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
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
The rationally designed ruthenium selenide(RuSe_(1.6)-500)nanocomposite with selenium vacancies was synthesized via a hydrothermal/annealing approach.During the annealing step,calcination under a H_(2)/Ar atmosphere f...The rationally designed ruthenium selenide(RuSe_(1.6)-500)nanocomposite with selenium vacancies was synthesized via a hydrothermal/annealing approach.During the annealing step,calcination under a H_(2)/Ar atmosphere facilitated the evaporation of selenium,thereby generating selenium vacancies.This study confirmed that RuSe_(1.6)-500 prepared by this method functions as an efficient electrocatalyst for the hydrogen evolution reaction(HER)in seawater.Furthermore,experiments and density functional theory calculations demonstrated that the enhanced electrocatalytic performance and resistance to Cl-induced corrosion in seawater can be attributed to the surface reconstruction of RuSe_(1.6)-500 during the HER process.Specifically,the reconstruction involves the adsorption of hydroxyl groups at selenium vacancies,leading to the formation of a hydroxy-rich surface on RuSe_(1.6)-500.The hydroxy-rich surface is responsible for the superior electrocatalytic activity and stability of RuSe_(1.6)-500 as an electrocatalyst for the HER in seawater.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
A core-shell type Co_(19)-added polyoxometalate H_(17)Na_(4)Cs_(21)[Co_(19)(μ_(3)-OH)_(12)(A-α-SiW_(10)O_(37))_(6)]·8 Cl·12H_(2)O(1)has been made under hydrothermal conditions guided by the lacunary direct...A core-shell type Co_(19)-added polyoxometalate H_(17)Na_(4)Cs_(21)[Co_(19)(μ_(3)-OH)_(12)(A-α-SiW_(10)O_(37))_(6)]·8 Cl·12H_(2)O(1)has been made under hydrothermal conditions guided by the lacunary directing synthetic strategy.Single crystal X-ray diffraction(SXRD)has shown that 19 Co^(2+)are arranged in a flat plane through edge sharing in a mode of 3-4-5-4-3,forming a core-shell type polyanion cluster{Co_(19)(SiW_(10))_6}with a diameter of approximately 2.24 nm.Visible-light-driven photocatalytic hydrogen evolution performance studies have shown that 1 is an efficient heterogeneous water reduction catalyst(WRC)with the H_(2)evolution rate of 2902.5μmol h^(-1)g^(-1).Moreover,the cycle tests indicated that 1 was also a good heterogeneous catalyst.展开更多
Compound-specific carbon isotopic compositions(δ^(13)C)of aromatic hydrocarbons offer a promising solution to the long-standing challenge of correlating ultra-deep oils with their source rocks.However,systematic stud...Compound-specific carbon isotopic compositions(δ^(13)C)of aromatic hydrocarbons offer a promising solution to the long-standing challenge of correlating ultra-deep oils with their source rocks.However,systematic studies on the evolution of these isotopic signatures during thermal maturation remain scarce.In this study,we conducted closed-system anhydrous gold-tube pyrolysis experiments using a representative marine crude oil from the Tarim Basin to systematically investigate the evolution of polycyclic aromatic hydrocarbon(PAH)compositions and their compound-specific δ^(13)C values during thermal maturation.The results show that the abundance and relative distribution of the naphthalene,phenanthrene,fluorene,and dibenzothiophene series vary significantly with increasing maturity.Based on the variation patterns of δ^(13)C values,the aromatic hydrocarbons can be divided into two categories.The first category includes parent PAHs such as naphthalene,phenanthrene,fluorene,and dibenzothiophene,along with some alkylated dibenzothiophenes,whose δ^(13)C values remain essentially invariant during thermal evolution.The second category comprises other alkylated aromatic hydrocarbons,whose δ^(13)C values remain stable at lower temperatures but become progressively enriched in δ^(13)C at higher temperatures due to demethylation.Considering the diverse origins of PAH precursors and the thermal invariance of δ^(13)C in certain aromatic hydrocarbons,compound-specific carbon isotope analysis represents a powerful tool for identifying source rocks in ultra-deep petroleum systems,thereby advancing our understanding of ultra-deep hydrocarbon accumulation.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
NiMo-based catalysts show significant potential for the hydrogen evolution reaction(HER).Optimizing the electronic structure and enhancing mass transfer are two critical factors for improving catalytic performance,but...NiMo-based catalysts show significant potential for the hydrogen evolution reaction(HER).Optimizing the electronic structure and enhancing mass transfer are two critical factors for improving catalytic performance,but they remain significant challenges.Herein,we present a route for synthesizing two-dimensional(2D)porous Mo_(2)N-Ni heterojunction nanosheets with tuned Ni-Mo ratio for enhanced alkaline HER performance.A precursor can be easily synthesized by assembling polyoxometalate clusters(PMo_(12))with layered hydroxy oxides(Ni(OH)_(2)).It is found that the interaction between PMo_(12)and Ni(OH)_(2)can effectively protect the particles from significant agglomeration during pyrolysis,resulting in the formation of 2D porous sheets composed of small Mo_(2)N-Ni units.The transfer of electrons from Ni to Mo_(2)N results in the redistribution of electrons at the heterojunction,optimizing the adsorption and desorption of intermediates.Moreover,the 2D porous structure comprised of small particles enhances mass transfer,thereby reducing the impedance of the catalyst.Consequently,the catalyst with an optimized Mo/Ni ratio exhibits an overpotential of 19 mV at 10 mA cm^(-2),being comparable to that of commercial Pt/C catalyst.The anion exchange membrane(AEM)electrolyzer,consisting of optimized Mo_(2)N-Ni and NiFe-LDH,achieves a current density of 500 mA cm^(-2)at 1.80 V and can operate stably for 300 h.This assembly method offers an effective strategy for the large-scale preparation of efficient catalysts.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
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
To reveal the influence of coupled effects of dry-wet cycling and precompression stress(CEDWCPS)on the damage evolution of limestone with horizontal fissure(LHF),a series of degradation and uniaxial compression tests ...To reveal the influence of coupled effects of dry-wet cycling and precompression stress(CEDWCPS)on the damage evolution of limestone with horizontal fissure(LHF),a series of degradation and uniaxial compression tests were conducted,and a corresponding piecewise damage constitutive model(PDCM)was established.We found that both dry-wet cycling and precompression stress deteriorate the physical properties,alter the microscopic characteristics,and reduce the mechanical properties of the LHF.These degradations are particularly pronounced under the CEDWCPS,although the magnitude of these changes gradually diminishes with the progression of dry-wet cycling.Meanwhile,they also reduce the deformation degree,prolong the micropore compaction stage,shorten the unstable crack propagation stage,lower the frequency and intensity of AE events,decrease the high-amplitude and high-frequency AE signals,enlarge crack scales,and shorten the crack initiation time.Among the changes of these indicators,the dry-wet cycling plays a dominant role.The crack types of LHF under the CEDWCPS(LHFCEDWCPS)are predominantly tensile cracks,supplemented by shear cracks.The failure mode can be defined as tensileshear composite failure.Finally,the established PDCM effectively captures the nonlinear deformation of micropore and the linear deformation of the matrix in LHFCEDWCPS,with all corresponding R^(2) consistently exceeding 0.97.展开更多
Oxygen evolution reaction(OER)is a key step in hydrogen production by water electrolysis technology.How-ever,developing efficient,stable,and low-cost OER electrocatalysts is still challenging.This article presents the...Oxygen evolution reaction(OER)is a key step in hydrogen production by water electrolysis technology.How-ever,developing efficient,stable,and low-cost OER electrocatalysts is still challenging.This article presents the preparation of a series of novel copper iridium nanocatalysts with heterostructures and low iridium content for OER.The electrochemical tests revealed higher OER of Cu@Ir_(0.3) catalyst under acidic conditions with a generated current density of 10 mA/cm^(2) at only 284 mV overpotential.The corresponding OER mass activity was estimated to be 1.057 A/mgIr,a value 8.39-fold higher than that of the commercial IrO_(2).After 50 h of endurance testing,the Cu@Ir_(0.3) catalyst preserved excellent catalytic activity with a negligible rise in overpotential and maintained a good heterostructures.Cu@Ir_(0.3) The excellent OER activity can be attributed to its heterostructure,as con-firmed by density functional theory(DFT)calculations,indicating that Cu@Ir The coupling between isoquanta causes charge redistribution,optimizing the adsorption energy of unsaturated Ir sites for oxygen intermediates and reducing the energy barrier of OER free energy determining the rate step.In summary,this method provides a new approach for designing efficient,stable,and low iridium content OER catalysts.展开更多
Root-inspired anchorage systems in the field of bio-inspired geotechnics are renowned for enhancing the pullout capacity of traditional geotechnical anchorage systems by simulating the morphology and architecture of p...Root-inspired anchorage systems in the field of bio-inspired geotechnics are renowned for enhancing the pullout capacity of traditional geotechnical anchorage systems by simulating the morphology and architecture of plant root systems.However,limited studies have explored their practical applications,particularly in improving slope stability.To fill this gap,this study investigates the reinforcement effect of root-inspired anchors on slope stabilization using transparent soil modeling and 3D-printed anchors,and examines the impact of anchor branching patterns(i.e.branching numbers,branching angle,and branching nodes)on slope bearing capacity,shear band evolution,and temporal and spatial variation of slope deformation.The results show that peak slope bearing capacity increases with branching numbers and branching angles,correlating with the envelope area of the curved shear band.Upper anchors result in step-like deflections in the shear band near the trailing edge,while lower anchors convert the upward concave shear band into an upward convex one,thus increasing the slope bearing capacity.Slope deformation is minimized with intermediate branching parameters,such as a branching number of 4 and a branching angle of 45°.The anchor reinforcement mechanisms,i.e.anchor rod shear resistance,interface friction,anchor pullout capacity,and plate tightening effects,are comprehensively discussed,and the installation effects resulting from compromise slope modeling are identified as the contributors.These findings shed light on the failure process of root-inspired anchors reinforced slopes and provide a preliminary reference for potential applications,especially for the tradeoff between anchor branching,slope deformation,and slope stability.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
During the oxygen evolution reaction(OER),reconstruction of transition metal sulfides(TMSs)is inevitable.However,the lack of a clear theoretical understanding of this process has impeded the development of effective r...During the oxygen evolution reaction(OER),reconstruction of transition metal sulfides(TMSs)is inevitable.However,the lack of a clear theoretical understanding of this process has impeded the development of effective reconstruction regulation strategies.In this study,we first explored the reconstruction mechanism of CoS_(2)during OER from the perspective of electronic structure and identified two possible pathways:the OH-assisted mechanism and the O-assisted mechanism.Further verification showed that these mechanisms are universally applicable to other TMSs(e.g.,FeS_(2)).Based on the reconstruction mechanism,we investigated the basic reasons for the influence of various regulation strategies,such as vacancy modification and facet engineering,on the reconstruction ability.This verified that the method of analyzing the change in the reconstruction ability of catalysts based on the reconstruction mechanism has a high degree of applicability.Importantly,we proposed a core regulation strategy:the coordination symmetry regulation strategy.Specifically,by breaking the symmetry of the surface coordination environment of TMSs(such as introducing heteroatom doping or strain),the reconstruction process will be facilitated.Our findings provide a comprehensive mechanistic explanation for the reconstruction of TMS catalysts and offer a new idea for the rational design of OER catalysts with controllable reconstruction capacity.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 the National Key Research and Development Program of China(2022YFB3805600,2022YFB3805604)the National Natural Science Foundation of China(52201286)+3 种基金the National 111 Project(B20002)the Key R&D Program of Shandong Province,China(2023CXGC010314)the Hubei Provincial Natural Science Foundation of China(2024AFB195)the Fundamental Research Funds for the Central Universities(104972025KFYzxk0014,104972024KFYjlb0008)。
文摘The rationally designed ruthenium selenide(RuSe_(1.6)-500)nanocomposite with selenium vacancies was synthesized via a hydrothermal/annealing approach.During the annealing step,calcination under a H_(2)/Ar atmosphere facilitated the evaporation of selenium,thereby generating selenium vacancies.This study confirmed that RuSe_(1.6)-500 prepared by this method functions as an efficient electrocatalyst for the hydrogen evolution reaction(HER)in seawater.Furthermore,experiments and density functional theory calculations demonstrated that the enhanced electrocatalytic performance and resistance to Cl-induced corrosion in seawater can be attributed to the surface reconstruction of RuSe_(1.6)-500 during the HER process.Specifically,the reconstruction involves the adsorption of hydroxyl groups at selenium vacancies,leading to the formation of a hydroxy-rich surface on RuSe_(1.6)-500.The hydroxy-rich surface is responsible for the superior electrocatalytic activity and stability of RuSe_(1.6)-500 as an electrocatalyst for the HER in seawater.
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supported by the National Natural Science Foundation of China(21831001,21571016,91122028)the National Science Fund for Distinguished Young Scholars(20725101)。
文摘A core-shell type Co_(19)-added polyoxometalate H_(17)Na_(4)Cs_(21)[Co_(19)(μ_(3)-OH)_(12)(A-α-SiW_(10)O_(37))_(6)]·8 Cl·12H_(2)O(1)has been made under hydrothermal conditions guided by the lacunary directing synthetic strategy.Single crystal X-ray diffraction(SXRD)has shown that 19 Co^(2+)are arranged in a flat plane through edge sharing in a mode of 3-4-5-4-3,forming a core-shell type polyanion cluster{Co_(19)(SiW_(10))_6}with a diameter of approximately 2.24 nm.Visible-light-driven photocatalytic hydrogen evolution performance studies have shown that 1 is an efficient heterogeneous water reduction catalyst(WRC)with the H_(2)evolution rate of 2902.5μmol h^(-1)g^(-1).Moreover,the cycle tests indicated that 1 was also a good heterogeneous catalyst.
基金supported by the National Natural Science Foundation of China Enterprise Innovation and Development Joint Fund Project(Grant No.U19B6003).
文摘Compound-specific carbon isotopic compositions(δ^(13)C)of aromatic hydrocarbons offer a promising solution to the long-standing challenge of correlating ultra-deep oils with their source rocks.However,systematic studies on the evolution of these isotopic signatures during thermal maturation remain scarce.In this study,we conducted closed-system anhydrous gold-tube pyrolysis experiments using a representative marine crude oil from the Tarim Basin to systematically investigate the evolution of polycyclic aromatic hydrocarbon(PAH)compositions and their compound-specific δ^(13)C values during thermal maturation.The results show that the abundance and relative distribution of the naphthalene,phenanthrene,fluorene,and dibenzothiophene series vary significantly with increasing maturity.Based on the variation patterns of δ^(13)C values,the aromatic hydrocarbons can be divided into two categories.The first category includes parent PAHs such as naphthalene,phenanthrene,fluorene,and dibenzothiophene,along with some alkylated dibenzothiophenes,whose δ^(13)C values remain essentially invariant during thermal evolution.The second category comprises other alkylated aromatic hydrocarbons,whose δ^(13)C values remain stable at lower temperatures but become progressively enriched in δ^(13)C at higher temperatures due to demethylation.Considering the diverse origins of PAH precursors and the thermal invariance of δ^(13)C in certain aromatic hydrocarbons,compound-specific carbon isotope analysis represents a powerful tool for identifying source rocks in ultra-deep petroleum systems,thereby advancing our understanding of ultra-deep hydrocarbon accumulation.
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supported by the National Key R&D Program of China(2022YFA1503002,2022YFA1503003)the National Natural Science Foundation of China(22271081)+2 种基金the Natural Science Foundation of Heilongjiang Province(PL2024B017)the Postdoctoral Science Foundation of Heilongjiang Province(LBH-Z22240)the Heilongjiang University Excellent Youth Foundation。
文摘NiMo-based catalysts show significant potential for the hydrogen evolution reaction(HER).Optimizing the electronic structure and enhancing mass transfer are two critical factors for improving catalytic performance,but they remain significant challenges.Herein,we present a route for synthesizing two-dimensional(2D)porous Mo_(2)N-Ni heterojunction nanosheets with tuned Ni-Mo ratio for enhanced alkaline HER performance.A precursor can be easily synthesized by assembling polyoxometalate clusters(PMo_(12))with layered hydroxy oxides(Ni(OH)_(2)).It is found that the interaction between PMo_(12)and Ni(OH)_(2)can effectively protect the particles from significant agglomeration during pyrolysis,resulting in the formation of 2D porous sheets composed of small Mo_(2)N-Ni units.The transfer of electrons from Ni to Mo_(2)N results in the redistribution of electrons at the heterojunction,optimizing the adsorption and desorption of intermediates.Moreover,the 2D porous structure comprised of small particles enhances mass transfer,thereby reducing the impedance of the catalyst.Consequently,the catalyst with an optimized Mo/Ni ratio exhibits an overpotential of 19 mV at 10 mA cm^(-2),being comparable to that of commercial Pt/C catalyst.The anion exchange membrane(AEM)electrolyzer,consisting of optimized Mo_(2)N-Ni and NiFe-LDH,achieves a current density of 500 mA cm^(-2)at 1.80 V and can operate stably for 300 h.This assembly method offers an effective strategy for the large-scale preparation of efficient catalysts.
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supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the Yunnan Province Science and Technology Plan Project(No.202403AA080001-4)the Key Research and Development Project of Guangxi,China(No.guikeAB24010144)the National Key Research and Development Project of China(Nos.2021YFB3901402 and 2018YFC1504802)。
文摘To reveal the influence of coupled effects of dry-wet cycling and precompression stress(CEDWCPS)on the damage evolution of limestone with horizontal fissure(LHF),a series of degradation and uniaxial compression tests were conducted,and a corresponding piecewise damage constitutive model(PDCM)was established.We found that both dry-wet cycling and precompression stress deteriorate the physical properties,alter the microscopic characteristics,and reduce the mechanical properties of the LHF.These degradations are particularly pronounced under the CEDWCPS,although the magnitude of these changes gradually diminishes with the progression of dry-wet cycling.Meanwhile,they also reduce the deformation degree,prolong the micropore compaction stage,shorten the unstable crack propagation stage,lower the frequency and intensity of AE events,decrease the high-amplitude and high-frequency AE signals,enlarge crack scales,and shorten the crack initiation time.Among the changes of these indicators,the dry-wet cycling plays a dominant role.The crack types of LHF under the CEDWCPS(LHFCEDWCPS)are predominantly tensile cracks,supplemented by shear cracks.The failure mode can be defined as tensileshear composite failure.Finally,the established PDCM effectively captures the nonlinear deformation of micropore and the linear deformation of the matrix in LHFCEDWCPS,with all corresponding R^(2) consistently exceeding 0.97.
基金supported by the Major Science and Technology Special Plan of Yunnan Province(Nos.202302AB080012 and 202402AB080004)the National Natural Science Foundation of China(No.22264025)+1 种基金the Basic Research Foundation of Yunnan Province(Nos.202401AS070033 and 202501AT070055)the Reserve talents for young and middleaged academic and technical leaders project of Yunnan Province(No.202405AC350071).
文摘Oxygen evolution reaction(OER)is a key step in hydrogen production by water electrolysis technology.How-ever,developing efficient,stable,and low-cost OER electrocatalysts is still challenging.This article presents the preparation of a series of novel copper iridium nanocatalysts with heterostructures and low iridium content for OER.The electrochemical tests revealed higher OER of Cu@Ir_(0.3) catalyst under acidic conditions with a generated current density of 10 mA/cm^(2) at only 284 mV overpotential.The corresponding OER mass activity was estimated to be 1.057 A/mgIr,a value 8.39-fold higher than that of the commercial IrO_(2).After 50 h of endurance testing,the Cu@Ir_(0.3) catalyst preserved excellent catalytic activity with a negligible rise in overpotential and maintained a good heterostructures.Cu@Ir_(0.3) The excellent OER activity can be attributed to its heterostructure,as con-firmed by density functional theory(DFT)calculations,indicating that Cu@Ir The coupling between isoquanta causes charge redistribution,optimizing the adsorption energy of unsaturated Ir sites for oxygen intermediates and reducing the energy barrier of OER free energy determining the rate step.In summary,this method provides a new approach for designing efficient,stable,and low iridium content OER catalysts.
基金supported by the High-end Foreign Expert Introduction Program(Grant No.G2022165004L)the Sichuan Transportation Science and Technology Project(Grant No.2018-ZL-01)China Railway 20th Bureau Science and Technology Project(Grant No.YF1900SD07B).
文摘Root-inspired anchorage systems in the field of bio-inspired geotechnics are renowned for enhancing the pullout capacity of traditional geotechnical anchorage systems by simulating the morphology and architecture of plant root systems.However,limited studies have explored their practical applications,particularly in improving slope stability.To fill this gap,this study investigates the reinforcement effect of root-inspired anchors on slope stabilization using transparent soil modeling and 3D-printed anchors,and examines the impact of anchor branching patterns(i.e.branching numbers,branching angle,and branching nodes)on slope bearing capacity,shear band evolution,and temporal and spatial variation of slope deformation.The results show that peak slope bearing capacity increases with branching numbers and branching angles,correlating with the envelope area of the curved shear band.Upper anchors result in step-like deflections in the shear band near the trailing edge,while lower anchors convert the upward concave shear band into an upward convex one,thus increasing the slope bearing capacity.Slope deformation is minimized with intermediate branching parameters,such as a branching number of 4 and a branching angle of 45°.The anchor reinforcement mechanisms,i.e.anchor rod shear resistance,interface friction,anchor pullout capacity,and plate tightening effects,are comprehensively discussed,and the installation effects resulting from compromise slope modeling are identified as the contributors.These findings shed light on the failure process of root-inspired anchors reinforced slopes and provide a preliminary reference for potential applications,especially for the tradeoff between anchor branching,slope deformation,and slope stability.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金supported by the National Key Research and Development program(2022YFA1504000)the National Natural Science Foundation of China(22302101)+4 种基金the Fundamental Research Funds for the Central Universities(63185015)the Shenzhen Science and Technology Program(JCYJ20210324121002007,JCYJ20230807151503007)the Yunnan Provincial Science and Technology Project at Southwest United Graduate School(202402AO370001)the China Postdoctoral Science Foundation(2022M721699)the Guangdong Basic and Applied Basic Research Foundation(2024A1515010347).
文摘During the oxygen evolution reaction(OER),reconstruction of transition metal sulfides(TMSs)is inevitable.However,the lack of a clear theoretical understanding of this process has impeded the development of effective reconstruction regulation strategies.In this study,we first explored the reconstruction mechanism of CoS_(2)during OER from the perspective of electronic structure and identified two possible pathways:the OH-assisted mechanism and the O-assisted mechanism.Further verification showed that these mechanisms are universally applicable to other TMSs(e.g.,FeS_(2)).Based on the reconstruction mechanism,we investigated the basic reasons for the influence of various regulation strategies,such as vacancy modification and facet engineering,on the reconstruction ability.This verified that the method of analyzing the change in the reconstruction ability of catalysts based on the reconstruction mechanism has a high degree of applicability.Importantly,we proposed a core regulation strategy:the coordination symmetry regulation strategy.Specifically,by breaking the symmetry of the surface coordination environment of TMSs(such as introducing heteroatom doping or strain),the reconstruction process will be facilitated.Our findings provide a comprehensive mechanistic explanation for the reconstruction of TMS catalysts and offer a new idea for the rational design of OER catalysts with controllable reconstruction capacity.
