex agricultural environments, the paper focuses on the latest advancements in vision transformers (ViT) and their hybrid architectures to enhance cross-domain robustness and fine-grained recognition capabilities. In response to the challenges posed by scarce long-tail data and limited edge computing power in real-world scenarios, the paper explores solutions related to few-shot learning and ultra-lightweight network deployment. Finally, a forward-looking analysis is presented on the application paradigms of multimodal feature fusion, vision-based large models, and explainable artificial intelligence (AI) within smart plant protection. This analysis aims to offer theoretical insights for the development of efficient and transparent intelligent diagnostic systems for agricultural diseases, thereby supporting the advancement of digital agriculture and the construction of a robust agricultural nation.
基金The National Natural Science Foundation of China(No.62172443).
文摘To address the issue that static densest subgraph mining algorithms often exhibit low efficiency when handling large scale dynamic graphs,this paper proposes a heuristic approximation algorithm.The algorithm approximates the densest k-subgraphs of the entire graph through four steps:partitioning the large-scale dynamic graph,constructing a partial set of the densest k-subgraphs,heuristically merging the subgraph sets,and finally extracting the densest k-subgraphs.This approach significantly reduces the computational time for large-scale dynamic graphs while simultaneously improving the quality of the resulting subgraphs.This algorithm is applicable to various definitions of“density”and can accommodate diverse requirements on the number of edges.When integrated with existing static densest subgraph detection algorithms,it achieves scalability and computational efficiency.Theoretical analysis demonstrates that the optimal density of the densest k-subgraphs extracted by the proposed algorithm reaches 0.9.To evaluate the performance of the algorithm,experiments were conducted on four billion-scale datasets:Friendster,Orkut,YouTube,and DBLP.The results indicate that the proposed algorithm outperforms static methods in both runtime efficiency and subgraph quality on large-scale dynamic graphs.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金support from the National Key Research and Development Program of China(No.2023YFA0914100/2023YFA0914102)the National Natural Science Foundation of China(Nos.22077029 and 22034002)+2 种基金the Science Fund for Distinguished Young Scholars of Hunan Province(No.2021JJ10034)support from the National Natural Science Foundation of China(No.22276049)M.M.acknowledge the support from the National Natural Science Foundation of China(No.22276050).
文摘We report for the first time the combination of directed evolution focused on enhancing and reversing the stereoselectivity of an enzyme with Cu(I)-mediated click chemistry(CuAAC),providing an asymmetric click approach for versatile chiral triazoles products.In this study,the halohydrin dehalogenase HheG was used as the enzyme which was evolved to induce a stereoselective ring-opening reaction of cyclic epoxides in the presence of NaN3 with the formation of chiral azido products.Two mutants of opposite stereopreference were generated,which convert cyclohexene oxide as well as cycloheptene oxide to(1S,2S)-2-azidocyclohexanol,(1R,2R)-2-azidocyclohexanol,(1S,2S)-2-azidocycloheptanol and(1R,2R)-2-azidocycloheptanol with essentially high stereoselectivity.The chiral products were then subjected to CuAAC in reactions with structurally different alkynes.Since HheG was found to be compatible with Cu(I),the process was also performed successfully in a unique 2-step one-pot process leading to various chiral triazoles.In order to understand the enhancement and reversal of the evolved enantioselectivity,QM and MD computations were performed.This approach harnesses the modifiability and high stereoselectivity of the evolved biocatalysts in combination with click chemistry.It holds great potential for diverse fields,particularly in the area of pharmaceuticals.
