Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their sim...Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.展开更多
In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding struct...In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.展开更多
Binary composites(ZIF-67/rGO)were synthesized by one-step precipitation method using cobalt nitrate hexahydrate as metal source,2-methylimidazole as organic ligand,and reduced graphene oxide(rGO)as carbon carrier.Then...Binary composites(ZIF-67/rGO)were synthesized by one-step precipitation method using cobalt nitrate hexahydrate as metal source,2-methylimidazole as organic ligand,and reduced graphene oxide(rGO)as carbon carrier.Then Ru3+was introduced for ion exchange,and the porous Ru-doped Co_(3)O_(4)/rGO(Ru-Co_(3)O_(4)/rGO)composite electrocatalyst was prepared by annealing.The phase structure,morphology,and valence state of the catalyst were analyzed by X-ray powder diffraction(XRD),scanning electron microscope(SEM),transmission electron microscopy(TEM),and X-ray photoelectron spectroscopy(XPS).In 1 mol·L^(-1)KOH,the oxygen evolution reaction(OER)performance of the catalyst was measured by linear sweep voltammetry,cyclic voltammetry,and chronoamperometry.The results show that the combination of Ru doping and rGO provides a fast channel for collaborative electron transfer.At the same time,rGO as a carbon carrier can improve the electrical conductivity of Ru-Co_(3)O_(4)particles,and the uniformly dispersed nanoparticles enable the reactants to diffuse freely on the catalyst.The results showed that the electrochemical performance of Ru-Co_(3)O_(4)/rGO was much better than that of Co_(3)O_(4)/rGO,and the overpotential of Ru-Co_(3)O_(4)/rGO was 363.5 mV at the current density of 50 mA·cm^(-2).展开更多
Brazing filler metals are widely applied,which serve as an industrial adhesive in the joining of dissimilar structures.With the continuous emergence of new structures and materials,the demand for novel brazing filler ...Brazing filler metals are widely applied,which serve as an industrial adhesive in the joining of dissimilar structures.With the continuous emergence of new structures and materials,the demand for novel brazing filler metals is ever-increasing.It is of great significance to investigate the optimized composition design methods and to establish systematic design guidelines for brazing filler metals.This study elucidated the fundamental rules for the composition design of brazing filler metals from a three-dimensional perspective encompassing the basic properties of applied brazing filler metals,formability and processability,and overall cost.The basic properties of brazing filler metals refer to their mechanical properties,physicochemical properties,electromagnetic properties,corrosion resistance,and the wettability and fluidity during brazing.The formability and processability of brazing filler metals include the processes of smelting and casting,extrusion,rolling,drawing and ring-making,as well as the processes of granulation,powder production,and the molding of amorphous and microcrystalline structures.The cost of brazing filler metals corresponds to the sum of materials value and manufacturing cost.Improving the comprehensive properties of brazing filler metals requires a comprehensive and systematic consideration of design indicators.Highlighting the unique characteristics of brazing filler metals should focus on relevant technical indicators.Binary or ternary eutectic structures can effectively enhance the flow spreading ability of brazing filler metals,and solid solution structures contribute to the formability.By employing the proposed design guidelines,typical Ag based,Cu based,Zn based brazing filler metals,and Sn based solders were designed and successfully applied in major scientific and engineering projects.展开更多
Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nano...Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nanorods,which had many voids.The S-FeCoTA catalysts exhibited excellent electrochemical oxygen evolution reaction(OER)performance with a low overpotential of 273 mV at 10 mA·cm^(-2)and a small Tafel slope of 36 mV·dec^(-1)in 1 mol·L^(-1)KOH.The potential remained at 1.48 V(vs RHE)at 10 mA·cm^(-2)under continuous testing for 15 h,implying that S-FeCoTA had good stability.The Faraday efficiency of S-FeCoTA was 94%.The outstanding OER activity of S-FeCoTA is attributed to the synergistic effects among S,Fe,and Co,thus promoting electron transfer,reducing the reaction kinetic barrier,and enhancing the OER performance.展开更多
To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating t...To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.展开更多
The characterization techniques were employed like transmission electron microscope,X-ray diffraction and microstructural characterization to investigate microstructural evolution and impact of precipitate-phase preci...The characterization techniques were employed like transmission electron microscope,X-ray diffraction and microstructural characterization to investigate microstructural evolution and impact of precipitate-phase precipitation on strength and toughness of a self-developed 32Si_(2)CrNi_(2)MoVNb steel during the quenching and tempering process.Research outputs indicated that the steel microstructure under the quenching state could be composed of martensite with a high dislocation density,a small amount of residual austenite,and many dispersed spherical MC carbides.In details,after tempering at 200℃,fine needle-shapedε-carbides would precipitate,which may improve yield strength and toughness of the steel.However,as compared to that after tempering at 200℃,the average length of needle-shapedε-carbides was found to increase to 144.1±4 from 134.1±3 nm after tempering at 340℃.As a result,the yield strength may increase to 1505±40 MPa,and the impact absorption energy(V-notch)may also decrease.Moreover,after tempering at 450℃,thoseε-carbides in the steel may transform into coarse rod-shaped cementite,and dislocation recoveries at such high tempering temperature may lead to decrease of strength and toughness of the steel.Finally,the following properties could be obtained:a yield strength of 1440±35 MPa,an ultimate tensile strength of 1864±50 MPa and an impact absorption energy of 45.9±4 J,by means of rational composition design and microstructural control.展开更多
In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and t...In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.展开更多
Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of t...An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.展开更多
In the application of high-pressure water jet assisted breaking of deep underground rock engineering,the influence mechanism of rock temperature on the rock fragmentation process under jet action is still unclear.Ther...In the application of high-pressure water jet assisted breaking of deep underground rock engineering,the influence mechanism of rock temperature on the rock fragmentation process under jet action is still unclear.Therefore,the fluid evolution characteristics and rock fracture behavior during jet impingement were studied.The results indicate that the breaking process of high-temperature rock by jet impact can be divided into four stages:initial fluid-solid contact stage,intense thermal exchange stage,perforation and fracturing stage,and crack propagation and penetration stage.With the increase of rock temperature,the jet reflection angles and the time required for complete cooling of the impact surface significantly decrease,while the number of cracks and crack propagation rate significantly increase,and the rock breaking critical time is shortened by up to 34.5%.Based on numerical simulation results,it was found that the center temperature of granite at 400℃ rapidly decreased from 390 to 260℃ within 0.7 s under jet impact.In addition,a critical temperature and critical heat flux prediction model considering the staged breaking of hot rocks was established.These findings provide valuable insights to guide the water jet technology assisted deep ground hot rock excavation project.展开更多
Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wil...Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.展开更多
Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3...Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3)Fe oxide/PANI)with a robust hetero-interface,which significantly improves oxygen evolution activities with an overpotential of 270 mV at 10 mA cm^(-2)and specific activity of 2.08 mA cm_(ECSA)^(-2)at overpotential of 300 mV,3.84-fold that of Ni_(3)Fe oxide.It is revealed that the catalyst–support interaction between Ni_(3)Fe oxide and PANI support enhances the Ni–O covalency via the interfacial Ni–N bond,thus promoting the charge and mass transfer on Ni_(3)Fe oxide.Considering the excellent activity and stability,rechargeable Zn-air batteries with optimum Ni_(3)Fe oxide/PANI are assembled,delivering a low charge voltage of 1.95 V to cycle for 400 h at 10 mA cm^(-2).The regulation of the effect of catalyst–support interaction on catalytic activity provides new possibilities for the future design of highly efficient OER catalysts.展开更多
This work investigated the anisotropy tensile properties of Inconel 625 alloy fabricated by laser powder bed fusion (LPBF) under various tests temperature, focusing the anisotropy evolution during the high temperature...This work investigated the anisotropy tensile properties of Inconel 625 alloy fabricated by laser powder bed fusion (LPBF) under various tests temperature, focusing the anisotropy evolution during the high temperature. The microstructure contained columnar grains with (111) texture in the vertical plane (90° sample), while a large equiaxed grain with (100) texture was produced in the horizontal plane (0° sample). As for 45° sample, a large number of equiaxed grains and a few columnar grains with (111) texture can be observed. The sample produced at a 0° orientation demonstrates the highest tensile strength, whereas the 90° sample exhibits the greatest elongation. Conversely, the 45° sample displays the least favorable overall performance. As the tests temperature increased from room temperature to 600℃, the anisotropy rate of ultimate tensile strength, yield strength and ductility between 0° and 45° samples, decreased from 8.98 to 6.96%, 2.36 to 1.28%, 19.93 to 12.23%, as well as between 0° and 90° samples decreased from 4.87 to 4.03%, 11.88 to 7.21% and 14.11 to 6.89%, respectively, because of the recovery of oriented columnar grains.展开更多
Solution and aging treatment were conducted on the laser directed energy deposition(LDED)-prepared carbon nanotubes(CNTs)-reinforced WE43(CNTs/WE43)layers to optimize their microstructure and surface properties in thi...Solution and aging treatment were conducted on the laser directed energy deposition(LDED)-prepared carbon nanotubes(CNTs)-reinforced WE43(CNTs/WE43)layers to optimize their microstructure and surface properties in this study.The microstructure of the WE43 and CNTs/WE43 layers was systematically compared.The dissolution of divorced eutectics at the grain boundaries was retarded by CNTs during solution treatment.The spot segregation composed of Mg_(24)Y_(5),CNTs,and Zr cores in the solution treated CNTs/WE43 layer presented a slight decreasing in Y content.The grain growth of both types of layers underwent three stages:slow,rapid,and steady-state.The significant inhibitory effect of CNTs on the grain growth of the LDED WE43 matrix was more pronounced than the promoting effect of temperature,resulting in a 47%increase at 510℃ and a 35%increase at 540℃ in the grain growth exponent compared to the WE43 layers at 510℃.During the subsequent aging treatment at 225℃,the precipitation sequences from plate-shaped β″to plate-shaped and globular β′ were observed in both types of layers.CNTs can facilitate an increase in the nucleation rate of precipitates,but without accelerating precipitation hardening rate.The long and short diameters of the precipitates in peak-aged state were decreased by 48.5%and 43.1%by addition of CNTs,respectively.The wear resistance of both the WE43 and CNTs/WE43 layers can be significantly enhanced through solution and aging treatment.The enhancement in wear resistance for the CNTs/WE43 layers is considerably greater than that of the WE43 layers.展开更多
A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the s...A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multi...The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.展开更多
To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a...To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.展开更多
The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This pape...The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.展开更多
文摘Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.
基金supported by the National Natural Science Foundation of China(Nos.12475174 and 12175101)Yue Lu Shan Center Industrial Innovation(No.2024YCII0108)。
文摘In recent years,the development of new types of nuclear reactors,such as transportable,marine,and space reactors,has presented new challenges for the optimization of reactor radiation-shielding design.Shielding structures typically need to be lightweight,miniaturized,and radiation-protected,which is a multi-parameter and multi-objective optimization problem.The conventional multi-objective(two or three objectives)optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters,as well as a deficiency in achieving a global optimal solution,thereby failing to meet the requirements of shielding optimization for newly developed reactors.In this study,genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective(having four or more objectives)optimal design of reactor radiation shielding.To validate the reliability of the methods,an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem.The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance,and they exhibit their reliability in practical engineering problems.The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes.Therefore,the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.
文摘Binary composites(ZIF-67/rGO)were synthesized by one-step precipitation method using cobalt nitrate hexahydrate as metal source,2-methylimidazole as organic ligand,and reduced graphene oxide(rGO)as carbon carrier.Then Ru3+was introduced for ion exchange,and the porous Ru-doped Co_(3)O_(4)/rGO(Ru-Co_(3)O_(4)/rGO)composite electrocatalyst was prepared by annealing.The phase structure,morphology,and valence state of the catalyst were analyzed by X-ray powder diffraction(XRD),scanning electron microscope(SEM),transmission electron microscopy(TEM),and X-ray photoelectron spectroscopy(XPS).In 1 mol·L^(-1)KOH,the oxygen evolution reaction(OER)performance of the catalyst was measured by linear sweep voltammetry,cyclic voltammetry,and chronoamperometry.The results show that the combination of Ru doping and rGO provides a fast channel for collaborative electron transfer.At the same time,rGO as a carbon carrier can improve the electrical conductivity of Ru-Co_(3)O_(4)particles,and the uniformly dispersed nanoparticles enable the reactants to diffuse freely on the catalyst.The results showed that the electrochemical performance of Ru-Co_(3)O_(4)/rGO was much better than that of Co_(3)O_(4)/rGO,and the overpotential of Ru-Co_(3)O_(4)/rGO was 363.5 mV at the current density of 50 mA·cm^(-2).
基金National Natural Science Foundation of China(U22A20191)。
文摘Brazing filler metals are widely applied,which serve as an industrial adhesive in the joining of dissimilar structures.With the continuous emergence of new structures and materials,the demand for novel brazing filler metals is ever-increasing.It is of great significance to investigate the optimized composition design methods and to establish systematic design guidelines for brazing filler metals.This study elucidated the fundamental rules for the composition design of brazing filler metals from a three-dimensional perspective encompassing the basic properties of applied brazing filler metals,formability and processability,and overall cost.The basic properties of brazing filler metals refer to their mechanical properties,physicochemical properties,electromagnetic properties,corrosion resistance,and the wettability and fluidity during brazing.The formability and processability of brazing filler metals include the processes of smelting and casting,extrusion,rolling,drawing and ring-making,as well as the processes of granulation,powder production,and the molding of amorphous and microcrystalline structures.The cost of brazing filler metals corresponds to the sum of materials value and manufacturing cost.Improving the comprehensive properties of brazing filler metals requires a comprehensive and systematic consideration of design indicators.Highlighting the unique characteristics of brazing filler metals should focus on relevant technical indicators.Binary or ternary eutectic structures can effectively enhance the flow spreading ability of brazing filler metals,and solid solution structures contribute to the formability.By employing the proposed design guidelines,typical Ag based,Cu based,Zn based brazing filler metals,and Sn based solders were designed and successfully applied in major scientific and engineering projects.
文摘Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nanorods,which had many voids.The S-FeCoTA catalysts exhibited excellent electrochemical oxygen evolution reaction(OER)performance with a low overpotential of 273 mV at 10 mA·cm^(-2)and a small Tafel slope of 36 mV·dec^(-1)in 1 mol·L^(-1)KOH.The potential remained at 1.48 V(vs RHE)at 10 mA·cm^(-2)under continuous testing for 15 h,implying that S-FeCoTA had good stability.The Faraday efficiency of S-FeCoTA was 94%.The outstanding OER activity of S-FeCoTA is attributed to the synergistic effects among S,Fe,and Co,thus promoting electron transfer,reducing the reaction kinetic barrier,and enhancing the OER performance.
文摘To ensure a long-term safety and reliability of electric vehicle and energy storage system,an accurate estimation of the state of health(SOH)for lithium-ion battery is important.In this study,a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine(ELM).Input weights and hidden layer biases were generated randomly in traditional ELM.To improve the estimation accuracy of ELM,the differential evolution algorithm was used to optimize these parameters in feasible solution spaces.First,incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests.Then,the ELM based on differential evolution algorithm(DE-ELM model)was used for a lithium-ion battery SOH estimation.At last,four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model.Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.
基金the National Natural Science Foundation of China(Key Program)(52031004).
文摘The characterization techniques were employed like transmission electron microscope,X-ray diffraction and microstructural characterization to investigate microstructural evolution and impact of precipitate-phase precipitation on strength and toughness of a self-developed 32Si_(2)CrNi_(2)MoVNb steel during the quenching and tempering process.Research outputs indicated that the steel microstructure under the quenching state could be composed of martensite with a high dislocation density,a small amount of residual austenite,and many dispersed spherical MC carbides.In details,after tempering at 200℃,fine needle-shapedε-carbides would precipitate,which may improve yield strength and toughness of the steel.However,as compared to that after tempering at 200℃,the average length of needle-shapedε-carbides was found to increase to 144.1±4 from 134.1±3 nm after tempering at 340℃.As a result,the yield strength may increase to 1505±40 MPa,and the impact absorption energy(V-notch)may also decrease.Moreover,after tempering at 450℃,thoseε-carbides in the steel may transform into coarse rod-shaped cementite,and dislocation recoveries at such high tempering temperature may lead to decrease of strength and toughness of the steel.Finally,the following properties could be obtained:a yield strength of 1440±35 MPa,an ultimate tensile strength of 1864±50 MPa and an impact absorption energy of 45.9±4 J,by means of rational composition design and microstructural control.
基金Supported by the Natural Science Foundation of Chongqing(General Program,NO.CSTB2022NSCQ-MSX0884)Discipline Teaching Special Project of Yangtze Normal University(csxkjx14)。
文摘In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
基金supported by the National Natural Science Foundation of China(No.51905123)Major Scientific and Technological Innovation Program of Shandong Province,China(Nos.2020CXGC010303,2022ZLGX04)Key R&D Programme of Shandong Province,China(No.2022JMRH0308).
文摘An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.
基金supported by National Natural Science Foundation of China (No.U23A20597)National Major Science and Technology Project of China (No.2024ZD1003803)+1 种基金Chongqing Science Fund for Distinguished Young Scholars of Chongqing Municipality (No.CSTB2022NSCQ-JQX0028)Natural Science Foundation of Chongqing (No.CSTB2024NSCQ-MSX0503)。
文摘In the application of high-pressure water jet assisted breaking of deep underground rock engineering,the influence mechanism of rock temperature on the rock fragmentation process under jet action is still unclear.Therefore,the fluid evolution characteristics and rock fracture behavior during jet impingement were studied.The results indicate that the breaking process of high-temperature rock by jet impact can be divided into four stages:initial fluid-solid contact stage,intense thermal exchange stage,perforation and fracturing stage,and crack propagation and penetration stage.With the increase of rock temperature,the jet reflection angles and the time required for complete cooling of the impact surface significantly decrease,while the number of cracks and crack propagation rate significantly increase,and the rock breaking critical time is shortened by up to 34.5%.Based on numerical simulation results,it was found that the center temperature of granite at 400℃ rapidly decreased from 390 to 260℃ within 0.7 s under jet impact.In addition,a critical temperature and critical heat flux prediction model considering the staged breaking of hot rocks was established.These findings provide valuable insights to guide the water jet technology assisted deep ground hot rock excavation project.
基金funded by the National Natural Science Foundation of China(grant no.32270238 and 31870311).
文摘Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.
基金Research Institute for Smart Energy(CDB2)the grant from the Research Institute for Advanced Manufacturing(CD8Z)+4 种基金the grant from the Carbon Neutrality Funding Scheme(WZ2R)at The Hong Kong Polytechnic Universitysupport from the Hong Kong Polytechnic University(CD9B,CDBZ and WZ4Q)the National Natural Science Foundation of China(22205187)Shenzhen Municipal Science and Technology Innovation Commission(JCYJ20230807140402006)Start-up Foundation for Introducing Talent of NUIST and Natural Science Foundation of Jiangsu Province of China(BK20230426).
文摘Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3)Fe oxide/PANI)with a robust hetero-interface,which significantly improves oxygen evolution activities with an overpotential of 270 mV at 10 mA cm^(-2)and specific activity of 2.08 mA cm_(ECSA)^(-2)at overpotential of 300 mV,3.84-fold that of Ni_(3)Fe oxide.It is revealed that the catalyst–support interaction between Ni_(3)Fe oxide and PANI support enhances the Ni–O covalency via the interfacial Ni–N bond,thus promoting the charge and mass transfer on Ni_(3)Fe oxide.Considering the excellent activity and stability,rechargeable Zn-air batteries with optimum Ni_(3)Fe oxide/PANI are assembled,delivering a low charge voltage of 1.95 V to cycle for 400 h at 10 mA cm^(-2).The regulation of the effect of catalyst–support interaction on catalytic activity provides new possibilities for the future design of highly efficient OER catalysts.
基金supported by the National Natural Science Foundation of China(Grant Nos.52205140,52175129)the Outstanding Youth Foundation of Hunan Province(Grant No.2023JJ20041)the Science and Technology Innovation Program of Hunan Province(2023RC3241).
文摘This work investigated the anisotropy tensile properties of Inconel 625 alloy fabricated by laser powder bed fusion (LPBF) under various tests temperature, focusing the anisotropy evolution during the high temperature. The microstructure contained columnar grains with (111) texture in the vertical plane (90° sample), while a large equiaxed grain with (100) texture was produced in the horizontal plane (0° sample). As for 45° sample, a large number of equiaxed grains and a few columnar grains with (111) texture can be observed. The sample produced at a 0° orientation demonstrates the highest tensile strength, whereas the 90° sample exhibits the greatest elongation. Conversely, the 45° sample displays the least favorable overall performance. As the tests temperature increased from room temperature to 600℃, the anisotropy rate of ultimate tensile strength, yield strength and ductility between 0° and 45° samples, decreased from 8.98 to 6.96%, 2.36 to 1.28%, 19.93 to 12.23%, as well as between 0° and 90° samples decreased from 4.87 to 4.03%, 11.88 to 7.21% and 14.11 to 6.89%, respectively, because of the recovery of oriented columnar grains.
基金supported by the National Natural Science Foundation of China(52005264).
文摘Solution and aging treatment were conducted on the laser directed energy deposition(LDED)-prepared carbon nanotubes(CNTs)-reinforced WE43(CNTs/WE43)layers to optimize their microstructure and surface properties in this study.The microstructure of the WE43 and CNTs/WE43 layers was systematically compared.The dissolution of divorced eutectics at the grain boundaries was retarded by CNTs during solution treatment.The spot segregation composed of Mg_(24)Y_(5),CNTs,and Zr cores in the solution treated CNTs/WE43 layer presented a slight decreasing in Y content.The grain growth of both types of layers underwent three stages:slow,rapid,and steady-state.The significant inhibitory effect of CNTs on the grain growth of the LDED WE43 matrix was more pronounced than the promoting effect of temperature,resulting in a 47%increase at 510℃ and a 35%increase at 540℃ in the grain growth exponent compared to the WE43 layers at 510℃.During the subsequent aging treatment at 225℃,the precipitation sequences from plate-shaped β″to plate-shaped and globular β′ were observed in both types of layers.CNTs can facilitate an increase in the nucleation rate of precipitates,but without accelerating precipitation hardening rate.The long and short diameters of the precipitates in peak-aged state were decreased by 48.5%and 43.1%by addition of CNTs,respectively.The wear resistance of both the WE43 and CNTs/WE43 layers can be significantly enhanced through solution and aging treatment.The enhancement in wear resistance for the CNTs/WE43 layers is considerably greater than that of the WE43 layers.
基金The National Natural Science Foundation of China(No.U19B2031).
文摘A new method based on the iterative adaptive algorithm(IAA)and blocking matrix preprocessing(BMP)is proposed to study the suppression of multi-mainlobe interference.The algorithm is applied to precisely estimate the spatial spectrum and the directions of arrival(DOA)of interferences to overcome the drawbacks associated with conventional adaptive beamforming(ABF)methods.The mainlobe interferences are identified by calculating the correlation coefficients between direction steering vectors(SVs)and rejected by the BMP pretreatment.Then,IAA is subsequently employed to reconstruct a sidelobe interference-plus-noise covariance matrix for the preferable ABF and residual interference suppression.Simulation results demonstrate the excellence of the proposed method over normal methods based on BMP and eigen-projection matrix perprocessing(EMP)under both uncorrelated and coherent circumstances.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
基金the Shanghai Pujiang Program (No.22PJD030),the National Natural Science Foundation of China (Nos.61603244 and 71904116)the National Natural Science Foundation of China-Shandong Joint Fund (No.U2006228)。
文摘The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.
基金The National Natural Science Foundation of China(No.62272239,62303214)Jiangsu Agricultural Science and Tech-nology Independent Innovation Fund(No.SJ222051).
文摘To tackle the path planning problem,this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution(TPADE).This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy.In the initial phase,it adapts according to environmental complexity.In the following phase,it combines individual and global experiences to fine-tune the orientation factor,effectively improving its global search capability.Furthermore,this study developed a new population update method,ensuring that well-adapted individuals are retained,which enhances population diversity.In benchmark function tests across different dimensions,the proposed algorithm consistently demonstrates superior convergence accuracy and speed.This study also tested the TPADE algorithm in path planning simulations.The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527138 and 31.963990 in simple and complex map environments,respectively.These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.
文摘The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.
