Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provid...Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.展开更多
Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen e...Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durabili...Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified...Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentrat...Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.展开更多
Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(...Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.展开更多
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru...Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.展开更多
With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on pre...With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on predetermined refueling duration;however,the precise mission scheduling solution will be difficult to apply due to uncertain refueling duration caused by orbital transfer deviations and stochastic actuator faults during actual on-orbit service.Therefore,this paper proposes a robust mission scheduling strategy for geosynchronous orbit spacecraft on-orbit refueling missions with uncertain refueling duration.Firstly,a robust mission scheduling model is constructed by introducing the budget uncertainty set to describe the uncertain refueling duration.Secondly,a hybrid harris hawks optimization algorithm is designed to explore the optimal mission allocation and refueling sequences,which combines cubic chaotic mapping to initialize the population,and the crossover in the genetic algorithm is introduced to enhance global convergence.Finally,the typical simulation examples are constructed with real-mission scenarios in three aspects to analyze:performance comparisons with various algorithms;robustness analyses via comparisons of different on-orbit refueling durations;investigations into the impacts of different initial population strategies on algorithm performance,demonstrating the proposed mission scheduling framework's robustness and effectiveness by comparing it with the exact mission scheduling.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
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
The liquid-only transfer dividing wall column(LDWC)offers a promising path for industrializing dividing wall columns by simplifying vapor split control.However,their energy efficiency is insufficient due to the additi...The liquid-only transfer dividing wall column(LDWC)offers a promising path for industrializing dividing wall columns by simplifying vapor split control.However,their energy efficiency is insufficient due to the addition of heat at the bottom and its removal at the top.Therefore,developing an effective strategy to enhance the energy efficiency of the entire LDWC system is crucial.This work investigates the intensification of LDWC based on the column grand composite curve(CGCC)and thermodynamic analysis,proposing a novel intensification strategy to improve energy efficiency effectively.An optimization model with four blocks is developed to minimize the total annual cost(TAC)of the intensified LDWC.Energy,exergy,economic,and environmental analyses are used to evaluate its performance.Ternary mixtures with different easy separation indexes(ESI)are selected as illustrative examples.For mixtures with ESI≤1,the optimal configuration involves partial feed preheating,compressors and intermediate reboilers on both side sections,along with optimized operating pressure.This setup leads to significant reductions in total energy consumption,TAC,and gas emissions by 43.80%,28.08%,and 42.85%for ESI=1,and by 46.17%,29.06%,and 45.35%for ESI<1,respectively,when compared to conventional distillation sequences(CDS).For mixtures with ESI>1,the best performance is achieved by implementing partial feed preheating and modifications only to the right section.This results in reductions of 21.64%in energy consumption,16.26%in TAC,and 21.51%in gas emissions when compared to CDS.In all cases,the optimal configurations show the lowest lost work and minimum work,indicating an improved thermodynamic performance.展开更多
Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft ...Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft flight at conventional rotational speeds in the Martian atmosphere.In this paper,we employ the Particle Swarm Optimization(PSO)algorithm to search for the control points of the Bezier curve,completing the parameterization of the airfoil upper and lower curves based on these control points.In order to directly enhance the lift-to-drag ratio of the airfoil at high angles of attack,the NSGA-II algorithm is utilized to optimize the lift-to-drag ratio of NACA 6904 at a=17.5°,Ma=0.43,Re=7600,and CLF 5605 at a=15°,Ma=0.7,Re=7481,respectively.The two-dimensional RANS(Reynolds Average NavierStokes)and k-ωSST turbulence models are employed in the optimization process by CFD to predict the lift and drag characteristics of the airfoil in a Martian environment.Under simulated Mars atmospheric conditions(pressure of 1380 Pa,test temperature of 24°C,equivalent Mars atmospheric density at the surface of 0.0162 g/cm~3),the airfoil after optimized is subjected to rotor lift-drag characteristic tests where a single-rotor lift-drag characteristic test bench is employed for verification.The experimental results demonstrate that the RB-TB-II blade,which is obtained by optimizing the airfoil based on the RB-SWQ-I blade,exhibits a 19.6%increase in Power Loading(PL)and a 20.4%increase in Figure of Merit(FM)compared with the RB-SWQ-I blade.Based on the results of airfoil optimization,increasing the camber at the leading edge of the airfoil under high angles of attack contributes to an improved lift-to-drag ratio.展开更多
Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced...Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.展开更多
The volatilization characteristics and kinetic mechanisms of arsenic were investigated in the temperature range of 623−773 K and pressure ranges of 10−10000 Pa.The experimental results reveal that the evaporation rate...The volatilization characteristics and kinetic mechanisms of arsenic were investigated in the temperature range of 623−773 K and pressure ranges of 10−10000 Pa.The experimental results reveal that the evaporation rate increases with increasing temperature and decreasing pressure.Surface reaction control dominates at low pressures(<100 Pa),whereas diffusion control dominates at high pressures(>5000 Pa).The evaporation behavior is successfully described by an Arrhenius-type model for temperature dependence and Logistic model for pressure dependence.Key kinetic parameters,including the critical pressure,maximum evaporation rate and evaporation coefficient,were calculated.The evaporation coefficient varies between 0.010 and 0.223,and the critical pressures vary between 281 and 478 Pa with temperature.展开更多
At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an ...At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an unfolding synthesis technique was proposed.This algorithm exhibits a narrow pulse shape,and the parallel design of the dual algorithms enables the recovery of pile-up signal amplitudes while preserving the distinct characteristics of neutron and gamma signals.The simplicity of the algorithm facilitates real-time neutron/gamma discrimination on an FPGA,allowing the energy spectra to be updated with each incoming signal.Furthermore,the algorithm can be readily tailored to various experimental conditions by adjusting the decay time constants.Multi-objective optimization reduces the need for manual parameter tuning by rapidly identifying the optimal parameters.Testing with a^(241)Am-Be neutron source and a NaIL scintillator yielded a figure of merit(FoM)value of 2.11 and produced a clear energy spectrum even at high count rates.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
基金Support by National Natural Science Foundation of China(22127802,22573091)the HY Action(62402010305)。
文摘Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.
基金Supported by the National Natural Science Foundation of China(No.52273056)the Science and Technology Development Program of Jilin Province,China(No.YDZJ202501ZYTS305)。
文摘Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金National MCF Energy R&D Program(2024YFE03260300)。
文摘Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
文摘Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3710805).
文摘Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.
基金supported by the National Natural Science Foundation of China(Grant No.12402139,No.52368070)supported by Hainan Provincial Natural Science Foundation of China(Grant No.524QN223)+3 种基金Scientific Research Startup Foundation of Hainan University(Grant No.RZ2300002710)State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ24107)the Horizontal Research Project(Grant No.HD-KYH-2024022)Innovative Research Projects for Postgraduate Students in Hainan Province(Grant No.Hys2025-217).
文摘Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.
文摘Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.
基金co-supported by the National Natural Science Foundation of China(Nos.62473110,62403166)the Fundamental Research Funds for the Central Universities,China(No.2023FRFK02043)+1 种基金the Natural Science Foundation of Heilongjiang Province,China(No.LH2022F023)the National Key Laboratory of Space Intelligent Control Foundation,China(No.2023-JCJQ-LB-006-19)。
文摘With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on predetermined refueling duration;however,the precise mission scheduling solution will be difficult to apply due to uncertain refueling duration caused by orbital transfer deviations and stochastic actuator faults during actual on-orbit service.Therefore,this paper proposes a robust mission scheduling strategy for geosynchronous orbit spacecraft on-orbit refueling missions with uncertain refueling duration.Firstly,a robust mission scheduling model is constructed by introducing the budget uncertainty set to describe the uncertain refueling duration.Secondly,a hybrid harris hawks optimization algorithm is designed to explore the optimal mission allocation and refueling sequences,which combines cubic chaotic mapping to initialize the population,and the crossover in the genetic algorithm is introduced to enhance global convergence.Finally,the typical simulation examples are constructed with real-mission scenarios in three aspects to analyze:performance comparisons with various algorithms;robustness analyses via comparisons of different on-orbit refueling durations;investigations into the impacts of different initial population strategies on algorithm performance,demonstrating the proposed mission scheduling framework's robustness and effectiveness by comparing it with the exact mission scheduling.
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.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
基金support provided by the National Natural Science Foundation of China(U24B6016)the Higher Education Institution Academic Discipline Innovation and Talent Introduction Plan(“111 Plan”)(No.B23025)are gratefully acknowledged.
文摘The liquid-only transfer dividing wall column(LDWC)offers a promising path for industrializing dividing wall columns by simplifying vapor split control.However,their energy efficiency is insufficient due to the addition of heat at the bottom and its removal at the top.Therefore,developing an effective strategy to enhance the energy efficiency of the entire LDWC system is crucial.This work investigates the intensification of LDWC based on the column grand composite curve(CGCC)and thermodynamic analysis,proposing a novel intensification strategy to improve energy efficiency effectively.An optimization model with four blocks is developed to minimize the total annual cost(TAC)of the intensified LDWC.Energy,exergy,economic,and environmental analyses are used to evaluate its performance.Ternary mixtures with different easy separation indexes(ESI)are selected as illustrative examples.For mixtures with ESI≤1,the optimal configuration involves partial feed preheating,compressors and intermediate reboilers on both side sections,along with optimized operating pressure.This setup leads to significant reductions in total energy consumption,TAC,and gas emissions by 43.80%,28.08%,and 42.85%for ESI=1,and by 46.17%,29.06%,and 45.35%for ESI<1,respectively,when compared to conventional distillation sequences(CDS).For mixtures with ESI>1,the best performance is achieved by implementing partial feed preheating and modifications only to the right section.This results in reductions of 21.64%in energy consumption,16.26%in TAC,and 21.51%in gas emissions when compared to CDS.In all cases,the optimal configurations show the lowest lost work and minimum work,indicating an improved thermodynamic performance.
基金supported by the National Key R&D Program of China(No.2024YFC3015804)the Basic Science Center Program for“Space Robot Intelligent Manipulation”,China(No.T2388101)。
文摘Although the thin and cold Martian atmosphere provides the feasibility of rotorcraft flight on Mars,rotors designed for denser Earth atmosphere with small angles of attack hardly generate enough thrust for rotorcraft flight at conventional rotational speeds in the Martian atmosphere.In this paper,we employ the Particle Swarm Optimization(PSO)algorithm to search for the control points of the Bezier curve,completing the parameterization of the airfoil upper and lower curves based on these control points.In order to directly enhance the lift-to-drag ratio of the airfoil at high angles of attack,the NSGA-II algorithm is utilized to optimize the lift-to-drag ratio of NACA 6904 at a=17.5°,Ma=0.43,Re=7600,and CLF 5605 at a=15°,Ma=0.7,Re=7481,respectively.The two-dimensional RANS(Reynolds Average NavierStokes)and k-ωSST turbulence models are employed in the optimization process by CFD to predict the lift and drag characteristics of the airfoil in a Martian environment.Under simulated Mars atmospheric conditions(pressure of 1380 Pa,test temperature of 24°C,equivalent Mars atmospheric density at the surface of 0.0162 g/cm~3),the airfoil after optimized is subjected to rotor lift-drag characteristic tests where a single-rotor lift-drag characteristic test bench is employed for verification.The experimental results demonstrate that the RB-TB-II blade,which is obtained by optimizing the airfoil based on the RB-SWQ-I blade,exhibits a 19.6%increase in Power Loading(PL)and a 20.4%increase in Figure of Merit(FM)compared with the RB-SWQ-I blade.Based on the results of airfoil optimization,increasing the camber at the leading edge of the airfoil under high angles of attack contributes to an improved lift-to-drag ratio.
基金supported by the Hundred-person Program of Chinese Academy of Sciences and the National Natural Science Foundation of China(No.11905074).
文摘Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.
基金Yunnan Fundamental Research Project,China(No.202201BE070001-056)。
文摘The volatilization characteristics and kinetic mechanisms of arsenic were investigated in the temperature range of 623−773 K and pressure ranges of 10−10000 Pa.The experimental results reveal that the evaporation rate increases with increasing temperature and decreasing pressure.Surface reaction control dominates at low pressures(<100 Pa),whereas diffusion control dominates at high pressures(>5000 Pa).The evaporation behavior is successfully described by an Arrhenius-type model for temperature dependence and Logistic model for pressure dependence.Key kinetic parameters,including the critical pressure,maximum evaporation rate and evaporation coefficient,were calculated.The evaporation coefficient varies between 0.010 and 0.223,and the critical pressures vary between 281 and 478 Pa with temperature.
基金supported by the National Natural Science Foundation of China(NSFC)(No.12075308)。
文摘At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an unfolding synthesis technique was proposed.This algorithm exhibits a narrow pulse shape,and the parallel design of the dual algorithms enables the recovery of pile-up signal amplitudes while preserving the distinct characteristics of neutron and gamma signals.The simplicity of the algorithm facilitates real-time neutron/gamma discrimination on an FPGA,allowing the energy spectra to be updated with each incoming signal.Furthermore,the algorithm can be readily tailored to various experimental conditions by adjusting the decay time constants.Multi-objective optimization reduces the need for manual parameter tuning by rapidly identifying the optimal parameters.Testing with a^(241)Am-Be neutron source and a NaIL scintillator yielded a figure of merit(FoM)value of 2.11 and produced a clear energy spectrum even at high count rates.
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.
